Research Areas: Bioinformatics, Graph Theory and Applications.
0531-88361896
bingqiang@sdu.edu.cn
2007.1-2010.1 University of Georgia Bioinformatics Joint Ph.D student
2006.9-2010.7 Shandong University Operational Research Doctoral Degree 2003.9-2006.7 Shandong University Operational Research Master's Degree
1999.9-2003.7 Shandong University Mathematics and Applied Mathematics Bachelor's Degree
2017.9--Now
School of Mathematics, Shandong University Professor
2013.9--2017.8
School of Mathematics, Shandong University Associate Professor
2010.7--2013.8
School of Mathematics, Shandong University Lecturer
Taishan Scholar Young Expert 2022
Changjiang Scholar Young Expert 2022
Since 2018
1. Highly accurate diagnosis of pancreatic cancer by integrative modeling using gut microbiome and exposome data, Yuli Zhang, Haohong Zhang, Bingqiang Liu*, Kang Ning*, iScience, 15 March 2024. 27(3): 109294. (SCI, IF 5.750)
2. A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data, Yang Li#, Yizhong Wang#, Cankun Wang, Anjun Ma, Qin Ma*, Bingqiang Liu*, Patterns, 2024 Feb 02, 5, 100927. (SCI, IF 6.500)
3. CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq Yizhong Wang#, Yang Li#, Cankun Wang, Chan-Wang Jerry Lio, Qin Ma*, Bingqiang Liu* Briefings in Bioinformatics, 2024 Jan 06, 25(1): 1-8. (SCI, IF 9.500)
4. MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer. Xiaoying Wang#, Maoteng Duan#, Jingxian Li, AnjunMa, Gang Xin, Dong Xu, Zihai Li, Bingqiang Liu*, QinMa*. Nature Communications, 2024, 15:338. (SCI, IF16.600)
5. Inference of disease-associated microbial gene modules based on metagenomic and meta-transcriptomic data. Zhaoqian Liu, Qi Wang, Anjun Ma, Shaohong Feng, Dongjun Chung, Jing Zhao, Qin Ma*, Bingqiang Liu*. Computers in Biology and Medicine, 2023. 165(107458). (SCI, IF7.700)
6. Computational methods and challenges in analyzing intratumoral microbiome data. Qi Wang, Zhaoqian Liu, Anjun Ma, Zihai Li, Bingqiang Liu*, Qin Ma*. Trends in Microbiology. 2023. 31(7): 707-722. (SCI, IF18.230)
7. Single-cell biological network inference using a heterogeneous graph transformer. Anjun Ma#, Xiaoying Qang#, Jingxian Li, Cankun Wang, Tong Xiao, Yuntao Liu, Hao Cheng, Juexin Wang, Yang Li, Yuzhou Chang, Jinpu Li, Duolin Wang, Yuexu Jiang, Li Su, Gang Xin, Shaopeng Gu, Zihai Li, Bingqiang Liu*, Dong Xu*, Qin Ma*. Nature Communications, 2023, 14:964. (SCI, IF17.694)
8. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data, Junyi Chen#, Xiaoying Wang#, Anjun Ma*, Qi-En Wang, Bingqiang Liu, Lang Li, Dong Xu, Qin Ma*, Nature Communications, 2022, 13:6494. (SCI, IF17.694)
9. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning, Yuzhou Chang#, Fei He#, Juexin Wang#, Shuo Chen, Jingyi Li, Jixin Liu, Yang Yu, Li Su, Anjun Ma, Carter Allen, Yu Lin, Shaoli Sun, Bingqiang Liu, José Javier Otero, Dongjun Chung, Hongjun Fu, Zihai Li, Dong Xu, Qin Ma. Computational and Structural Biotechnology Journal, 2022, V20: 4600-4617. (SCI, IF 6.155)
10. Leveraging Existing 16SrRNA Microbial Data to Define a Composite Biomarker for Autism Spectrum Disorder, Yushuang Xu#, Yihua Wang#, Jinshuang Xu, Yu Song, Bingqiang Liu*, Zhifan Xiong*, Microbiology Spectrum. 2022, 10(4): e0033122. (SCI, IF 9.043)
11. DESSO-DB: A web database for sequence and shape motif analyses and identification. Xiaoying Wang, Cankun Wang, Lang Li, Qin Ma, Anjun Ma*, Bingqiang Liu*, Computational and Structural Biotechnology Journal, 2022, V20: 3053-3058. (SCI, IF 6.155)
12. A novel computational framework for genome-scale alternative transcription units prediction. Qi Wang, Zhaoqian Liu, Bo Yan,Wen-Chi Chou, Laurence Ettwiller, Qin Ma* and Bingqiang Liu*, Briefings in bioinformatics, 2021, Nov 22(6). (SCI, IF 13.994)
13. The functional determinants in the organization of bacterial genomes. Zhaoqian Liu, Jingtong Feng, Bin Yu, Qin Ma*, and Bingqiang Liu*, Briefings in bioinformatics, 2021 May 20; 22(3). (SCI, IF 13.994)
14. Network analyses in microbiome based on high-throughput multi-omics data. Zhaoqian Liu#, Anjun Ma#, Ewy Mathé, Marlena Merling, Qin Ma*, and Bingqiang Liu*, Briefings in bioinformatics, 2021, Mar 22(2):1639-1655. (SCI, IF 13.994)
15. Prediction of protein–protein interactions based on elastic net and deep forest, Bin Yu#*, Cheng Chen#, Xiaolin Wang, Zhaomin Yu, Anjun Ma, Bingqiang Liu. Expert Systems with Applications, 2021 Aug. 15, 176: 114876. (SCI, IF6.954)
16. LncRNA DGCR5 Isoform-1 Silencing Suppresses the Malignant Phenotype of Clear Cell Renal Cell Carcinoma via miR-211-5p/Snail Signal Axis, Guangxin Zhong, Dan Luo, Yijun Fan, Jue Wang, Bingqiang Liu, Zhonghua Xu and Xiang Zhang*, Frontiers in Cell and Developmental Biology, 2021 12 Jul. 9(700029). (SCI, IF6.684)
