Navigating the Functional Landscape of Transcription Factors via Non-Negative Tensor Factorization Analysis of MEDLINE Abstracts

• Published in: Frontiers in Bioengineering and Biotechnology Vol. 5; p. 48
• Main Authors: Roy, Sujoy, Yun, Daqing, Madahian, Behrouz, Berry, Michael W., Deng, Lih-Yuan, Goldowitz, Daniel, Homayouni, Ramin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media SA 28.08.2017; Frontiers Media S.A
• Subjects: applied multilinear algebra; Bioengineering and Biotechnology; biomedical text mining; Biotechnology; multiway analysis; tensor decomposition; tensor factorization; TP248.13-248.65; transcription factors; applied multilinear algebra; tensor factorization; transcription factors; multiway analysis; biomedical text mining; tensor decomposition
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2017.00048

In this study, we developed and evaluated a novel text-mining approach, using non-negative tensor factorization (NTF), to simultaneously extract and functionally annotate transcriptional modules consisting of sets of genes, transcription factors (TFs), and terms from MEDLINE abstracts. A sparse 3-mode term × gene × TF tensor was constructed that contained weighted frequencies of 106,895 terms in 26,781 abstracts shared among 7,695 genes and 994 TFs. The tensor was decomposed into sub-tensors using non-negative tensor factorization (NTF) across 16 different approximation ranks. Dominant entries of each of 2,861 sub-tensors were extracted to form term-gene-TF annotated transcriptional modules (ATMs). More than 94% of the ATMs were found to be enriched in at least one KEGG pathway or GO category, suggesting that the ATMs are functionally relevant. One advantage of this method is that it can discover potentially new gene-TF associations from the literature. Using a set of microarray and ChIP-Seq datasets as gold standard, we show that the precision of our method for predicting gene-TF associations is significantly higher than chance. In addition, we demonstrate that the terms in each ATM can be used to suggest new GO classifications to genes and TFs. Taken together, our results indicate that NTF is useful for simultaneous extraction and functional annotation of transcriptional regulatory networks from unstructured text, as well as for literature based discovery. A web tool called Transcriptional Regulatory Modules Extracted from Literature (TREMEL), available at http://binf1.memphis.edu/tremel, was built to enable browsing and searching of ATMs.