王峻
Professor
Visit:
Personal Information:
  • Name (Pinyin):
    Wang Jun
  • E-Mail:
    kingjun@sdu.edu.cn
  • Date of Employment:
    2020-06-12
  • School/Department:
    School of software, Joint SDU-NTU for Artificial Intelligence Research(C-FAIR)
  • Administrative Position:
    Professor
  • Education Level:
    With Certificate of Graduation for Doctorate Study
  • Business Address:
    1500 ShunHua Road, High Tech Industrial Development Zone, Jinan, China 250101
  • Gender:
    Female
  • Contact Information:
    kingjun@sdu.edu.cn; wjkingjun@gmail.com
  • Degree:
    Doctoral Degree in Engineering
  • Status:
    Employed
  • Alma Mater:
    Harbin Institute of Technology
  • Supervisor of Doctorate Candidates
  • Supervisor of Master's Candidates
Discipline:
Other Majors of Software Engineering;
Honors and Titles:

2023-08-31    山东计算机学会自然科学一等奖;
2019-09-18    重庆市科技进步三等奖;
2020-07-31    重庆市自然科学三等奖;
Biography

Professor, ACM/IEEE Member, CCF Senior Member

Expertise: 

Bioinformatics (Biomarker&Genetic interaction identification, Genetic association analysis on multi-omics)

Computational Systems Biology (Pathway discovery and analysis, Biological network construction and analysis)

Artificial Intelligence for Science (Causality learning&Clustering&Network modeling and analysis for diseases analysis and plant breeding)


Selected Papers:

Full list

Journal Paper:

[1]. Jiaxuan Liang, Jun Wang*, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang, Maozu Guo. Gradient-based Local Causal Structure Learning. IEEE Transactions on Cybernetics (CCF B, SCI, IF=19.118) ,  2024, 54(1): 486-495.

[2]. Jun Wang, Xing Wang, Guoxian Yu*, Carlotta Domeniconi, Zhiwen Yu, Zili Zhang. Discovering Multiple Co-Clusterings with Matrix Factorization, IEEE Transactions on Cybernetics (CCF B,SCI, IF=19.118), 2021, 51(7): 3576-3587.

[3]. Guoxian Yu, Yuehui Wang, Jun Wang*, Carlotta Domeniconi, Maozu Guo, Xiangliang Zhang. Attributed Heterogeneous Network Fusion via Collaborative Matrix Tri-factorization, Information Fusion (CCF B,SCI, IF=18.6), 2020, 63: 153-165.

[4]. Yunfeng Zhao, Guoxian Yu, Jun Wang*, Carlotta Domeniconi, Maozu Guo, Xiangliang Zhang, Lizhen Cui. Personalized Federated Few-Shot Learning. IEEE Transactions on Neural Networks and Learning Systems (CCF B, SCIIF=14.255) , 2024, 35(2): 2534-2544.

[5]. Guoxian Yu, Yuying Xing+, Jun Wang*, Carlotta Domeniconi, Xiangliang Zhang. Multi-view Multi-instance Multi-label Active Learning. IEEE Transactions on Neural Networks and Learning Systems (CCF B, SCIIF=14.255), 2022, 33(9): 4311-4321.

[6]. Guoxian Yu, Xuanwu Liu, Jun Wang*, Carlotta Domeniconi, Xiangliang Zhang. Flexible Cross-Modal Hashing, IEEE Transactions on Neural Networks and Learning Systems (CCF B, SCIIF=14.255), 2022, 33(1): 304-314. 

[7]. Guoxian Yu, Jinzheng Tu, Jun Wang*, Carlotta Domeniconi, Xiangliang Zhang. Active Multi-Label Crowd Consensus, IEEE Transactions on Neural Networks and Learning Systems (CCF B, SCIIF=14.255), 2021, 32(4): 1448-1459.

