ChiMamba: Predicting Chromatin Interactions Based on Mamba

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成果类型：

会议论文

作者：

Zhang, Jianing;Song, Chao;Cui, Ting;Li, Chunquan;Ma, Jiquan

通讯作者：

Ma, JQ;Li, CQ

作者机构：

[Ma, JQ; Zhang, Jianing; Ma, Jiquan] Heilongjiang Univ, Dept Comp Sci & Technol, Harbin 150080, Peoples R China.

[Li, Chunquan] Univ South China, Sch Comp, Hengyang 421001, Peoples R China.

[Li, Chunquan; Song, Chao] Univ South China, Hengyang Med Sch, Affiliated Hosp 1, Cardiovasc Lab Big Data & Imaging Artificial Inte, Hengyang 421001, Peoples R China.

[Cui, Ting] Univ South China, Hengyang Med Sch, Sch Basic Med Sci, Dept Biochem & Mol Biol, Hengyang 421001, Peoples R China.

通讯机构：

[Ma, JQ ] H

[Li, CQ ] U

Heilongjiang Univ, Dept Comp Sci & Technol, Harbin 150080, Peoples R China.

Univ South China, Sch Comp, Hengyang 421001, Peoples R China.

Univ South China, Hengyang Med Sch, Affiliated Hosp 1, Cardiovasc Lab Big Data & Imaging Artificial Inte, Hengyang 421001, Peoples R China.

语种：

英文

关键词：

Chromatin Interaction;Mamba;Deep Learning

期刊：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISSN：

0302-9743

年：

2024

卷：

14882

页码：

50-61

会议名称：

20th International Conference on Intelligent Computing (ICIC)

会议论文集名称：

Lecture Notes in Bioinformatics

会议时间：

AUG 05-08, 2024

会议地点：

Tianjin Univ Sci & Tech, Tianjin, PEOPLES R CHINA

会议主办单位：

Tianjin Univ Sci & Tech

主编：

Huang, DS Pan, Y Zhang, Q

出版地：

152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE

出版者：

SPRINGER-VERLAG SINGAPORE PTE LTD

ISBN：

978-981-97-5691-9; 978-981-97-5692-6

DOI：

10.1007/978-981-97-5692-6_5

基金类别：

National Natural Science Foundation of China [62171166, 62302206]; Research Foundation of the First Affiliated Hospital ofUniversity of South China for Advanced Talents [20210002-1005 USCAT-2021-01]; Natural Science Foundation of Hunan Province [2023JJ40594, 2023JJ30536]

机构署名：

本校为通讯机构

院系归属：

药学与生物科学学院

摘要：

Chromatin interactions play a crucial role in establishing specific chromatin structures and regulating gene expression over distant genomic regions. However, the high cost of sequencing techniques limits the identification of chromatin interactions across diverse samples. Considering its significance, quite a few deep learning-based methods have recently emerged for computationally detecting chromatin interactions. In this study, we propose ChiMamba, a bidirectional selective state space model based on Mamba, which integrates genomic sequences and chromatin accessibility data to predict chrom...

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