{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T02:45:20Z","timestamp":1761101120253},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)\u2013gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications.<\/jats:p>\n               <jats:p>Results: We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF\u2013gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and\/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA.<\/jats:p>\n               <jats:p>Availability: The ROBNCA software is available at http:\/\/people.tamu.edu\/\u223camina\/ROBNCA<\/jats:p>\n               <jats:p>Contact: \u00a0serpedin@ece.tamu.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt433","type":"journal-article","created":{"date-parts":[[2013,8,13]],"date-time":"2013-08-13T00:24:10Z","timestamp":1376353450000},"page":"2410-2418","source":"Crossref","is-referenced-by-count":20,"title":["ROBNCA: robust network component analysis for recovering transcription factor activities"],"prefix":"10.1093","volume":"29","author":[{"given":"Amina","family":"Noor","sequence":"first","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, 2Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, 3Department of Chemical Engineering and 4Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar"}]},{"given":"Aitzaz","family":"Ahmad","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, 2Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, 3Department of Chemical Engineering and 4Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar"}]},{"given":"Erchin","family":"Serpedin","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, 2Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, 3Department of Chemical Engineering and 4Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar"}]},{"given":"Mohamed","family":"Nounou","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, 2Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, 3Department of Chemical Engineering and 4Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar"}]},{"given":"Hazem","family":"Nounou","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, 2Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, 3Department of Chemical Engineering and 4Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar"}]}],"member":"286","published-online":{"date-parts":[[2013,8,11]]},"reference":[{"key":"2023012810464358300_btt433-B1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"Boyd","year":"2004"},{"key":"2023012810464358300_btt433-B2","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1093\/bioinformatics\/btn131","article-title":"Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data","volume":"24","author":"Chang","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012810464358300_btt433-B3","first-page":"29","article-title":"Independent component analysis","author":"Comon","year":"1992","journal-title":"Higher-Order Statistics"},{"key":"2023012810464358300_btt433-B4","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1214\/10-AOAS410","article-title":"Robust graphical modeling of gene networks using classical and alternative t-distributions","volume":"5","author":"Finegold","year":"2011","journal-title":"Ann. 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