[8]. Jiaxuan Liang, Jun Wang*, Guoxian Yu, Wei Guo, Carlotta Domeniconi, Maozu Guo. Directed Acyclic Graph Learning on Attributed Heterogeneous Network. IEEE Transactions on Knowledge and Data Engineering (CCF A, SCI, IF=9.235), 2023,35(9): 10845-10856.

[9]. Guoxian Yu, Xia Chen, Carlotta Domeniconi, Jun Wang*, Zhao Li, Zili Zhang, Xiangliang Zhang. CMAL: Cost-effective Multi-label Active Learning by Querying Subexamples, IEEE Transactions on Knowledge and Data Engineering (CCF A, SCI, IF=9.235), 2022, 34(5): 2091-2105.

[10].  Haojiang Tan, Maozu Guo, Jian Chen, Jun Wang*, Guoxian Yu*. HetFCM: Functional co-module discovery by heterogeneous network co-clustering. Nucleic Acids Research (SCI, IF=14.9) , 2024, 52, e62.

[11]. Guoxian Yu, Liangrui Ren, Jun Wang*, Carlotta Domeniconi, Xiangliang Zhang. Multiple Clusterings: Recent Advances and Perspectives. Computer Science Review, (SCI, IF=12.9),  2024, 52: 100621.

[12]. Liangrui Ren, Jun Wang*, Zhao Li, Qingzhong Li, Guoxian Yu. scMCs: a framework for single cell multi-omics data integration and multiple clusterings. Bioinformatics (CCF B, SCI, IF=6.931), 2023, 39(4): btad133.

[13]. Xingze Wang, Guoxian Yu*, Jun Wang*, Azlan Mohd Zain, and Wei Guo. Lung Cancer Subtype Diagnosis using Weakly-paired Multi-omics Data. Bioinformatics (CCF B, SCI, IF=6.931), 2022, 38(22):5092-5099. 

[14]. Guoxian Yu, Keyao Wang, Carlotta Domeniconi, Maozu Guo*, Jun Wang*. Isoform function prediction based on bi-random walks on a heterogeneous network, Bioinformatics (CCF B, SCI, IF=6.931), 2020, 36(1): 303-310.

[15]. Jun Wang, Xi Chen, Zhengtian Wu, Maozu Guo and Guoxian Yu*. Cooperative driver pathways discovery by multiplex network embedding. Briefings in Bioinformatics (CCF B, SCI, IF=13.994), 2023, 24(3): bbad112.

[16]. Zimo Huang, Jun Wang*, Xudong Lu, Azlan Mohd Zain, Guoxian Yu. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial n1etwork. Briefings in Bioinformatics (CCF B, IF=13.994), 2023, bbad040.

[17]. Zimo Huang, Jun Wang*, Zhongmin Yan, Maozu Guo. Differentially Expressed Genes prediction by multiple self-attention on epigenetics data. Briefings in Bioinformatics, (CCF B, SCI, IF=13.994), 2022, 23(4): bbac117.

[18]. Xin Wang, Xia Cao, Yuantao Feng, Maozu Guo, Guoxian Yu, Jun Wang*. ELSSI: parallel SNP-SNP interactions detection by ensemble multi-type detectors. Briefings in Bioinformatics (CCF B, SCI, IF=13.994), 2022, 23(4): bbac213. 

[19]. Jun Wang, Ziying Yang, Carlotta Domeniconi, Xiangliang Zhang, Guoxian Yu*. Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs, and pathways, Briefings in Bioinformatics (CCF B, SCI, IF=13.994), 2021, 22(2): 1984-1999.

[20]. Zimo Huang, Jun Wang*, Zhongmin Yan, Lin Wan, Maozu Guo. Differential gene expression prediction by ensemble deep networks on Histone Modification data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2023, 20(1): 340-351. 

[21]. Jun Wang*, Huiling Zhang, Wei Ren, Maozu Guo, Guoxian Yu. EpiMC: Detecting Epistatic Interactions using Multiple Clusterings. IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2022, 19(1): 243-254. 

