{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:43:55Z","timestamp":1782855835611,"version":"3.54.5"},"reference-count":147,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon programme","award":["101047160-BioPIM"],"award-info":[{"award-number":["101047160-BioPIM"]}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"crossref","award":["200021_213084"],"award-info":[{"award-number":["200021_213084"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Archit. Code Optim."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures, where states and edges capture modifications (i.e., insertions, deletions, and substitutions) by assigning probabilities to them. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. Accurate computation of these probabilities is essential for the correct identification of sequence similarities. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. When we analyze state-of-the-art works, we identify an urgent need for a flexible, high-performance, and energy-efficient hardware-software co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs.<\/jats:p><jats:p>We introduce<jats:italic>ApHMM<\/jats:italic>, the<jats:italic>first<\/jats:italic>flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM employs hardware-software co-design to tackle the major inefficiencies in the Baum-Welch algorithm by (1)\u00a0designing flexible hardware to accommodate various pHMM designs, (2)\u00a0exploiting predictable data dependency patterns through on-chip memory with memoization techniques, (3)\u00a0rapidly filtering out unnecessary computations using a hardware-based filter, and (4)\u00a0minimizing redundant computations.<\/jats:p><jats:p>ApHMM achieves substantial speedups of 15.55\u00d7\u2013260.03\u00d7, 1.83\u00d7\u20135.34\u00d7, and 27.97\u00d7 when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: (1)\u00a0error correction, (2)\u00a0protein family search, and (3)\u00a0multiple sequence alignment, by 1.29\u00d7\u201359.94\u00d7, 1.03\u00d7\u20131.75\u00d7, and 1.03\u00d7\u20131.95\u00d7, respectively, while improving their energy efficiency by 64.24\u00d7\u2013115.46\u00d7, 1.75\u00d7, and 1.96\u00d7.<\/jats:p>","DOI":"10.1145\/3632950","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T21:58:00Z","timestamp":1703800680000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-efficient Genome Analysis"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-7863","authenticated-orcid":false,"given":"Can","family":"Firtina","sequence":"first","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2245-6263","authenticated-orcid":false,"given":"Kamlesh","family":"Pillai","sequence":"additional","affiliation":[{"name":"Intel Labs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7018-7585","authenticated-orcid":false,"given":"Gurpreet S.","family":"Kalsi","sequence":"additional","affiliation":[{"name":"Intel Labs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6703-7668","authenticated-orcid":false,"given":"Bharathwaj","family":"Suresh","sequence":"additional","affiliation":[{"name":"Intel Labs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3665-6285","authenticated-orcid":false,"given":"Damla Senol","family":"Cali","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6153-9008","authenticated-orcid":false,"given":"Jeremie S.","family":"Kim","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4576-0030","authenticated-orcid":false,"given":"Taha","family":"Shahroodi","sequence":"additional","affiliation":[{"name":"TU Delft, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4475-6945","authenticated-orcid":false,"given":"Meryem Banu","family":"Cavlak","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2581-8637","authenticated-orcid":false,"given":"Jo\u00ebl","family":"Lindegger","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6117-3701","authenticated-orcid":false,"given":"Mohammed","family":"Alser","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6514-1571","authenticated-orcid":false,"given":"Juan G\u00f3mez","family":"Luna","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5372-0173","authenticated-orcid":false,"given":"Sreenivas","family":"Subramoney","sequence":"additional","affiliation":[{"name":"Intel Labs, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0075-2312","authenticated-orcid":false,"given":"Onur","family":"Mutlu","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"What is a hidden Markov model?","author":"Eddy Sean R.","year":"2004","unstructured":"Sean R. Eddy. 2004. What is a hidden Markov model? Nat. Biotechnol. 22 (Oct. 2004), 1315\u20131316.","journal-title":"Nat. Biotechnol."},{"key":"e_1_3_3_3_2","article-title":"A systematic review of hidden Markov models and their applications","author":"Mor Bhavya","year":"2021","unstructured":"Bhavya Mor, Sunita Garhwal, and Ajay Kumar. 2021. A systematic review of hidden Markov models and their applications. Arch. Comput. Methods Eng. (2021).","journal-title":"Arch. Comput. Methods Eng."},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","DOI":"10.1007\/s00521-017-3028-2","article-title":"A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition","author":"Mustafa Mohammed Kyari","year":"2019","unstructured":"Mohammed Kyari Mustafa, Tony Allen, and Kofi Appiah. 2019. A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural. Comput. Appl. (2019).","journal-title":"Neural. Comput. Appl."},{"key":"e_1_3_3_5_2","volume-title":"Proceedings of the ICASSP","author":"Mao Shuiyang","year":"2019","unstructured":"Shuiyang Mao, Dehua Tao, Guangyan Zhang, P. C. Ching, and Tan Lee. 2019. Revisiting hidden Markov models for speech emotion recognition. In Proceedings of the ICASSP."},{"key":"e_1_3_3_6_2","volume-title":"Proceedings of the WorldS4","author":"Hamidi Mohamed","year":"2018","unstructured":"Mohamed Hamidi, Hassan Satori, Ouissam Zealouk, Khalid Satori, and Naouar Laaidi. 2018. Interactive voice response server voice network administration using hidden Markov model speech recognition system. In Proceedings of the WorldS4."},{"key":"e_1_3_3_7_2","volume-title":"Proceedings of the ICVRIS","author":"Xue Chao","year":"2018","unstructured":"Chao Xue. 2018. A novel english speech recognition approach based on hidden Markov model. In Proceedings of the ICVRIS."},{"key":"e_1_3_3_8_2","volume-title":"Proceedings of the ACII","author":"Li Longfei","year":"2013","unstructured":"Longfei Li, Yong Zhao, Dongmei Jiang, Yanning Zhang, Fengna Wang, Isabel Gonzalez, Enescu Valentin, and Hichem Sahli. 2013. Hybrid deep neural networkhidden Markov model (DNN-HMM)-based speech emotion recognition. In Proceedings of the ACII."},{"key":"e_1_3_3_9_2","volume-title":"Proceedings of the ITC","author":"Patel Ibrahim","year":"2010","unstructured":"Ibrahim Patel and Y. Srinivasa Rao. 2010. Speech recognition using hidden Markov model with MFCC-subband technique. In Proceedings of the ITC."},{"key":"e_1_3_3_10_2","article-title":"Sentiment analysis on Urdu Tweets using Markov chains","author":"Nasim Zarmeen","year":"2020","unstructured":"Zarmeen Nasim and Sayeed Ghani. 2020. Sentiment analysis on Urdu Tweets using Markov chains. SN Comput. Sci. (2020).","journal-title":"SN Comput. Sci."},{"key":"e_1_3_3_11_2","article-title":"Opinion mining using ensemble text hidden Markov models for text classification","author":"Kang Mangi","year":"2018","unstructured":"Mangi Kang, Jaelim Ahn, and Kichun Lee. 2018. Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst. Appl. (2018).","journal-title":"Expert Syst. Appl."},{"key":"e_1_3_3_12_2","article-title":"Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models","author":"Zeinali Hossein","year":"2017","unstructured":"Hossein Zeinali, Hossein Sameti, Lukas Burget, and Jan Honza Cernocky. 2017. Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models. Comput. Speech Lang. (2017).","journal-title":"Comput. Speech Lang."},{"key":"e_1_3_3_13_2","article-title":"Open-vocabulary recognition of machine-printed Arabic text using hidden Markov models","author":"Ahmad Irfan","year":"2016","unstructured":"Irfan Ahmad, Sabri A. Mahmoud, and Gernot A. Fink. 2016. Open-vocabulary recognition of machine-printed Arabic text using hidden Markov models. Pattern Recognit. (2016).","journal-title":"Pattern Recognit."},{"key":"e_1_3_3_14_2","volume-title":"Proceedings of the PACBB","author":"Vieira A. Seara","year":"2014","unstructured":"A. Seara Vieira, E. L. Iglesias, and L. Borrajo. 2014. T-HMM: A novel biomedical text classifier based on hidden Markov models. In Proceedings of the PACBB."},{"key":"e_1_3_3_15_2","article-title":"An acoustic sensing gesture recognition system design based on a hidden Markov model","author":"Moreira Bruna S.","year":"2020","unstructured":"Bruna S. Moreira, Angelo Perkusich, and Saulo O. D. Luiz. 2020. An acoustic sensing gesture recognition system design based on a hidden Markov model. Sensors (2020).","journal-title":"Sensors"},{"key":"e_1_3_3_16_2","volume-title":"Innovations in Soft Computing and Information Technology","author":"Sinha Keshav","year":"2019","unstructured":"Keshav Sinha, Rashmi Kumari, Annu Priya, and Partha Paul. 2019. A computer vision-based gesture recognition using hidden Markov model. In Innovations in Soft Computing and Information Technology. Springer."},{"key":"e_1_3_3_17_2","volume-title":"Proceedings of the ICCE","author":"Haid Markus","year":"2019","unstructured":"Markus Haid, Bernhard Budaker, Markus Geiger, Daniel Husfeldt, Marie Hartmann, and Nick Berezowski. 2019. Inertial-based gesture recognition for artificial intelligent cockpit control using hidden Markov models. In Proceedings of the ICCE."},{"key":"e_1_3_3_18_2","volume-title":"Proceedings of the SYNASC","author":"Calin Alina Delia","year":"2016","unstructured":"Alina Delia Calin. 2016. Gesture recognition on kinect time series data using dynamic time warping and hidden Markov models. In Proceedings of the SYNASC."},{"key":"e_1_3_3_19_2","volume-title":"Proceedings of the ITSC","author":"Deo Nachiket","year":"2016","unstructured":"Nachiket Deo, Akshay Rangesh, and Mohan Trivedi. 2016. In-vehicle hand gesture recognition using hidden Markov models. In Proceedings of the ITSC."},{"key":"e_1_3_3_20_2","volume-title":"Proceedings of the GlobalSIP","author":"Malysa Greg","year":"2016","unstructured":"Greg Malysa, Dan Wang, Lorin Netsch, and Murtaza Ali. 2016. Hidden Markov model-based gesture recognition with FMCW radar. In Proceedings of the GlobalSIP."},{"key":"e_1_3_3_21_2","doi-asserted-by":"crossref","DOI":"10.5772\/50204","article-title":"Two-stage hidden Markov model in gesture recognition for human robot interaction","author":"Nguyen-Duc-Thanh Nhan","year":"2012","unstructured":"Nhan Nguyen-Duc-Thanh, Sungyoung Lee, and Donghan Kim. 2012. Two-stage hidden Markov model in gesture recognition for human robot interaction. Int. J. Adv. Robot. Syst. (2012).","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"e_1_3_3_22_2","volume-title":"Proceedings of the IACC","author":"Shrivastava Rajat","year":"2013","unstructured":"Rajat Shrivastava. 2013. A hidden Markov model based dynamic hand gesture recognition system using OpenCV. In Proceedings of the IACC."},{"key":"e_1_3_3_23_2","volume-title":"Proceedings of the VLSI","author":"Wu Xiao","year":"2020","unstructured":"Xiao Wu, Arun Subramaniyan, Zhehong Wang, Satish Narayanasamy, Reetu Das, and David Blaauw. 2020. 17.3 GCUPS pruning-based pair-hidden-Markov-model accelerator for next-generation DNA sequencing. In Proceedings of the VLSI."},{"key":"e_1_3_3_24_2","volume-title":"Proceedings of the ICMCCE","author":"Lanyue Hu","year":"2020","unstructured":"Hu Lanyue, Chen Jianhua, Wang Rongshu, Lu Zhiwen, and Hou Bin. 2020. A 5 read hybrid error correction algorithm based on segmented pHMM. In Proceedings of the ICMCCE."},{"key":"e_1_3_3_25_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btaa179","article-title":"Apollo: A sequencing-technology-independent, scalable and accurate assembly polishing algorithm","author":"Firtina Can","year":"2020","unstructured":"Can Firtina, Jeremie S. Kim, Mohammed Alser, Damla Senol Cali, A Ercument Cicek, Can Alkan, and Onur Mutlu. 2020. Apollo: A sequencing-technology-independent, scalable and accurate assembly polishing algorithm. Bioinform. (2020).","journal-title":"Bioinform."},{"key":"e_1_3_3_26_2","doi-asserted-by":"crossref","DOI":"10.1186\/s12859-019-3019-7","article-title":"HH-suite3 for fast remote homology detection and deep protein annotation","author":"Steinegger Martin","year":"2019","unstructured":"Martin Steinegger, Markus Meier, Milot Mirdita, Harald V\u00f6hringer, Stephan J. Haunsberger, and Johannes S\u00f6ding. 2019. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinform. (2019).","journal-title":"BMC Bioinform."},{"key":"e_1_3_3_27_2","article-title":"Semi-supervised learning of Hidden Markov Models for biological sequence analysis","author":"Tamposis Ioannis A.","year":"2019","unstructured":"Ioannis A. Tamposis, Konstantinos D. Tsirigos, Margarita C. Theodoropoulou, Panagiota I. Kontou, and Pantelis G. Bagos. 2019. Semi-supervised learning of Hidden Markov Models for biological sequence analysis. Bioinform. (2019).","journal-title":"Bioinform."},{"key":"e_1_3_3_28_2","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky724","article-title":"Hercules: A profile HMM-based hybrid error correction algorithm for long reads","author":"Firtina Can","year":"2018","unstructured":"Can Firtina, Ziv Bar-Joseph, Can Alkan, and A Ercument Cicek. 2018. Hercules: A profile HMM-based hybrid error correction algorithm for long reads. NAR (2018).","journal-title":"NAR"},{"key":"e_1_3_3_29_2","article-title":"ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes","author":"Yin Xiaole","year":"2018","unstructured":"Xiaole Yin, Xiao-Tao Jiang, Benli Chai, Liguan Li, Ying Yang, James R. Cole, James M. Tiedje, and Tong Zhang. 2018. ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinform. (2018).","journal-title":"Bioinform."},{"key":"e_1_3_3_30_2","volume-title":"Proceedings of the FPGA","author":"Huang Sitao","year":"2017","unstructured":"Sitao Huang, Gowthami Jayashri Manikandan, Anand Ramachandran, Kyle Rupnow, Wen-mei W. Hwu, and Deming Chen. 2017. Hardware acceleration of the pair-HMM algorithm for DNA variant calling. In Proceedings of the FPGA."},{"key":"e_1_3_3_31_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btw044","article-title":"BCFtools\/RoH: A hidden Markov model approach for detecting autozygosity from next-generation sequencing data","author":"Narasimhan Vagheesh","year":"2016","unstructured":"Vagheesh Narasimhan, Petr Danecek, Aylwyn Scally, Yali Xue, Chris Tyler-Smith, and Richard Durbin. 