Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders

•Enhanced classification of hotpot regions.•Autoencoder based deep feature extraction.•Combination of class specific autoencoders. In yeast and in some mammals the frequencies of recombination are high in some genomic locations which are known as recombination hotspots and in the locations where the...

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Vydáno v:Journal of theoretical biology Ročník 444; s. 73 - 82
Hlavní autoři: Nath, Abhigyan, Karthikeyan, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 07.05.2018
Témata:
Algorithms
Autoencoders
Base Sequence
Classification
Deep features
Deep Learning
Neural Networks (Computer)
Recombination hotspots
Recombination, Genetic - genetics
Saccharomyces cerevisiae - genetics
Deep learning
Recombination hotspots
Deep features
Autoencoders
ISSN:0022-5193, 1095-8541, 1095-8541
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Shrnutí:•Enhanced classification of hotpot regions.•Autoencoder based deep feature extraction.•Combination of class specific autoencoders. In yeast and in some mammals the frequencies of recombination are high in some genomic locations which are known as recombination hotspots and in the locations where the recombination is below average are consequently known as coldspots. Knowledge of the hotspot regions gives clues about understanding the meiotic process and also in understanding the possible effects of sequence variation in these regions. Moreover, accurate information about the hotspot and coldspot regions can reveal insights into the genome evolution. In the present work, we have used class specific autoencoders for feature extraction and reduction. Subsequently the deep features that are extracted from the autoencoders were used to train three different classifiers, namely: gradient boosting machines, random forest and deep learning neural networks for predicting the hotspot and coldspot regions. A comparative performance analysis was carried out by experimenting on deep features extracted from different sets of the training data using autoencoders for selecting the best set of deep features. It was observed that learning algorithms trained on features extracted from the combined class specific autoencoder out performed when compared with the performances of these learning algorithms trained with other sets of deep features. So the combined class-specific autoencoder based feature extraction can be applied to a growing range of biological problems to achieve superior prediction performance.
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ISSN:0022-5193
1095-8541
1095-8541
DOI:10.1016/j.jtbi.2018.02.016