[22]. Jun Wang, Long Zhang, An Zeng, Dawen Xia, Jiantao Yu, Guoxian Yu*. DeepIII: Predicting isoform-isoform interactions by deep neural networks and data fusion, IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2022, 19(4): 2177-2187.

[23]. Guoxian Yu, Qiuyue Huang+, Xiangliang Zhang, Maozu Guo*, Jun Wang*. Tissue Specificity based Isoform Function Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2022, 19(5): 3048-3059.

[24]. Guoxian Yu, Yeqian Yang+, Yangyang Yan+, Maozu Guo, Xiangliang Zhang, Jun Wang*. DeepIDA: predicting isoform-disease associations by data fusion and deep neural networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2022, 19(4): 2166-2176.

[25]. Ziying Yang, Guoxian Yu, Maozu Guo, Jiantao Yu, Xiangliang Zhang, Jun Wang*. CDPath: Cooperative driver pathways discovery using integer linear programming and Markov clustering, IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2021, 18(4): 1439-1450.

[26]. Guoxian Yu, Keyao Wang, Guangyuan Fu, Maozu Guo, Jun Wang*. NMFGO: Gene function prediction via nonnegative matrix factorization with Gene Ontology, IEEE/ACM Transactions on Computational Biology and Bioinformatics (CCF B, SCI, IF=4.5), 2020, 36(1): 303-310.

[27]. Lin Jiang, Guoxian Yu, Maozu Guo*, Jun Wang*. Feature selection with missing labels based on label compression and local feature correlation, Neurocomputing (CCF C, SCI, IF=6), 2020, 395: 95-106.

[28]. Long Zhang, Guoxian Yu, Dawen Xia, Jun Wang*. Protein-Protein Interactions Prediction based on Ensemble Deep Neural Networks, Neurocomputing (CCF C, SCI, IF=6), 2019, 324: 10-19.

[29]. Qiaoyu Tan, Yanming Yu, Guoxian Yu, Jun Wang*. Semi-supervised multi-label classification using incomplete label information, Neurocomputing (CCF C, SCI, IF=6), 2017, 260: 192-202.

[30]. Jun Wang, Guangjun Yao, Guoxian Yu*. Semi-supervised classification by discriminative regularization, Applied Soft Computing (CCF B, SCI, IF=8.7), 2017, 58: 245-255.

[31]. Xia Cao, Guoxian Yu, Wei Ren, Maozu Guo, Jun Wang*. DualWMDR: detecting epistatic interaction with dual screening and multifactor dimensionality reduction, Human Mutation (SCI, IF=3.9), 2020, 41(3): 719-734.

[32]. Long Zhang, Guoxian Yu, Maozu Guo, Jun Wang*. Predicting protein-protein interactions using high-quality non-interacting pairs, BMC Bioinformatics (CCF C, SCI, IF=3), 2018, 19(S19): 525.



Conference Paper:


[1]. Dezhi Yang, Xintong He, Jun Wang*, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang. Federated Causality Learning with Explainable Adaptive Optimization. 38th AAAI Conference on Artificial Intelligence (AAAI) (CCF A) , 2024, pp. 16308-16315

[2]. Zijun Gao, Jun Wang*, Guoxian Yu, Zhongmin Yan, Carlotta Domeniconi, Jinglin Zhang. Long-tail Cross Modal Hashing. 37th AAAI Conference on Artificial Intelligence (AAAI) (CCF A) , 2023.

[3]. Dezhi Yang, Guoxian Yu, Jun Wang*, Zhengtian Wu, Maozu Guo. Reinforcement Causal Structure Learning on Order Graph. 37th AAAI Conference on Artificial Intelligence (AAAI) (CCF A) , 2023.

[4]. Shaowei Wei, Jun Wang*, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang. Multi-View Multiple Clusterings using Deep Matrix Factorization, 34rd AAAI Conference on Artificial Intelligence (AAAI) (CCF A), 2020, pp. 6348-6355.