2016. BCFtools\/RoH: A hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinform. (2016).","journal-title":"Bioinform."},{"key":"e_1_3_3_32_2","article-title":"FISH: Fast and accurate diploid genotype imputation via segmental hidden Markov model","author":"Zhang Lei","year":"2014","unstructured":"Lei Zhang, Yu-Fang Pei, Xiaoying Fu, Yong Lin, Yu-Ping Wang, and Hong-Wen Deng. 2014. FISH: Fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinform. (2014).","journal-title":"Bioinform."},{"key":"e_1_3_3_33_2","article-title":"Dfam: A database of repetitive DNA based on profile hidden Markov models","author":"Wheeler Travis J.","year":"2012","unstructured":"Travis J. Wheeler, Jody Clements, Sean R. Eddy, Robert Hubley, Thomas A. Jones, Jerzy Jurka, Arian F. A. Smit, and Robert D. Finn. 2012. Dfam: A database of repetitive DNA based on profile hidden Markov models. NAR (2012).","journal-title":"NAR"},{"key":"e_1_3_3_34_2","article-title":"Accelerated profile HMM searches","author":"Eddy Sean R.","year":"2011","unstructured":"Sean R. Eddy. 2011. Accelerated profile HMM searches. PLoS Comput. Biol. (2011).","journal-title":"PLoS Comput. Biol."},{"key":"e_1_3_3_35_2","article-title":"Hidden Markov models and their applications in biological sequence analysis","author":"Yoon Byung-Jun","year":"2009","unstructured":"Byung-Jun Yoon. 2009. Hidden Markov models and their applications in biological sequence analysis. Curr. Genomics (2009).","journal-title":"Curr. Genomics"},{"key":"e_1_3_3_36_2","article-title":"Profile comparer: A program for scoring and aligning profile hidden Markov models","author":"Madera Martin","year":"2008","unstructured":"Martin Madera. 2008. Profile comparer: A program for scoring and aligning profile hidden Markov models. Bioinform. (2008).","journal-title":"Bioinform."},{"key":"e_1_3_3_37_2","article-title":"Bayesian basecalling for DNA sequence analysis using hidden Markov models","author":"Liang Kuo-ching","year":"2007","unstructured":"Kuo-ching Liang, Xiaodong Wang, and Dimitris Anastassiou. 2007. Bayesian basecalling for DNA sequence analysis using hidden Markov models. IEEE TCBB (2007).","journal-title":"IEEE TCBB"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btl486","article-title":"Modelling interaction sites in protein domains with interaction profile hidden Markov models","author":"Friedrich Torben","year":"2006","unstructured":"Torben Friedrich, Birgit Pils, Thomas Dandekar, J\u00f6rg Schultz, and Tobias M\u00fcller. 2006. Modelling interaction sites in protein domains with interaction profile hidden Markov models. Bioinform. (2006).","journal-title":"Bioinform."},{"key":"e_1_3_3_39_2","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-6-104","article-title":"A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models","author":"Sgourakis Nikolaos G.","year":"2005","unstructured":"Nikolaos G. Sgourakis, Pantelis G. Bagos, Panagiotis K. Papasaikas, and Stavros J. Hamodrakas. 2005. A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models. BMC Bioinform. (2005).","journal-title":"BMC Bioinform."},{"key":"e_1_3_3_40_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/bth091","article-title":"COACH: Profile-profile alignment of protein families using hidden Markov models","author":"Edgar Robert C.","year":"2004","unstructured":"Robert C. Edgar and K. Sjolander. 2004. COACH: Profile-profile alignment of protein families using hidden Markov models. Bioinform. (2004).","journal-title":"Bioinform."},{"key":"e_1_3_3_41_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfranklin.2003.12.008","article-title":"Basecalling using hidden Markov models","author":"Boufounos Petros","year":"2004","unstructured":"Petros Boufounos, Sameh El-Difrawy, and Dan Ehrlich. 2004. Basecalling using hidden Markov models. J. Frank. Inst. (2004).","journal-title":"J. Frank. Inst."},{"key":"e_1_3_3_42_2","article-title":"A profile hidden Markov model for signal peptides generated by HMMER","author":"Zhang Zemin","year":"2003","unstructured":"Zemin Zhang and William I. Wood. 2003. A profile hidden Markov model for signal peptides generated by HMMER. Bioinform. (2003).","journal-title":"Bioinform."},{"key":"e_1_3_3_43_2","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/30.1.276","article-title":"The PFAM protein families database","author":"Bateman Alex","year":"2002","unstructured":"Alex Bateman, Ewan Birney, Lorenzo Cerruti, Richard Durbin, Laurence Etwiller, Sean R. Eddy, Sam Griffiths-Jones, Kevin L. Howe, Mhairi Marshall, and Erik L.L. Sonnhammer. 2002. The PFAM protein families database. NAR (2002).","journal-title":"NAR"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790492"},{"key":"e_1_3_3_45_2","article-title":"Profile hidden Markov models","author":"Eddy Sean R.","year":"1998","unstructured":"Sean R. Eddy. 1998. Profile hidden Markov models. Bioinform. (1998).","journal-title":"Bioinform."},{"key":"e_1_3_3_46_2","article-title":"Hidden Markov models of biological primary sequence information.","author":"Baldi Pierre","year":"1994","unstructured":"Pierre Baldi, Yves Chauvin, Tim Hunkapiller, and M. A. McClure. 1994. Hidden Markov models of biological primary sequence information. Proc. Natl. Acad. Sci. U.S.A. (1994).","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"e_1_3_3_47_2","volume-title":"Proceedings of the ICISSP","author":"Ali Muhammad","year":"2022","unstructured":"Muhammad Ali, Monem Hamid, Jacob Jasser, Joachim Lerman, Samod Shetty, and Fabio Di Troia. 2022. Profile hidden Markov model malware detection and API call obfuscation. In Proceedings of the ICISSP."},{"key":"e_1_3_3_48_2","article-title":"ProDroidAn Android malware detection framework based on profile hidden Markov model","author":"Sasidharan Satheesh Kumar","year":"2021","unstructured":"Satheesh Kumar Sasidharan and Ciza Thomas. 2021. ProDroidAn Android malware detection framework based on profile hidden Markov model. PMC (2021).","journal-title":"PMC"},{"key":"e_1_3_3_49_2","article-title":"Adversarial attacks against profile HMM website fingerprinting detection model","author":"Liu Xiaolei","year":"2019","unstructured":"Xiaolei Liu, Zhongliu Zhuo, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, and Mohsen Guizani. 2019. Adversarial attacks against profile HMM website fingerprinting detection model. Cogn. Syst. Res. (2019).","journal-title":"Cogn. Syst. Res."},{"key":"e_1_3_3_50_2","volume-title":"Proceedings of the ICoDSE","author":"Pranamulia Ramandika","year":"2017","unstructured":"Ramandika Pranamulia, Yudistira Asnar, and Riza Satria Perdana. 2017. Profile hidden Markov model for malware classification usage of system call sequence for malware classification. In Proceedings of the ICoDSE."},{"key":"e_1_3_3_51_2","volume-title":"Proceedings of the SECRYPT","author":"Ravi Saradha","year":"2013","unstructured":"Saradha Ravi, N. Balakrishnan, and Bharath Venkatesh. 2013. Behavior-based Malware analysis using profile hidden Markov models. In Proceedings of the SECRYPT."},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","DOI":"10.1007\/s11416-008-0105-1","article-title":"Profile hidden Markov models and metamorphic virus detection","author":"Attaluri Srilatha","year":"2009","unstructured":"Srilatha Attaluri, Scott McGhee, and Mark Stamp. 2009. Profile hidden Markov models and metamorphic virus detection. J. Comput. Virol. (2009).","journal-title":"J. Comput. Virol."