Convolutional Neural Network and Probabilistic model for Transcript Factor Binding Sites motif finding

--Jinli Zhang. GGB program. PhD student

Project progress

• Jan/04-Jan/15

Reading paper about convolutional neural network and motif finding, learned how the probabilistic model was used to finding the motif.

• Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data. Jack Lanchantin, Yanjun Qi. Bioinformatics.,36, i659–i667

• Chapter 4 Exhaustive search. Neil C. Jones and Pavel Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004.

• Jan/16 - Jan/31

  • download datasets(bed file) from Encodes, prepare .fasta files as input for PWD matrix and CNN.

  • watching a Youtube video about how Gibbs sampling was used to do the motif finding.

• Feb/1 - Feb/13

  • implementing Gibbs sampling and Convolutional neural network on small testing data.

  • reading how Hidden Markov Model was used to do the motif finding.

  • implementing Expectation and maximization on small testing data and watching youtube https://www.youtube.com/watch?v=kq5NAd4pnkU&t=587s

• Feb/14 - Feb/27

  • Finishing expectation and maximization for motif finding

  • watch youtube video https://www.youtube.com/watch?v=0Zuqytgf6yY&t=1286s for CNN methods.

  • implementing CNN

• Feb/27- end

  • reading a Hidden Markov Model model from one github. https://github.com/hamzarawal/HMM-Baum-Welch-Algorithm

  • implementing Hidden Markov Model

  • wrapping up the projects.