17. GTB-PPI: Prediction of protein-protein interactions based on L1-regularized logistic regression and gradient tree boosting. Bin Yu*, Cheng Chen, Hongyan Zhou, Bingqiang Liu, Qin Ma*. Genomics, Proteomics & Bioinformatics, 2020, Oct. 18(5): 582-592. (SCI, IF7.691)
18. Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics. Yang Li, Anjun Ma, Ewy A. Mathé, Lang Li, Bingqiang Liu*, and Qin Ma*, Trends in Genetics, 2020, 36(12): 951-966. (SCI, IF 11.821)
19. DNNAce: Prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion. Bin Yu, Yu Zhaomin, Cheng Chen, Anjun Ma, Bingqiang Liu, Baoguang Tian, and Qin Ma, Chemometrics and Intelligent Laboratory Systems, 2020, 200: 103999. (SCI, IF 2.895)
20. IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq. Anjun Ma, Cankun Wang, Yuzhou Chang, Faith H Brennan, Adam McDermaid, Bingqiang Liu, Chi Zhang, Phillip G Popovich, Qin Ma*, Nucleic Acids Research, 2020, 48(W1): W275-W286. (SCI, IF 19.161)
21. QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data. Juan Xie, Anjun Ma, Yu Zhang, Bingqiang Liu, Sha Cao, CankunWang, Jennifer Xu, Chi Zhang,* and Qin Ma*, Bioinformatics, 36(4), 2020, 1143–1149. (SCI, IF 6.937)
22. Identification of lncRNAs-gene interactions in transcription regulation based on co-expression analysis of RNA-seq data. Sijie Lu, Juan Xie, Yang Li, Bin Yu, Qin Ma*, and Bingqiang Liu*, Mathematical Biosciences and Engineering, 2019, 16(6), 7112-7125. (SCI, IF 2.194)
23. Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework. Jinyu Yang, Anjun Ma, Adam D. Hoppe, Cankun Wang, Yang Li, Chi Zhang, Yan Wang, Bingqiang Liu*, and Qin Ma*, Nucleic Acids Research, 2019, 47(15), 7809–7824. (SCI, IF 19.161)
24. MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome. Anjun Ma, Minxuan Sun, Adam McDermaid, Bingqiang Liu, Qin Ma*, Bioinformatics, 2019, 35(21): 4474–4477. (SCI, IF 5.61)
25. Interpretation of differential gene expression results of RNA-seq data: review and integration. Adam McDermaid, Brandon Monier, Jing Zhao, Bingqiang Liu, Qin Ma*, Briefings in bioinformatics, 2019, 20(6): 2044-2054. (SCI, IF 13.994)
26. rSeqTU-a machine-learning based R package for prediction of bacterial transcription units. Sheng-Yong Niu, Bingqiang Liu, Qin Ma* and Wen-Chi Chou*, Frontiers in Genetics, 2019, 10: 374. (SCI, IF 4.599)
27. Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique. Xiaoying Wang#, Bin Yu#*, Cheng Chen, Anjun Ma, Bingqiang Liu, Qin Ma*, Bioinformatics, 2019, 35(14): 2395–2402. (SCI, IF 6.937)
28. MiMod: A New Algorithm for Mining Biological Network Modules. Yang Li, Bingqiang Liu, Jing Li, Guojun Li*, IEEE Access, 2019, 7, 2909946. (SCI, IF 4.199)
29. Computational prediction of sigma-54 promoters in bacterial genomes by integrating motif finding and machine learning strategies. Bingqiang Liu, Ling Han, Xiangrong Liu, Jichang Wu, Qin Ma*, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019. 16(4): 1211–1218. (SCI, IF 3.702)
30. An algorithmic perspective of de-novo cis-regulatory motif finding based on ChIP-seq data. Bingqiang Liu, Jinyu Yang, Yang Li, Adam McDermaid, Qin Ma*, Briefings in Bioinformatics, 2018, 19(5): 1069-1081. (SCI, IF 13.994)
National funding
1. 2022-09, Algorithms design in Cell type specific regulatory mechanism inference from single-cell multi-omics data, National Natural Science Foundation of China, General Program, PI;
2. 2020-12, Mathematic models and algorithms in precision medicine of breast cancer, National Key Research and Development Program of China, PI;
3. 2019-08, Algorithm design for biological and network data mining based on graph model and combinatorial optimization, National Natural Science Foundation of China, Key Program, co-I；
4. 2018-11, The key techniques and application demonstrations in ML-based intelligent innovation methods, The Innovation Method Fund of China (Ministry of Science and Technology of China), PI of sub-program.
5. 2017-08, Cis-regulatory motif prediction and analyses based on next generation sequencing data, National Natural Science Foundation of China, General Program, PI;
6. 2014-08, Development of techniques and algorithms in transcriptome assembly using high throughput RNA-seq data, National Natural Science Foundation of China, Key Program, co-I；
7. 2013-08, Combining ChIP-seq with phylogenetic information to identify cis regulatory motifs, National Natural Science Foundation of China, Youth Program, PI;
8. 2012-10, De novo Assembly of transcriptome with alternative splicing from RNA-Seq data, National Natural Science Foundation of China, General Program, Co-PI；
9. 2010-08, Prediction of Prokaryotic TFBSs and its application, National Natural Science Foundation of China, General Program, Co-I;