},{"key":"e_1_3_3_53_2","volume-title":"Proceedings of the JCDL","author":"Riddell A. B.","year":"2022","unstructured":"A. B. Riddell. 2022. Reliable editions from unreliable components: Estimating ebooks from print editions using profile hidden Markov models. In Proceedings of the JCDL."},{"key":"e_1_3_3_54_2","volume-title":"Proceedings of the ICIP","author":"Kazantzidis Ioannis","year":"2018","unstructured":"Ioannis Kazantzidis, Francisco Florez-Revuelta, and Jean-Christophe Nebel. 2018. Profile hidden Markov models for foreground object modelling. In Proceedings of the ICIP."},{"key":"e_1_3_3_55_2","article-title":"A framework to identify housing location patterns using profile hidden Markov Models","author":"Saadi Isma\u00efl","year":"2016","unstructured":"Isma\u00efl Saadi, Feng Liu, Ahmed Mustafa, Jacques Teller, and Mario Cools. 2016. A framework to identify housing location patterns using profile hidden Markov Models. Adv. Sci. Lett (2016).","journal-title":"Adv. Sci. Lett"},{"key":"e_1_3_3_56_2","volume-title":"Proceedings of the CCCV","author":"Ding Wenwen","year":"2015","unstructured":"Wenwen Ding, Kai Liu, Fei Cheng, Huan Shi, and Baijian Zhang. 2015. Skeleton-based human action recognition with profile hidden Markov models. In Proceedings of the CCCV."},{"key":"e_1_3_3_57_2","article-title":"Characterizing activity sequences using profile Hidden Markov Models","author":"Liu Feng","year":"2015","unstructured":"Feng Liu, Davy Janssens, JianXun Cui, Geert Wets, and Mario Cools. 2015. Characterizing activity sequences using profile Hidden Markov Models. Expert Syst. Appl. (2015).","journal-title":"Expert Syst. Appl."},{"key":"e_1_3_3_58_2","volume-title":"Proceedings of the ICME","author":"Liu Yan","year":"2009","unstructured":"Yan Liu, Pei-Yun Hsueh, Jennifer Lai, Mirweis Sangin, Marc-Antoine Nussli, and Pierre Dillenbourg. 2009. Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications. In Proceedings of the ICME."},{"key":"e_1_3_3_59_2","volume-title":"Proceedings of the DAC","author":"Mutlu Onur","year":"2023","unstructured":"Onur Mutlu and Can Firtina. 2023. Accelerating genome analysis via algorithm-architecture co-design. In Proceedings of the DAC."},{"key":"e_1_3_3_60_2","doi-asserted-by":"crossref","unstructured":"Can Firtina Melina Soysal Jo\u00ebl Lindegger and Onur Mutlu. 2023. RawHash2: Accurate and fast mapping of raw nanopore signals using a hash-based seeding mechanism. arXiv: 2309.05771. Retrieved from https:\/\/arxiv.org\/abs\/2309.05771","DOI":"10.1093\/bioinformatics\/btae478"},{"key":"e_1_3_3_61_2","unstructured":"Jo\u00ebl Lindegger Can Firtina Nika Mansouri Ghiasi Mohammad Sadrosadati Mohammed Alser and Onur Mutlu. 2023. RawAlign: Accurate fast and scalable raw nanopore signal mapping via combining seeding and alignment. arXiv: 2310.05037. Retrieved from https:\/\/arxiv.org\/abs\/2310.05037"},{"key":"e_1_3_3_62_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad272","article-title":"RawHash: Enabling fast and accurate real-time analysis of raw nanopore signals for large genomes","author":"Firtina Can","year":"2023","unstructured":"Can Firtina, Nika Mansouri Ghiasi, Joel Lindegger, Gagandeep Singh, Meryem Banu Cavlak, Haiyu Mao, and Onur Mutlu. 2023. RawHash: Enabling fast and accurate real-time analysis of raw nanopore signals for large genomes. Bioinform. (2023).","journal-title":"Bioinform."},{"key":"e_1_3_3_63_2","volume-title":"Proceedings of the APBC","author":"Kim Jeremie S.","year":"2023","unstructured":"Jeremie S. Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Nastaran Hajinazar, Mohammed Alser, Can Alkan, and Onur Mutlu. 2023. AirLift: A fast and comprehensive technique for remapping alignments between reference genomes. In Proceedings of the APBC."},{"key":"e_1_3_3_64_2","doi-asserted-by":"crossref","DOI":"10.1093\/nargab\/lqad004","article-title":"BLEND: A fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis","author":"Firtina Can","year":"2023","unstructured":"Can Firtina, Jisung Park, Mohammed Alser, Jeremie S. Kim, Damla Senol Cali, Taha Shahroodi, Nika Mansouri Ghiasi, Gagandeep Singh, Konstantinos Kanellopoulos, Can Alkan, and Onur Mutlu. 2023. BLEND: A fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis. NARGAB (2023).","journal-title":"NARGAB"},{"key":"e_1_3_3_65_2","article-title":"FastRemap: A tool for quickly remapping reads between genome assemblies","author":"Kim Jeremie S.","year":"2022","unstructured":"Jeremie S. Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Can Alkan, and Onur Mutlu. 2022. FastRemap: A tool for quickly remapping reads between genome assemblies. Bioinform. (2022).","journal-title":"Bioinform."},{"key":"e_1_3_3_66_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.csbj.2022.08.019","article-title":"From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures","author":"Alser Mohammed","year":"2022","unstructured":"Mohammed Alser, Joel Lindegger, Can Firtina, Nour Almadhoun, Haiyu Mao, Gagandeep Singh, Juan Gomez-Luna, and Onur Mutlu. 2022. From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures. CSBJ (2022).","journal-title":"CSBJ"},{"key":"e_1_3_3_67_2","volume-title":"Proceedings of the ASPLOS","author":"Ghiasi Nika Mansouri","year":"2022","unstructured":"Nika Mansouri Ghiasi, Jisung Park, Harun Mustafa, Jeremie Kim, Ataberk Olgun, Arvid Gollwitzer, Damla Senol Cali, Can Firtina, Haiyu Mao, Nour Almadhoun Alserr, Rachata Ausavarungnirun, Nandita Vijaykumar, Mohammed Alser, and Onur Mutlu. 2022. GenStore: A high-performance in-storage processing system for genome sequence analysis. In Proceedings of the ASPLOS."},{"key":"e_1_3_3_68_2","volume-title":"Proceedings of the ISCA","author":"Cali Damla Senol","year":"2022","unstructured":"Damla Senol Cali, Konstantinos Kanellopoulos, Jo\u00ebl Lindegger, Z\u00fclal Bing\u00f6l, Gurpreet S. Kalsi, Ziyi Zuo, Can Firtina, Meryem Banu Cavlak, Jeremie Kim, Nika Mansouri Ghiasi, Gagandeep Singh, Juan G\u00f3mez-Luna, Nour Almadhoun Alserr, Mohammed Alser, Sreenivas Subramoney, Can Alkan, Saugata Ghose, and Onur Mutlu. 2022. SeGraM: A universal hardware accelerator for genomic sequence-to-graph and sequence-to-sequence mapping. In Proceedings of the ISCA."},{"key":"e_1_3_3_69_2","doi-asserted-by":"crossref","DOI":"10.1186\/s13059-021-02443-7","article-title":"Technology dictates algorithms: Recent developments in read alignment","author":"Alser Mohammed","year":"2021","unstructured":"Mohammed Alser, Jeremy Rotman, Dhrithi Deshpande, Kodi Taraszka, Huwenbo Shi, Pelin Icer Baykal, Harry Taegyun Yang, Victor Xue, Sergey Knyazev, Benjamin D. Singer, Brunilda Balliu, David Koslicki, Pavel Skums, Alex Zelikovsky, Can Alkan, Onur Mutlu, and Serghei Mangul. 2021. Technology dictates algorithms: Recent developments in read alignment. Genome Biol. (2021).","journal-title":"Genome Biol."},{"key":"e_1_3_3_70_2","article-title":"FPGA-based near-memory acceleration of modern data-intensive applications","author":"Singh Gagandeep","year":"2021","unstructured":"Gagandeep Singh, Mohammed Alser, Damla Senol Cali, Diamantopoulos Diamantopoulos, Juan G\u00f3mez-Luna, Henk Corporaal, and Onur Mutlu. 