{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T22:40:05Z","timestamp":1755902405805,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,9]]},"DOI":"10.1145\/3637732.3637783","type":"proceedings-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T11:04:51Z","timestamp":1709118291000},"page":"99-106","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Non-negative Matrix Tri-Factorization and Knowledge-Based Embeddings for Drug Repurposing: an Application to Parkinson&amp;#39;s Disease"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-2229","authenticated-orcid":false,"given":"Letizia","family":"Messa","sequence":"first","affiliation":[{"name":"Department of Electronics,Information and Bioengineering (DEIB), Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6059-0204","authenticated-orcid":false,"given":"Carolina","family":"Testa","sequence":"additional","affiliation":[{"name":"Department of Electronics,Information and Bioengineering (DEIB), Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4603-396X","authenticated-orcid":false,"given":"Stephana","family":"Carelli","sequence":"additional","affiliation":[{"name":"Center of Functional Genomics and Rare Diseases, Buzzi Children's Hospital, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7944-3143","authenticated-orcid":false,"given":"Federica","family":"Rey","sequence":"additional","affiliation":[{"name":"Pediatric Clinical Research Center &amp;#34;Fondazione Romeo ed Enrica Invernizzi&amp;#34;, DIBIC, University of Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9571-0862","authenticated-orcid":false,"given":"Cristina","family":"Cereda","sequence":"additional","affiliation":[{"name":"Center of Functional Genomics and Rare Diseases, Buzzi Children's Hospital, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2585-7206","authenticated-orcid":false,"given":"Manuela Teresa","family":"Raimondi","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-2415","authenticated-orcid":false,"given":"Stefano","family":"Ceri","sequence":"additional","affiliation":[{"name":"Department of Electronics,Information and Bioengineering (DEIB), Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9786-2851","authenticated-orcid":false,"given":"Pietro","family":"Pinoli","sequence":"additional","affiliation":[{"name":"Department of Electronics,Information and Bioengineering (DEIB), Politecnico di Milano, Italy"}]}],"member":"320","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular pharmaceutics 13, 7","author":"Aliper Alexander","year":"2016","unstructured":"Alexander Aliper, Sergey Plis, Artem Artemov, Alvaro Ulloa, Polina Mamoshina, and Alex Zhavoronkov. 2016. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular pharmaceutics 13, 7 (2016), 2524\u20132530."},{"key":"e_1_3_2_1_2_1","volume-title":"Low data drug discovery with one-shot learning. ACS central science 3, 4","author":"Altae-Tran Han","year":"2017","unstructured":"Han Altae-Tran, Bharath Ramsundar, Aneesh\u00a0S Pappu, and Vijay Pande. 2017. Low data drug discovery with one-shot learning. ACS central science 3, 4 (2017), 283\u2013293."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40268-020-00316-1"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIBCB.2019.8791474"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2991763"},{"key":"e_1_3_2_1_6_1","volume-title":"Rutin as a potent antioxidant: Implications for neurodegenerative disorders. Oxidative Medicine and Cellular Longevity","author":"Enogieru Adaze\u00a0Bijou","year":"2018","unstructured":"Adaze\u00a0Bijou Enogieru, William Haylett, Donavon\u00a0Charles Hiss, Soraya Bardien, and Okobi\u00a0Eko Ekpo. 2018. Rutin as a potent antioxidant: Implications for neurodegenerative disorders. Oxidative Medicine and Cellular Longevity (2018)."},{"key":"e_1_3_2_1_7_1","volume-title":"D1","author":"Fabregat Antonio","year":"2018","unstructured":"Antonio Fabregat, Steven Jupe, Lisa Matthews, Konstantinos Sidiropoulos, Marc Gillespie, Phani Garapati, Robin Haw, Bijay Jassal, Florian Korninger, Bruce May, 2018. The reactome pathway knowledgebase. Nucleic acids research 46, D1 (2018), D649\u2013D655."