Automatic genome segmentation with HMM-ANN hybrid models

Li Shen

doi:10.1101/034579

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bioRxiv · 10.1101/034579

Automatic genome segmentation with HMM-ANN hybrid models

Li Shen

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Abstract

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.

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Li Shen. 2015-12-16. Automatic genome segmentation with HMM-ANN hybrid models. https://doi.org/10.1101/034579

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