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Start Over Author "Yichao Cai" Journal genome biology
2 results on '"Yichao Cai"'

1. Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Author

Fan Cao, Yu Zhang, Yichao Cai, Sambhavi Animesh, Ying Zhang, Semih Can Akincilar, Yan Ping Loh, Xinya Li, Wee Joo Chng, Vinay Tergaonkar, Chee Keong Kwoh, and Melissa J. Fullwood

Subjects
Machine learning, 3D genome organization, Chromatin interactions, ChIA-PET, Hi-C, DNA sequence, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Published
2021
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2. Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Author

Ying Zhang, Vinay Tergaonkar, Xinya Li, Melissa J. Fullwood, Yu Zhang, Fan Cao, Sambhavi Animesh, Yan Ping Loh, Yichao Cai, Wee Joo Chng, Chee Keong Kwoh, Semih Can Akıncılar, Fullwood, Melissa J [0000-0003-0692-4434], Apollo - University of Cambridge Repository, School of Biological Sciences, School of Computer Science and Engineering, and Institute of Molecular and Cell Biology, A*STAR

Subjects
FOS: Computer and information sciences, Bioinformatics, QH301-705.5, DNA sequence, Method, Computational biology, Biology, QH426-470, DNA sequencing, Machine Learning, chemistry.chemical_compound, Hi-C, RNA polymerase, Gene expression, Machine learning, 3D genome organization, Genetics, Humans, Biology (General), ChIA-PET, Genome, Leukemia, Artificial neural network, Base Sequence, Biological sciences [Science], Computational Biology, 3D Genome Organization, Human genetics, Chromatin, chemistry, CTCF, Computer science and engineering [Engineering], Neural Networks, Computer, Chromatin interactions
Abstract

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation (NRF) Singapore through an NRF Fellowship awarded to M.J.F (NRF-NRFF2012-054) and NTU start-up funds awarded to M.J.F. This research is supported by the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 awarded to Daniel Tenen as lead PI with M.J.F as co-investigator (MOE2014-T3-1-006). This research is supported by a National Research Foundation Competitive Research Programme grant awarded to V.T. as lead PI and M.J.F. as co-PI (NRF-CRP17-2017-02). This research is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative. This research is supported by a Ministry of Education Tier II grant awarded to M.J.F (T2EP30120-0020).

Published
2021

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