2021. FPGA-based near-memory acceleration of modern data-intensive applications. IEEE Micro (2021).","journal-title":"IEEE Micro"},{"key":"e_1_3_3_71_2","doi-asserted-by":"crossref","DOI":"10.1109\/MM.2020.3013728","article-title":"Accelerating genome analysis: A primer on an ongoing journey","author":"Alser Mohammed","year":"2020","unstructured":"Mohammed Alser, Zulal Bing\u00f6l, Damla Senol Cali, Jeremie Kim, Saugata Ghose, Can Alkan, and Onur Mutlu. 2020. Accelerating genome analysis: A primer on an ongoing journey. IEEE Micro (2020).","journal-title":"IEEE Micro"},{"key":"e_1_3_3_72_2","article-title":"SneakySnake: A fast and accurate universal genome pre-alignment filter for CPUs, GPUs and FPGAs","author":"Alser Mohammed","year":"2020","unstructured":"Mohammed Alser, Taha Shahroodi, Juan G\u00f3mez-Luna, Can Alkan, and Onur Mutlu. 2020. SneakySnake: A fast and accurate universal genome pre-alignment filter for CPUs, GPUs and FPGAs. Bioinform. (2020).","journal-title":"Bioinform."},{"key":"e_1_3_3_73_2","volume-title":"Proceedings of the DATE","author":"Angizi Shaahin","year":"2020","unstructured":"Shaahin Angizi, Jiao Sun, Wei Zhang, and Deliang Fan. 2020. PIM-aligner: A processing-in-MRAM platform for biological sequence alignment. In Proceedings of the DATE."},{"key":"e_1_3_3_74_2","volume-title":"Proceedings of the SC20","author":"Goenka Sneha D.","year":"2020","unstructured":"Sneha D. Goenka, Yatish Turakhia, Benedict Paten, and Mark Horowitz. 2020. SegAlign: A scalable GPU-based whole genome aligner. In Proceedings of the SC20."},{"key":"e_1_3_3_75_2","volume-title":"Proceedings of the MICRO","author":"Cali Damla Senol","year":"2020","unstructured":"Damla Senol Cali, Gurpreet S. Kalsi, Z\u00fclal Bing\u00f6l, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Norion, Allison Scibisz, Sreenivas Subramoneyon, Can Alkan, Saugata Ghose, and Onur Mutlu. 2020. GenASM: A high-performance, low-power approximate string matching acceleration framework for genome sequence analysis. In Proceedings of the MICRO."},{"key":"e_1_3_3_76_2","volume-title":"Proceedings of the MICRO","author":"Nag Anirban","year":"2019","unstructured":"Anirban Nag, C. N. Ramachandra, Rajeev Balasubramonian, Ryan Stutsman, Edouard Giacomin, Hari Kambalasubramanyam, and Pierre-Emmanuel Gaillardon. 2019. GenCache: Leveraging in-cache operators for efficient sequence alignment. In Proceedings of the MICRO."},{"key":"e_1_3_3_77_2","article-title":"Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions","author":"Cali Damla Senol","year":"2019","unstructured":"Damla Senol Cali, Jeremie S. Kim, Saugata Ghose, Can Alkan, and Onur Mutlu. 2019. Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions. Brief. Bioinform. (2019).","journal-title":"Brief. Bioinform."},{"key":"e_1_3_3_78_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btz234","article-title":"Shouji: A fast and efficient pre-alignment filter for sequence alignment","author":"Alser Mohammed","year":"2019","unstructured":"Mohammed Alser, Hasan Hassan, Akash Kumar, Onur Mutlu, and Can Alkan. 2019. Shouji: A fast and efficient pre-alignment filter for sequence alignment. Bioinformatics (2019).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_79_2","volume-title":"Proceedings of the ASPLOS","author":"Turakhia Yatish","year":"2018","unstructured":"Yatish Turakhia, Gill Bejerano, and William J. Dally. 2018. Darwin: A genomics co-processor provides up to 15,000X acceleration on long read assembly. In Proceedings of the ASPLOS."},{"key":"e_1_3_3_80_2","article-title":"GRIM-filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies","author":"Kim Jeremie S.","year":"2018","unstructured":"Jeremie S. Kim, Damla Senol Cali, Hongyi Xin, Donghyuk Lee, Saugata Ghose, Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan, and Onur Mutlu. 2018. GRIM-filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies. BMC Genomics (2018).","journal-title":"BMC Genomics"},{"key":"e_1_3_3_81_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btx342","article-title":"GateKeeper: A new hardware architecture for accelerating pre-alignment in DNA short read mapping","author":"Alser Mohammed","year":"2017","unstructured":"Mohammed Alser, Hasan Hassan, Hongyi Xin, O\u011fuz Ergin, Onur Mutlu, and Can Alkan. 2017. GateKeeper: A new hardware architecture for accelerating pre-alignment in DNA short read mapping. Bioinformatics (2017).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_82_2","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gki408","article-title":"The HHpred interactive server for protein homology detection and structure prediction","author":"S\u00f6ding Johannes","year":"2005","unstructured":"Johannes S\u00f6ding, Andreas Biegert, and Andrei N. Lupas. 2005. The HHpred interactive server for protein homology detection and structure prediction. NAR (2005).","journal-title":"NAR"},{"key":"e_1_3_3_83_2","article-title":"The Pfam protein families database","author":"Finn Robert D.","year":"2010","unstructured":"Robert D. Finn, Jaina Mistry, John Tate, Penny Coggill, Andreas Heger, Joanne E. Pollington, O. Luke Gavin, Prasad Gunasekaran, Goran Ceric, Kristoffer Forslund, Liisa Holm, Erik L. L. Sonnhammer, Sean R. Eddy, and Alex Bateman. 2010. The Pfam protein families database. NAR (2010).","journal-title":"NAR"},{"key":"e_1_3_3_84_2","article-title":"A comparison of profile hidden Markov model procedures for remote homology detection","author":"Madera Martin","year":"2002","unstructured":"Martin Madera and Julian Gough. 2002. A comparison of profile hidden Markov model procedures for remote homology detection. NAR (2002).","journal-title":"NAR"},{"key":"e_1_3_3_85_2","article-title":"Profile HMM based multiple sequence alignment for DNA sequences","author":"Mulia Sudipta","year":"2012","unstructured":"Sudipta Mulia, Debahuti Mishra, and Tanushree Jena. 2012. Profile HMM based multiple sequence alignment for DNA sequences. Procedia Eng. (2012).","journal-title":"Procedia Eng."},{"key":"e_1_3_3_86_2","article-title":"PROMALS: Towards accurate multiple sequence alignments of distantly related proteins","author":"Pei Jimin","year":"2007","unstructured":"Jimin Pei and Nick V. Grishin. 2007. PROMALS: Towards accurate multiple sequence alignments of distantly related proteins. Bioinformatics (2007).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_87_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btg158","article-title":"SATCHMO: Sequence alignment and tree construction using hidden Markov models","author":"Edgar Robert C.","year":"2003","unstructured":"Robert C. Edgar and Kimmen Sj\u00f6lander. 2003. SATCHMO: Sequence alignment and tree construction using hidden Markov models. Bioinformatics (2003).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_88_2","article-title":"Generalized Baum-Welch algorithm based on the similarity between sequences","author":"Rezaei Vahid","year":"2013","unstructured":"Vahid Rezaei, Hamid Pezeshk, and Horacio P\u00e9rez-Sa\u2019nchez. 2013. Generalized Baum-Welch algorithm based on the similarity between sequences. PLoS ONE (2013).","journal-title":"PLoS ONE"},{"key":"e_1_3_3_89_2","article-title":"Bayesian monte carlo estimation for profile hidden Markov models","author":"Lewis Steven J.","year":"2008","unstructured":"Steven J. Lewis, Alpan Raval, and John E. Angus. 2008. Bayesian monte carlo estimation for profile hidden Markov models. Math. Comput. Model. (2008).","journal-title":"Math. Comput. Model."