[5]. Xing Wang, Jun Wang*, Carlotta Domeniconi, Guoxian Yu, Guoqiang Xiao, Maozu Guo. Multiple Independent Subspace Clusterings, 33rd AAAI Conference on Artificial Intelligence (AAAI) (CCF A), 2019, pp. 5353-5360.

[6]. Shaowei Wei, Jun Wang*, Guoxian Yu, Carlotta Domeniconi, and Xiangliang Zhang. Deep Incomplete Multi-View Multiple Clusterings, IEEE International Conference on Data Mining (ICDM) (CCF B), 2020, pp. 651-660.

[7]. Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang*, Zhiwen Yu, and Zili Zhang. Multiple Co-Clusterings, International Conference on Data Mining (ICDM) (CCF B), 2018, pp. 1308-1313.

[8]. Dezhi Yang, Guoxian Yu, Jun Wang*, Zhongmin Yan, Maozu Guo. Causal Discovery by Graph Attention Reinforcement Learning. SIAM International Conference on Data Mining (SDM) (CCF B) , 2023.

[9]. Shixin Yao, Guoxian Yu, Xing Wang, Jun Wang*, Carlotta Domeniconi, Maozu Guo. Discovering Multiple Co-Clusterings in Subspaces, SIAM Conference on Data Mining (SDM) (CCF B), 2019, pp. 423-431.

[10]. Haojiang Tan, Jun Wang*, Guoxian Yu, Wei Guo, and Maozu Guo. Phenotype Prediction by Heterogeneous Molecular Network Embedding. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B) , 2022. 

[11]. Xin Wang, Jun Wang*, Guoxian Yu, Beibei Xin, and Maozu Guo. Maize Epistasis Detection by Multi-class Quantitative Multifactor Dimensionality Reduction. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B) , 2021, pp. 314-319. 

[12]. Haojiang Tan, Sichao Qiu, Jun Wang*, Guoxian Yu, Wei Guo, and Maozu Guo. Genome-Phenome Association Prediction by Deep Factorizing Heterogeneous Molecular Network. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B) , 2021, pp. 211-216. 

[13]. Huiling Zhang, Jun Wang*, Guoxian Yu, Lizhen Cui, Maozu Guo. Epistasis Detection using Heterogeneous Bio-molecular Network, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B), 2020, pp. 194-199.

[14]. Sufang Li, Jun Wang*, Maozu Guo, and Xiangliang Zhang. Cooperative Driver Pathway Discovery byHierarchical Clustering and Link Prediction., IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B), 2020, pp. 194-199.

[15]. Ziying Yang, Guoxian Yu, Jiantao Yu, Maozu Guo, Jun Wang*. CoPath: discovering cooperative driver pathways using greedy mutual exclusivity and bi-clustering, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B), 2019, pp. 165-170.

[16].Guoxian Yu, Yuehui Wang, Jun Wang*, Guangyuan Fu, Maozu Guo, and Carlotta Domeniconi. Weighted matrix factorization based data fusion for predicting lncRNA-disease associations, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (CCF B), 2018, pp. 572-577.


Fundings:

Ongoing:

2020-08, Natural Science Fund of China

2022-09, Natural Science Fund of China

Finished:

2018-08, Natural Science Fund of China

2011-08, Natural Science Fund of China

2016-06, Natural Science Fund of Chongqing

2019-11, CAAI-Huawei MindSpore Open Fund


Professional Services:

Associate Editor: 

Journal of Beijing University of Civil Engineering and Architecture

Computational Biomedicine

Member:

CCF Bioinformatics, CCF Artificial Intelligence and Pattern Recognition

CAAI Bioinformatics, CAAI Machine Learning

IEEE, China Computer Federation(CCF)

SPC/PC

IJCAI19-24, AAAI19-24, PAKDD19-24, BIBM 19-24, ISBRA16-20,  etc.

Reviewer

Nature Communication, Genome Biology, Nucleic Acids Research,

Bioinformatics, Briefings in Bioinformatics, Current Bioinformatics, IEEE TNNLS,

TKDE, TCYB, TII, Information Fusion, Neural Networks, Neurocomputing, etc.





