Faizy Ahsan

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

Supplementary data are available at Bioinformatics online.

2022-04-13

Bioinformatics (publié)

doi.org

Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites

Francois Laviolette

The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant m e… (voir plus)thodological d evelopments f rom t he field of machine learning. Such computational models are essential to help interpret the non-coding portion of human genomes, and to learn more about the regulatory mechanisms controlling gene expression. In parallel, massive genome sequencing efforts have produced assembled genomes for hundred of vertebrate species, but this data is underused. We present PhyloReg, a new semi-supervised learning approach that can be used for a wide variety of sequence-to-function prediction problems, and that takes advantage of hundreds of millions of years of evolution to regularize predictors and improve accuracy. We demonstrate that PhyloReg can be used to better train a previously proposed deep learning model of transcription factor binding. Simulation studies further help delineate the benefits o f t he a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.

2020-12-15

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publié)

doi.org

Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy

Faizy Ahsan

Doina Precup

Mathieu Blanchette

2016-10-01

ACM International Conference on Bioinformatics, Computational Biology and Biomedicine (publié)

doi.org