},{"key":"e_1_3_3_90_2","article-title":"An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process","author":"Baum Leonard E.","year":"1972","unstructured":"Leonard E. Baum. 1972. An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities (1972).","journal-title":"Inequalities"},{"key":"e_1_3_3_91_2","article-title":"Bayesian methods for hidden Markov models","author":"Scott Steven L.","year":"2002","unstructured":"Steven L. Scott. 2002. Bayesian methods for hidden Markov models. JASA (2002).","journal-title":"JASA"},{"key":"e_1_3_3_92_2","volume-title":"Proceedings of the I@A","author":"Boussemart Yves","year":"2009","unstructured":"Yves Boussemart, Jonathan Las Fargeas, Mary L. Cummings, and Nicholas Roy. 2009. Comparing learning techniques for hidden Markov models of human supervisory control behavior. In Proceedings of the I@A."},{"key":"e_1_3_3_93_2","article-title":"The consensus string problem and the complexity of comparing hidden Markov models","author":"Lyngs\u00f8 Rune B.","year":"2002","unstructured":"Rune B. Lyngs\u00f8 and Christian N. S. Pedersen. 2002. The consensus string problem and the complexity of comparing hidden Markov models. JCSS (2002).","journal-title":"JCSS"},{"key":"e_1_3_3_94_2","article-title":"Quasi-consensus-based comparison of profile hidden Markov models for protein sequences","author":"Kahsay Robel Y.","year":"2005","unstructured":"Robel Y. Kahsay, Guoli Wang, Guang Gao, Li Liao, and Roland Dunbrack. 2005. Quasi-consensus-based comparison of profile hidden Markov models for protein sequences. Bioinformatics (2005).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_95_2","volume-title":"Proceedings of the BIBM","author":"Ren Shanshan","year":"2015","unstructured":"Shanshan Ren, Vlad-Mihai Sima, and Zaid Al-Ars. 2015. FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis. In Proceedings of the BIBM."},{"key":"e_1_3_3_96_2","article-title":"FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models","author":"Pietras M.","year":"2017","unstructured":"M. Pietras and P. Kl\u0119sk. 2017. FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models. B Pol. Acad. Sci.-Tech. (2017).","journal-title":"B Pol. Acad. Sci.-Tech."},{"key":"e_1_3_3_97_2","volume-title":"Proceedings of the ICPADS","author":"Yu Leiming","year":"2014","unstructured":"Leiming Yu, Yash Ukidave, and David Kaeli. 2014. GPU-accelerated HMM for speech recognition. In Proceedings of the ICPADS."},{"key":"e_1_3_3_98_2","volume-title":"Proceedings of the DAS","author":"Soiman Stefania-Iuliana","year":"2014","unstructured":"Stefania-Iuliana Soiman, Ionela Rusu, and Stefan-Gheorghe Pentiuc. 2014. A parallel accelerated approach of HMM Forward Algorithm for IBM Roadrunner clusters. In Proceedings of the DAS."},{"key":"e_1_3_3_99_2","article-title":"The expectation-maximization algorithm","author":"Moon T. K.","year":"1996","unstructured":"T. K. Moon. 1996. The expectation-maximization algorithm. IEEE Signal Process. Mag. (1996).","journal-title":"IEEE Signal Process. Mag."},{"key":"e_1_3_3_100_2","article-title":"Training a hidden Markov model with a bayesian spiking neural network","author":"Tavanaei Amirhossein","year":"2018","unstructured":"Amirhossein Tavanaei and Anthony S. Maida. 2018. Training a hidden Markov model with a bayesian spiking neural network. J. Signal Process. Syst. (2018).","journal-title":"J. Signal Process. Syst."},{"key":"e_1_3_3_101_2","doi-asserted-by":"crossref","DOI":"10.1007\/s11004-015-9604-z","article-title":"Petro-elastic log-facies classification using the expectationmaximization algorithm and hidden Markov models","author":"Lindberg David Volent","year":"2015","unstructured":"David Volent Lindberg and Dario Grana. 2015. Petro-elastic log-facies classification using the expectationmaximization algorithm and hidden Markov models. Math. Geosci. (2015).","journal-title":"Math. Geosci."},{"key":"e_1_3_3_102_2","volume-title":"Proceedings of the AIIPCC","author":"Hubin Aliaksandr","year":"2019","unstructured":"Aliaksandr Hubin. 2019. An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models. In Proceedings of the AIIPCC."},{"key":"e_1_3_3_103_2","article-title":"Fast and accurate de novo genome assembly from long uncorrected reads","author":"Vaser Robert","year":"2017","unstructured":"Robert Vaser, Ivan Sovi\u0107, Niranjan Nagarajan, and Mile \u0160iki\u0107. 2017. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. (2017).","journal-title":"Genome Res."},{"key":"e_1_3_3_104_2","article-title":"NextPolish: A fast and efficient genome polishing tool for long-read assembly","author":"Hu Jiang","year":"2020","unstructured":"Jiang Hu, Junpeng Fan, Zongyi Sun, and Shanlin Liu. 2020. NextPolish: A fast and efficient genome polishing tool for long-read assembly. Bioinformatics (2020).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_105_2","article-title":"NeuralPolish: A novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks","author":"Huang Neng","year":"2021","unstructured":"Neng Huang, Fan Nie, Peng Ni, Feng Luo, Xin Gao, and Jianxin Wang. 2021. NeuralPolish: A novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks. Bioinformatics (2021).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_106_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0112963","article-title":"Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement","author":"Walker Bruce J.","year":"2014","unstructured":"Bruce J. Walker, Thomas Abeel, Terrance Shea, Margaret Priest, Amr Abouelliel, Sharadha Sakthikumar, Christina A. Cuomo, Qiandong Zeng, Jennifer Wortman, Sarah K. Young, and Ashlee M. Earl. 2014. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE (2014).","journal-title":"PLoS ONE"},{"key":"e_1_3_3_107_2","article-title":"The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies","author":"Zimin Aleksey V.","year":"2020","unstructured":"Aleksey V. Zimin and Steven L. Salzberg. 2020. The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies. PLoS Comput. Biol. (2020).","journal-title":"PLoS Comput. Biol."},{"key":"e_1_3_3_108_2","article-title":"Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data","author":"Chin Chen-Shan","year":"2013","unstructured":"Chen-Shan Chin, David H. Alexander, Patrick Marks, Aaron A. Klammer, James Drake, Cheryl Heiner, Alicia Clum, Alex Copeland, John Huddleston, Evan E. Eichler, Stephen W. Turner, and Jonas Korlach. 2013. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods (2013).","journal-title":"Nat. Methods"},{"key":"e_1_3_3_109_2","doi-asserted-by":"crossref","DOI":"10.1109\/TIT.1967.1054010","article-title":"Error bounds for convolutional codes and an asymptotically optimum decoding algorithm","author":"Viterbi A.","year":"1967","unstructured":"A. Viterbi. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. (1967).","journal-title":"IEEE Trans. Inf."},{"key":"e_1_3_3_110_2","doi-asserted-by":"crossref","DOI":"10.1186\/gb-2001-3-1-reviews2001","article-title":"Tools and resources for identifying protein families, domains and motifs","author":"Mulder Nicola J.","year":"2001","unstructured":"Nicola J. Mulder and Rolf Apweiler. 2001. Tools and resources for identifying protein families, domains and motifs. Genome Biol. (2001).","journal-title":"Genome Biol."