ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics

Shi，Ning1; Zhu，Zexuan2; Tang，Ke3; Parker，David1; He，Shan1,3

2020-01-15

10.1093/bioinformatics/btz563

BIOINFORMATICS   影响因子和分区

1367-4803

1367-4811

MOTIVATION: Inferring gene regulatory networks from gene expression time series data is important for gaining insights into the complex processes of cell life. A popular approach is to infer Boolean networks. However, it is still a pressing open problem to infer accurate Boolean networks from experimental data that are typically short and noisy. RESULTS: To address the problem, we propose a Boolean network inference algorithm which is able to infer accurate Boolean network topology and dynamics from short and noisy time series data. The main idea is that, for each target gene, we use an And/Or tree ensemble algorithm to select prime implicants of which each is a conjunction of a set of input genes. The selected prime implicants are important features for predicting the states of the target gene. Using these important features we then infer the Boolean function of the target gene. Finally, the Boolean functions of all target genes are combined as a Boolean network. Using the data generated from artificial and real-world gene regulatory networks, we show that our algorithm can infer more accurate Boolean network topology and dynamics from short and noisy time series data than other algorithms. Our algorithm enables us to gain better insights into complex regulatory mechanisms of cell life. AVAILABILITY AND IMPLEMENTATION: Package ATEN is freely available at https://github.com/ningshi/ATEN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SCI

英语

其他

Shenzhen Scientific Research and Development Funding Program[JCYJ20170302154328155]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:000526660300031

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

2-s2.0-85078560585

Scopus

1.School of Computer Science,University of Birmingham,Birmingham,B15 2TT,United Kingdom
2.College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,518060,China
3.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Shi，Ning,Zhu，Zexuan,Tang，Ke,et al. ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics[J]. BIOINFORMATICS,2020,36(2):578-585.

Shi，Ning,Zhu，Zexuan,Tang，Ke,Parker，David,&He，Shan.(2020).ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics.BIOINFORMATICS,36(2),578-585.

Shi，Ning,et al."ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics".BIOINFORMATICS 36.2(2020):578-585.