Education
  • 2006-9 — 2010-7
    Harbin Institute of Technology
    Computer Technology
    Doctor
  • 2004-9 — 2006-7
    Harbin Institute of Technology
    Computer Technology
    Master's Degree
  • 2000-9 — 2004-7
    Harbin Institute of Technology
    Computer Technology
    Bachelor
Publication
Papers

1. 梁嘉旋. Multi-Granularity Causal Structure Learning .2024,38 (12):13727-13735

2. 梁嘉旋. Gradient-based Local Causal Structure Learning .IEEE Transactions on Cybernetics.2024,54 (1):486

3. 赵云峰. Personalized Federated Few-Shot Learning .IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS.2024,35 (2):2534

4. 苏聪. Multi-dimensional Fair Federated Learning .2024

5. 杨德智. Federated Causality Learning with Explainable Adaptive Optimization .2024

6. 谭好江. HetFCM: Functional co-module discovery by heterogeneous network co-clustering .Nucleic Acids Research.2024,52 (3)

7. 余国先. Multiple Clusterings: Recent Advances and Perspectives .Computer Science Review.2024 (100621)

8. 苏聪. Causality-based fair multiple decision by response functions .ACM Transactions on Knowledge Discovery from Data.2024,18 (3)

9. 康祥平. Semi-Asynchronous Online Federated Crowdsourcing .2024

10. 梁嘉旋. Directed Acyclic Graph Learning on Attributed Heterogeneous Network .IEEE Transactions on Knowledge and Data Engineering.2023,35 (10):10845

11. 黄子墨. Differential gene expression prediction by ensemble deep networks on Histone Modification data .IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS.2023,20 (1):340

12. 杨德智. Reinforcement Causal Structure Learning on Order Graph .2023

13. 高子俊. Long-tail Cross Modal Hashing .2023

14. 康祥平. Self-paced Annotations of Crowd Workers .Knowledge and Information Systems.2022,64 (00):3235

15. 任良瑞. A Diversified Attention Model for Interpretable Multiple Clusterings .TKDE.2022 (1)

16. 黄子墨. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network .Briefings in Bioinformatics.2023,24 (2):1

17. 杨德智. Causal Discovery by Graph Attention Reinforcement Learning .2023

18. 任良瑞. scMCs: a framework for single cell multi-omics data integration and multiple clusterings .Bioinformatics.2023,39 (4):1

19. 邱思超. Meta multi-instance multi-label learning by heterogeneous network fusion .Information Fusion.2023,94 (00):272

20. 苏聪. A review of causality-based fairness machine learning .Intelligence and Robotics.2022,2 (3):244

21. 王星泽. Lung Cancer Subtype Diagnosis using Weakly-paired Multi-omics Data .Bioinformatics.2022,38 (22):5092

22. 梁嘉旋. Directed Acyclic Graph Learning on Attributed Heterogeneous Network .IEEE Transactions on Knowledge and Data Engineering (TKDE).2023,0 (00):1

23. 王峻. Cooperative driver pathways discovery by multiplex network embedding .Briefings in Bioinformatics.2023,0 (0):1

24. 梁嘉旋. Gradient-based Local Causal Structure Learning .IEEE Transactions on Cybernetics.2023,00 (00):1

25. 刘玄武. Weakly Supervised Cross-Modal Hashing .IEEE TRANSACTIONS ON BIG DATA.2019,8 (2):552

26. 谭好江. Phenotype Prediction by Heterogeneous Molecular Network Embedding .2022

27. 王峻. 基于单细胞数据的癌症协同驱动模块识别方法 .中国科学-信息科学.2022 (00)

28. 王峻. Differential gene expression prediction by ensemble deep networks on Histone Modification data .IEEE/ACM transactions on computational biology and bioinformatics.2021,1 (1):1

29. 王昕. ELSSI: parallel SNP-SNP interactions detection by ensemble multi-type detectors. .Briefings in Bioinformatics .2022,23 (4)