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.1013903"},{"key":"e_1_3_2_1_9_1","volume-title":"PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular systems biology 7, 1","author":"Gottlieb Assaf","year":"2011","unstructured":"Assaf Gottlieb, Gideon\u00a0Y Stein, Eytan Ruppin, and Roded Sharan. 2011. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular systems biology 7, 1 (2011), 496."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0006536"},{"key":"e_1_3_2_1_11_1","volume-title":"Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks. BMC bioinformatics 20","author":"Hu ShanShan","year":"2019","unstructured":"ShanShan Hu, Chenglin Zhang, Peng Chen, Pengying Gu, Jun Zhang, and Bing Wang. 2019. Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks. BMC bioinformatics 20 (2019), 1\u201312."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00450-7"},{"key":"e_1_3_2_1_13_1","volume-title":"KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 28, 1","author":"Kanehisa Minoru","year":"2000","unstructured":"Minoru Kanehisa and Susumu Goto. 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 28, 1 (2000), 27\u201330."},{"key":"e_1_3_2_1_14_1","volume-title":"Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS computational biology 5, 7","author":"Kinnings L","year":"2009","unstructured":"Sarah\u00a0L Kinnings, Nina Liu, Nancy Buchmeier, Peter\u00a0J Tonge, Lei Xie, and Philip\u00a0E Bourne. 2009. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS computational biology 5, 7 (2009), e1000423."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2012.6392722"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr260"},{"key":"e_1_3_2_1_17_1","volume-title":"The novel function of bexarotene for neurological diseases. Ageing Research Reviews","author":"Liu Yangtao","year":"2023","unstructured":"Yangtao Liu, Pengwei Wang, Guofang Jin, Peijie Shi, Yonghui Zhao, Jiayi Guo, Yaling Yin, Qianhang Shao, Peng Li, and Pengfei Yang. 2023. The novel function of bexarotene for neurological diseases. Ageing Research Reviews (2023), 102021."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv055"},{"key":"e_1_3_2_1_19_1","volume-title":"D1","author":"Martens Marvin","year":"2021","unstructured":"Marvin Martens, Ammar Ammar, Anders Riutta, Andra Waagmeester, Denise\u00a0N Slenter, Kristina Hanspers, Ryan A.\u00a0Miller, Daniela Digles, Elisson\u00a0N Lopes, Friederike Ehrhart, 2021. WikiPathways: connecting communities. Nucleic acids research 49, D1 (2021), D613\u2013D621."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.taap.2018.03.005"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0061318"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-5-30"},{"key":"e_1_3_2_1_23_1","volume-title":"A review of computational drug repurposing. Translational and clinical pharmacology 27, 2","author":"Park Kyungsoo","year":"2019","unstructured":"Kyungsoo Park. 2019. A review of computational drug repurposing. Translational and clinical pharmacology 27, 2 (2019), 59\u201363."},{"key":"e_1_3_2_1_24_1","volume-title":"Drug repurposing: a promising tool to accelerate the drug discovery process. Drug discovery today 24, 10","author":"Parvathaneni Vineela","year":"2019","unstructured":"Vineela Parvathaneni, Nishant\u00a0S Kulkarni, Aaron Muth, and Vivek Gupta. 2019. Drug repurposing: a promising tool to accelerate the drug discovery process. Drug discovery today 24, 10 (2019), 2076\u20132085."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2021.3091814"},{"key":"e_1_3_2_1_26_1","volume-title":"Drug repurposing: progress, challenges and recommendations. Nature reviews Drug discovery 18, 1","author":"Pushpakom Sudeep","year":"2019","unstructured":"Sudeep Pushpakom, Francesco Iorio, Patrick\u00a0A Eyers, K\u00a0Jane Escott, Shirley Hopper, Andrew Wells, Andrew Doig, Tim Guilliams, Joanna Latimer, Christine McNamee, 2019. Drug repurposing: progress, challenges and recommendations. Nature reviews Drug discovery 18, 1 (2019), 41\u201358."},{"key":"e_1_3_2_1_27_1","volume-title":"The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016","author":"Rouillard D","year":"2016","unstructured":"Andrew\u00a0D Rouillard, Gregory\u00a0W Gundersen, Nicolas\u00a0F Fernandez, Zichen Wang, Caroline\u00a0D Monteiro, Michael\u00a0G McDermott, and Avi Ma\u2019ayan. 2016. