PT  - JOURNAL ARTICLE
AU  - Li Shen
TI  - Automatic genome segmentation with HMM-ANN hybrid models
AID  - 10.1101/034579
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 034579
4099  - http://biorxiv.org/content/early/2015/12/16/034579.short
4100  - http://biorxiv.org/content/early/2015/12/16/034579.full
AB  - We consider the problem of automatic genome segmentation (AGS) that aims to assign discrete labels to all genomic regions based on multiple ChIP-seq samples. We propose to use a hybrid model that combines a hidden Markov model (HMM) with an artificial neural network (ANN) to overcome the weaknesses of a standard HMM. Our contributions are threefold: first, we benchmark two approaches to generate targets for ANN training on an example dataset; second, we investigate many different ANN models to identify the ones with best predictions on chromatin states; third, we test different hyper-parameters and discuss how they affect the machine learning algorithms’ performance. We find our best performing models to beat two pervious state-of-the-art methods for AGS by large margins.