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

@article{Roy2017NavigatingTF,
  title={Navigating the Functional Landscape of Transcription Factors via Non-Negative Tensor Factorization Analysis of MEDLINE Abstracts},
  author={Sujoy Roy and Daqing Yun and Behrouz Madahian and Michael W. Berry and Lih-Yuan Deng and Dan Goldowitz and Ramin Homayouni},
  journal={Frontiers in Bioengineering and Biotechnology},
  year={2017},
  volume={5}
}
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… 

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