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016 (2016)."},{"key":"e_1_3_2_1_28_1","volume-title":"PID: the pathway interaction database. Nucleic acids research 37, suppl_1","author":"Schaefer F","year":"2009","unstructured":"Carl\u00a0F Schaefer, Kira Anthony, Shiva Krupa, Jeffrey Buchoff, Matthew Day, Timo Hannay, and Kenneth\u00a0H Buetow. 2009. PID: the pathway interaction database. Nucleic acids research 37, suppl_1 (2009), D674\u2013D679."},{"key":"e_1_3_2_1_29_1","volume-title":"Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS central science 4, 1","author":"Segler HS","year":"2018","unstructured":"Marwin\u00a0HS Segler, Thierry Kogej, Christian Tyrchan, and Mark\u00a0P Waller. 2018. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS central science 4, 1 (2018), 120\u2013131."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.hermed.2019.100322"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jphotobiol.2018.04.019"},{"key":"e_1_3_2_1_32_1","volume-title":"Serum albumin, cognitive function, motor impairment, and survival prognosis in Parkinson disease. Medicine 101, 37","author":"Sun Shujun","year":"2022","unstructured":"Shujun Sun, Yiyong Wen, and Yandeng Li. 2022. Serum albumin, cognitive function, motor impairment, and survival prognosis in Parkinson disease. Medicine 101, 37 (2022)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3608164.3608171"},{"key":"e_1_3_2_1_34_1","volume-title":"International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer, 94\u2013104","author":"Testa Carolina","year":"2021","unstructured":"Carolina Testa, Sara Pid\u00f2, and Pietro Pinoli. 2021. A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity. In International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer, 94\u2013104."},{"key":"e_1_3_2_1_35_1","volume-title":"Capsaicin consumption reduces brain amyloid-beta generation and attenuates Alzheimer\u2019s disease-type pathology and cognitive deficits in APP\/PS1 mice. Translational psychiatry 10, 1","author":"Wang Jun","year":"2020","unstructured":"Jun Wang, Bin-Lu Sun, Yang Xiang, Ding-Yuan Tian, Chi Zhu, Wei-Wei Li, Yu-Hui Liu, Xian-Le Bu, Lin-Lin Shen, Wang-Sheng Jin, 2020. Capsaicin consumption reduces brain amyloid-beta generation and attenuates Alzheimer\u2019s disease-type pathology and cognitive deficits in APP\/PS1 mice. Translational psychiatry 10, 1 (2020), 230."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0078518"},{"key":"e_1_3_2_1_37_1","volume-title":"DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research 34, suppl_1","author":"Wishart S","year":"2006","unstructured":"David\u00a0S Wishart, Craig Knox, An\u00a0Chi Guo, Savita Shrivastava, Murtaza Hassanali, Paul Stothard, Zhan Chang, and Jennifer Woolsey. 2006. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research 34, suppl_1 (2006), D668\u2013D672."},{"key":"e_1_3_2_1_38_1","volume-title":"Estimation of clinical trial success rates and related parameters. Biostatistics 20, 2 (01","author":"Wong Chi\u00a0Heem","year":"2018","unstructured":"Chi\u00a0Heem Wong, Kien\u00a0Wei Siah, and Andrew\u00a0W Lo. 2018. Estimation of clinical trial success rates and related parameters. Biostatistics 20, 2 (01 2018), 273\u2013286."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab487"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btn162"}],"event":{"name":"ICBBE 2023: 2023 10th International Conference on Biomedical and Bioinformatics Engineering","acronym":"ICBBE 2023","location":"Kyoto Japan"},"container-title":["Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637732.3637783","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637732.3637783","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T22:00:00Z","timestamp":1755900000000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637732.3637783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,9]]},"references-count":40,"alternative-id":["10.1145\/3637732.3637783","10.1145\/3637732"],"URL":"https:\/\/doi.org\/10.1145\/3637732.3637783","relation":{},"subject":[],"published":{"date-parts":[[2023,11,9]]},"assertion":[{"value":"2024-02-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}