},{"key":"e_1_3_3_111_2","doi-asserted-by":"crossref","DOI":"10.12688\/f1000research.17315.1","article-title":"Rapid identification of novel protein families using similarity searches","author":"Jeffryes Matt","year":"2018","unstructured":"Matt Jeffryes and Alex Bateman. 2018. Rapid identification of novel protein families using similarity searches. F1000Research (2018).","journal-title":"F1000Research"},{"key":"e_1_3_3_112_2","article-title":"DeepFam: Deep learning based alignment-free method for protein family modeling and prediction","author":"Seo Seokjun","year":"2018","unstructured":"Seokjun Seo, Minsik Oh, Youngjune Park, and Sun Kim. 2018. DeepFam: Deep learning based alignment-free method for protein family modeling and prediction. Bioinformatics (2018).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_113_2","doi-asserted-by":"crossref","DOI":"10.1093\/molbev\/msac070","article-title":"Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families","author":"Vicedomini R.","year":"2022","unstructured":"R. Vicedomini, J.P. Bouly, E. Laine, A. Falciatore, and A. Carbone. 2022. Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families. Mol. Biol. Evol. (2022).","journal-title":"Mol. Biol. Evol."},{"key":"e_1_3_3_114_2","doi-asserted-by":"crossref","DOI":"10.1021\/acs.jpcb.8b07206","article-title":"On the natural structure of amino acid patterns in families of protein sequences","author":"Turjanski Pablo","year":"2018","unstructured":"Pablo Turjanski and Diego U. Ferreiro. 2018. On the natural structure of amino acid patterns in families of protein sequences. J. Phys. Chem. B. (2018).","journal-title":"J. Phys. Chem. B."},{"key":"e_1_3_3_115_2","article-title":"Using deep learning to annotate the protein universe","author":"Bileschi Maxwell L.","year":"2022","unstructured":"Maxwell L. Bileschi, David Belanger, Drew H. Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Alex Bateman, Mark A. DePristo, and Lucy J. Colwell. 2022. Using deep learning to annotate the protein universe. Nat. Biotechnol. (2022).","journal-title":"Nat. Biotechnol."},{"key":"e_1_3_3_116_2","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkaa913","article-title":"Pfam: The protein families database in 2021","author":"Mistry Jaina","year":"2021","unstructured":"Jaina Mistry, Sara Chuguransky, Lowri Williams, Matloob Qureshi, Gustavo A. Salazar, Erik L. L. Sonnhammer, Silvio C. E. Tosatto, Lisanna Paladin, Shriya Raj, Lorna J. Richardson, Robert D. Finn, and Alex Bateman. 2021. Pfam: The protein families database in 2021. NAR (2021).","journal-title":"NAR"},{"key":"e_1_3_3_117_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0105067","article-title":"Profile hidden Markov models for the detection of viruses within metagenomic Sequence Data","author":"Skewes-Cox Peter","year":"2014","unstructured":"Peter Skewes-Cox, Thomas J. Sharpton, Katherine S. Pollard, and Joseph L. DeRisi. 2014. Profile hidden Markov models for the detection of viruses within metagenomic Sequence Data. PLoS ONE (2014).","journal-title":"PLoS ONE"},{"key":"e_1_3_3_118_2","article-title":"Computational complexity of multiple sequence alignment with SP-score","author":"Just Winfried","year":"2001","unstructured":"Winfried Just. 2001. Computational complexity of multiple sequence alignment with SP-score. J. Comput. Biol. (2001).","journal-title":"J. Comput. Biol."},{"key":"e_1_3_3_119_2","article-title":"On the complexity of multiple sequence alignment","author":"Wang Lusheng","year":"1994","unstructured":"Lusheng Wang and Tao Jiang. 1994. On the complexity of multiple sequence alignment. J. Comput. Biol. (1994).","journal-title":"J. Comput. Biol."},{"key":"e_1_3_3_120_2","article-title":"A review on multiple sequence alignment from the perspective of genetic algorithm","author":"Chowdhury Biswanath","year":"2017","unstructured":"Biswanath Chowdhury and Gautam Garai. 2017. A review on multiple sequence alignment from the perspective of genetic algorithm. Genomics (2017).","journal-title":"Genomics"},{"key":"e_1_3_3_121_2","article-title":"ProbPFP: A multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function","author":"Zhan Qing","year":"2019","unstructured":"Qing Zhan, Nan Wang, Shuilin Jin, Renjie Tan, Qinghua Jiang, and Yadong Wang. 2019. ProbPFP: A multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function. BMC Bioinform. (2019).","journal-title":"BMC Bioinform."},{"key":"e_1_3_3_122_2","unstructured":"Intel. 2022. Vtune Profiler. Retrieved from https:\/\/www.intel.com\/content\/www\/us\/en\/developer\/tools\/oneapi\/vtune-profiler.html"},{"key":"e_1_3_3_123_2","article-title":"Gprof: A call graph execution profiler","author":"Graham Susan L.","year":"2004","unstructured":"Susan L. Graham, Peter B. Kessler, and Marshall K. McKusick. 2004. Gprof: A call graph execution profiler. SIGPLAN Not. (2004).","journal-title":"SIGPLAN Not."},{"key":"e_1_3_3_124_2","unstructured":"Bonnie Kirkpatrick and Kay Kirkpatrick. 2012. Optimal state-space reduction for pedigree hidden Markov models. arXiv: 1202.2468. Retrieved from https:\/\/arxiv.org\/abs\/1202.2468"},{"key":"e_1_3_3_125_2","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-6-231","article-title":"A linear memory algorithm for Baum-Welch training","author":"Mikl\u00f3s Istv\u00e1n","year":"2005","unstructured":"Istv\u00e1n Mikl\u00f3s and Irmtraud M. Meyer. 2005. A linear memory algorithm for Baum-Welch training. BMC Bioinform. (2005).","journal-title":"BMC Bioinform."},{"key":"e_1_3_3_126_2","article-title":"Reduced space sequence alignment","author":"Grice J.Alicia","year":"1997","unstructured":"J.Alicia Grice, Richard Hughey, and Don Speck. 1997. Reduced space sequence alignment. Bioinformatics (1997).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_127_2","article-title":"Optimizing reduced-space sequence analysis","author":"Wheeler Raymond","year":"2000","unstructured":"Raymond Wheeler and Richard Hughey. 2000. Optimizing reduced-space sequence analysis. Bioinformatics (2000).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_128_2","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/14.5.401","article-title":"Reduced space hidden Markov model training.","author":"Tarnas C.","year":"1998","unstructured":"C. Tarnas and R. Hughey. 1998. Reduced space hidden Markov model training. Bioinformatics (1998).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_129_2","article-title":"Detecting critical state before phase transition of complex biological systems by hidden Markov model","author":"Chen Pei","year":"2016","unstructured":"Pei Chen, Rui Liu, Yongjun Li, and Luonan Chen. 2016. Detecting critical state before phase transition of complex biological systems by hidden Markov model. Bioinformatics (2016).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_130_2","article-title":"The EMBL-EBI search and sequence analysis tools APIs in 2019","author":"Madeira F\u00e1bio","year":"2019","unstructured":"F\u00e1bio Madeira, Young mi Park, Joon Lee, Nicola Buso, Tamer Gur, Nandana Madhusoodanan, Prasad Basutkar, Adrian R. N. Tivey, Simon C. Potter, Robert D. Finn, and Rodrigo Lopez. 