30. 赵云峰. Personalized Federated Few-Shot Learning .IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS.2022

31. 谭好江. Weighted deep factorizing heterogeneous molecular network for genome-phenome association prediction .METHODS.2022,205 :18-28

32. 王峻. EpiMC: Detecting Epistatic Interactions using Multiple Clusterings .IEEE/ACM Transactions on Computational Biology and Bioinformatics.2021,19 (1):243

33. 黄子墨. Differentially expressed genes prediction by multiple self-attention on epigenetics data .Briefings in Bioinformatics.2022

34. 余国先. Crowdsourcing with Self-paced Workers .2021

35. 余国先. Tissue Specificity based Isoform Function Prediction .IEEE/ACM Transactions on Computational Biology and Bioinformatics.2021 (99):1

36. 王峻. Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways .Briefings in Bioinformatics.2021,22 (2):1984

37. 王昕. Maize Epistasis Detection by Multi-class Quantitative Multifactor Dimensionality Reduction .2021

38. 谭好江. Genome-Phenome Association Prediction by Deep Factorizing Heterogeneous Molecular Network .2021

39. 余国先. EpiHNet: Detecting epistasis by heterogeneous molecule network .METHODS.2021 (00)

40. 王峻. CDPath: Cooperative Driver Pathways Discovery Using Integer Linear Programming and Markov Clustering .IEEE/ACM Transactions on Computational Biology and Bioinformatics.2019 (16):1

41. 余国先. CMAL: Cost-effective Multi-label Active Learning by Querying Subexamples .IEEE Transactions on Knowledge and Data Engineering (TKDE).2021,33 (1):1

42. 余国先. Flexible Cross-Modal Hashing .IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS.2020 (32):1

43. 余国先. Partial Multi-Label Learning using Label Compression .INTERNATIONAL CONFERENCE ON DATA MINING, RIO JANEI.2020 (20):1

44. 余国先. CrowdWT: Crowdsourcing via Joint Modeling of Workers and Tasks .ACM Transactions on Knowledge and Data Engineering.2020,15 (1):1

45. 余国先. Partial Multi-label Learning with Label and Feature Collaboration .DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PROCEEDINGS.2020 (25):621

46. 余国先. Attributed heterogeneous network fusion via collaborative matrix tri-factorization .Information Fusion.2020,63 :153

47. 余国先. Co-clustering Ensembles based on Multiple Relevance Measures .IEEE Transactions on Knowledge and Data Engineering (TKDE).2021 (33):1

48. 王峻. Deep Incomplete Multi-View Multiple Clusterings .2020

49. 余国先. Cross-Species Protein Function Prediction with Asynchronous-Random Walk .IEEE/ACM Transactions on Computational Biology and Bioinformatics.2019 (21):1

50. 王峻. Epistasis Detection using Heterogeneous Bio-molecular Network .2020

51. 王峻. Cooperative Driver Pathway Discovery by Hierarchical Clustering and Link Prediction .2020

52. 王峻. Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways .Briefings in Bioinformatics.2020 (00):1

53. 余国先. Multi-view Multi-instance Multi-label Active Learning .IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS.2021 (32)

54. 王峻. Imbalance deep multi-instance learning for predicting isoform-isoform interactions .INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS.2021,36 (6):2797

55. 余国先. DeepIDA: predicting isoform-disease associations by data fusion and deep neural networks .IEEE/ACM Transactions on Computational Biology and Bioinformatics.2021 (1):1

56. 王峻. DeepIII: Predicting isoform-isoform interactions by deep neural networks and data fusion .IEEE/ACM Transactions on Computational Biology and Bioinformatics.2021 (1):1

57. 余国先. Imbalance deep multi-instance learning for predicting isoform-isoform interactions .INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS.2021,36 (6):2797

Patens
Research Group
Title
Intelligent data engineering and Analytics (idea) laboratory
Description of Research Group:
www.sdu-idea.cn
Members
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