2019. The EMBL-EBI search and sequence analysis tools APIs in 2019. NAR (2019).","journal-title":"NAR"},{"key":"e_1_3_3_131_2","article-title":"HMMER web server: 2018 update","author":"Potter Simon C.","year":"2018","unstructured":"Simon C. Potter, Aur\u00e9lien Luciani, Sean R. Eddy, Youngmi Park, Rodrigo Lopez, and Robert D. Finn. 2018. HMMER web server: 2018 update. NAR (2018).","journal-title":"NAR"},{"key":"e_1_3_3_132_2","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky995","article-title":"The Pfam protein families database in 2019","author":"El-Gebali Sara","year":"2019","unstructured":"Sara El-Gebali, Jaina Mistry, Alex Bateman, Sean R. Eddy, Aur\u00e9lien Luciani, Simon C. Potter, Matloob Qureshi, Lorna J. Richardson, Gustavo A. Salazar, Alfredo Smart, Erik L. L. Sonnhammer, Layla Hirsh, Lisanna Paladin, Damiano Piovesan, Silvio C. E. Tosatto, and Robert D. Finn. 2019. The Pfam protein families database in 2019. NAR (2019).","journal-title":"NAR"},{"key":"e_1_3_3_133_2","article-title":"RefSeq: Expanding the Prokaryotic genome annotation pipeline reach with protein family model curation","author":"Li Wenjun","year":"2021","unstructured":"Wenjun Li, Kathleen R. O\u2019Neill, Daniel H. Haft, Michael DiCuccio, Vyacheslav Chetvernin, Azat Badretdin, George Coulouris, Farideh Chitsaz, Myra K. Derbyshire, A Scott Durkin, Noreen R. Gonzales, Marc Gwadz, Christopher J. Lanczycki, James S. Song, Narmada Thanki, Jiyao Wang, Roxanne A. Yamashita, Mingzhang Yang, Chanjuan Zheng, Aron Marchler-Bauer, and Fran\u00e7oise Thibaud-Nissen. 2021. RefSeq: Expanding the Prokaryotic genome annotation pipeline reach with protein family model curation. NAR (2021).","journal-title":"NAR"},{"key":"e_1_3_3_134_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pntd.0000716","article-title":"New assembly, reannotation and analysis of the entamoeba histolytica genome reveal new genomic features and protein content information","author":"Lorenzi Hernan A.","year":"2010","unstructured":"Hernan A. Lorenzi, Daniela Puiu, Jason R. Miller, Lauren M. Brinkac, Paolo Amedeo, Neil Hall, and Elisabet V. Caler. 2010. New assembly, reannotation and analysis of the entamoeba histolytica genome reveal new genomic features and protein content information. PLoS Negl. Trop. Dis. (2010).","journal-title":"PLoS Negl. Trop. Dis."},{"key":"e_1_3_3_135_2","unstructured":"Synopsys. 2016. Design Compiler (Version L-2016.03-SP2). (Mar.2016)."},{"key":"e_1_3_3_136_2","doi-asserted-by":"crossref","DOI":"10.1145\/1365490.1365500","article-title":"Scalable parallel programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for?","author":"Nickolls John","year":"2008","unstructured":"John Nickolls, Ian Buck, Michael Garland, and Kevin Skadron. 2008. Scalable parallel programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for? Queue (2008).","journal-title":"Queue"},{"key":"e_1_3_3_137_2","article-title":"Minimap2: Pairwise alignment for nucleotide sequences","author":"Li Heng","year":"2018","unstructured":"Heng Li. 2018. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics (2018).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_138_2","article-title":"Minimap and miniasm: Fast mapping and de novo assembly for noisy long sequences","author":"Li Heng","year":"2016","unstructured":"Heng Li. 2016. Minimap and miniasm: Fast mapping and de novo assembly for noisy long sequences. Bioinformatics (2016).","journal-title":"Bioinformatics"},{"key":"e_1_3_3_139_2","doi-asserted-by":"crossref","DOI":"10.1007\/s13369-016-2162-y","article-title":"Reconfigurable hardware accelerator for profile hidden Markov models","author":"Ibrahim Atef","year":"2016","unstructured":"Atef Ibrahim, Hamed Elsimary, Abdullah Aljumah, and Fayez Gebali. 2016. Reconfigurable hardware accelerator for profile hidden Markov models. Arab J. Sci. Eng. (2016).","journal-title":"Arab J. Sci. Eng."},{"key":"e_1_3_3_140_2","volume-title":"Proceedings of the ICCD","author":"Li Enliang","year":"2021","unstructured":"Enliang Li, Subho S. Banerjee, Sitao Huang, Ravishankar K. Iyer, and Deming Chen. 2021. Improved GPU implementations of the pair-HMM forward algorithm for DNA sequence alignment. In Proceedings of the ICCD."},{"key":"e_1_3_3_141_2","volume-title":"Proceedings of the BIBM","author":"Wertenbroek Rick","year":"2019","unstructured":"Rick Wertenbroek and Yann Thoma. 2019. Acceleration of the pair-HMM forward algorithm on FPGA with cloud integration for GATK. In Proceedings of the BIBM."},{"key":"e_1_3_3_142_2","volume-title":"Proceedings of the FPL","author":"Banerjee Subho S.","year":"2017","unstructured":"Subho S. Banerjee, Mohamed el Hadedy, Ching Y. Tan, Zbigniew T. Kalbarczyk, Steve Lumetta, and Ravishankar K. Iyer. 2017. On accelerating pair-HMM computations in programmable hardware. In Proceedings of the FPL."},{"key":"e_1_3_3_143_2","article-title":"A high-throughput pruning-based pair-hidden-Markov-model hardware accelerator for next-generation DNA sequencing","author":"Wu Xiao","year":"2021","unstructured":"Xiao Wu, Arun Subramaniyan, Zhehong Wang, Satish Narayanasamy, Reetuparna Das, and David Blaauw. 2021. A high-throughput pruning-based pair-hidden-Markov-model hardware accelerator for next-generation DNA sequencing. IEEE Solid-State Circ. Lett. (2021).","journal-title":"IEEE Solid-State Circ. Lett."},{"key":"e_1_3_3_144_2","article-title":"CUDAMPF++: A proactive resource exhaustion scheme for accelerating homologous sequence search on CUDA-enabled GPU","author":"Jiang Hanyu","year":"2018","unstructured":"Hanyu Jiang, Narayan Ganesan, and Yu-Dong Yao. 2018. CUDAMPF++: A proactive resource exhaustion scheme for accelerating homologous sequence search on CUDA-enabled GPU. IEEE TPDS (2018).","journal-title":"IEEE TPDS"},{"key":"e_1_3_3_145_2","volume-title":"Proceedings of the IPCCC","author":"Quirem Saddam","year":"2011","unstructured":"Saddam Quirem, Fahian Ahmed, and Byeong Kil Lee. 2011. CUDA acceleration of P7Viterbi algorithm in HMMER 3.0. In Proceedings of the IPCCC."},{"key":"e_1_3_3_146_2","article-title":"Hardware acceleration of HMMER on FPGAs","author":"Derrien Steven","year":"2008","unstructured":"Steven Derrien and Patrice Quinton. 2008. Hardware acceleration of HMMER on FPGAs. J. Signal Process. Syst. (2008).","journal-title":"J. Signal Process. Syst."},{"key":"e_1_3_3_147_2","volume-title":"Proceedings of the IPDPS","author":"Oliver Tim","year":"2007","unstructured":"Tim Oliver, Leow Yuan Yeow, and Bertil Schmidt. 2007. High performance database searching with HMMer on FPGAs. In Proceedings of the IPDPS."},{"key":"e_1_3_3_148_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.parco.2008.08.003","article-title":"Integrating FPGA acceleration into HMMer","author":"Oliver Tim","year":"2008","unstructured":"Tim Oliver, Leow Yuan Yeow, and Bertil Schmidt. 2008. Integrating FPGA acceleration into HMMer. Parallel Comput. (2008).","journal-title":"Parallel Comput."}],"container-title":["ACM Transactions on Architecture and Code Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632950","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632950","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:35:50Z","timestamp":1750178150000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,15]]},"references-count":147,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3,31]]}},"alternative-id":["10.1145\/3632950"],"URL":"https:\/\/doi.org\/10.1145\/3632950","relation":{},"ISSN":["1544-3566","1544-3973"],"issn-type":[{"value":"1544-3566","type":"print"},{"value":"1544-3973","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,15]]},"assertion":[{"value":"2022-08-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}