｜中心简介
交叉学科大数据研究中心以大数据科学与技术为支撑，解决交叉学科中涉及的大数据分析问题，在大数据与各个学科领域开展交叉研究和协同创新。
｜研究团队
目前研究团队有教授1名、助理教授5名、副研究员2名。
｜研究中心主任
朱泽轩 教授
朱泽轩，深圳大学教授，大数据系统计算技术国家工程实验室副主任，深圳大学计算机与软件学院人工智能系主任，中国数字音视频编解码技术标准工作组基因压缩专题组组长。
｜研究方向
专注于大数据科学与生物学、物理学、化学、神经科学、医学等跨学科研究和协同创新。利用数据挖掘、深度学习、机器学习等方法和算法，结合大规模并行处理、分布式数据库和云计算等技术，解决包括生物组学大数据分析、人工智能辅助药物设计、类脑计算等前沿交叉学科问题及其应用。
｜合作单位
深圳华大基因股份有限公司、华为技术有限公司、深圳市新产业生物医学工程股份有限公司、菲鹏生物股份有限公司。
｜研究成果
科研成果
1. Q. Zhou, F. Ji, D. Lin, X. Liu, Z. Zhu*, and J. Ruan*, KSNP: a fast DBG-based haplotyping tool approaching data-in time cost, Nature Communications, vol. 15, article no. 3126, 2024
2. Q. Yu, Q. Lin, J. Ji, W. Zhou, S. He*, Z. Zhu*, and K. C. Tan, A survey on evolutionary computation based drug discovery, IEEE Transactions on Evolutionary Computation，2024（accepted）
3. T. Dai, M. Ya, J. Li, X. Zhang, S.-T. Xia, and Z. Zhu*, CFGN: A lightweight context feature guided network for image super-resolution, IEEE Transactions on Emerging Topics in Computational Intelligence, 2023 (accepted)
4. Z. Liu, G. Li, H. Zhang, Z. Liang, and Z. Zhu*, Multifactorial evolutionary algorithm based on diffusion gradient descent, IEEE Transactions on Cybernetics, 2023 (accepted)
5. L. Liu, W. Yuan, Z. Liang, X. Ma, and Z. Zhu*, Construction of polar codes based on memetic algorithm, IEEE Transactions on Emerging Topics in Computational Intelligence, 2022 (accepted)
6. Z. Liang, Y. Zhu, X. Wang, Z. Li, and Z. Zhu*，Evolutionary multitasking for multi-objective optimization based on generative strategies, IEEE Transactions on Evolutionary Computation, 2022 (accepted)
7. X. Ma, Z. Huang, X. Li, Y. Qi, L. Wang, and Z. Zhu*, Multi-objectivization of single-objective optimization in evolutionary computation: A survey, IEEE Transactions on Cybernetics, 2021 (accepted)
8. X. Ma, Z. Huang, X. Li, L. Wang, Y. Qi, and Z. Zhu*, Merged differential grouping for large-scale global optimization, IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1439-1451, 2022.
9. M. Yang, Z.-A Huang, W. Gu, K. Han, W. Pan, X. Yang*, and Z. Zhu*, Prediction of biomarker-disease associations based on graph attention network and text representation, Briefings in Bioinformatics, vol. 23, no. 5, pp. 1-14, 2022
10. S. Xie, T. He, S. He, and Z. Zhu*, CURC: A CUDA-based reference-free read compressor, Bioinformatics, vol. 38, no. 12, pp. 3294-3296, 2022.
11. Z. Liang, W. Liang, Z. Wang, X. Ma, L. Liu*, and Z. Zhu*, Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution, IEEE Transactions on Systems, Man, and Cybernetics - Systems, vol. 52, no. 7, pp. 4457-4469, 2022.
12. X. Ma, J. Yin, A. Zhu, X. Li, Y. Yu, L. Wang, Y. Qi, and Z. Zhu*, Enhanced multifactorial evolutionary algorithm with meme helper-tasks, IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7837-7851, 2022.
13. Z. Liang, H. Dong, C. Liu, W. Liang, and Z. Zhu*, Evolutionary multitasking for multiobjective optimization with subspace alignment and adaptive differential evolution, IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2096-2109, 2022.
14. Z. Liang, T. Wu, X. Ma, Z. Zhu*, and S. Yang, A dynamic multiobjective evolutionary algorithm based on decision variable classification, IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1602-1615, 2022.
15. Z. Liang, X. Xu, L. Liu*, Y. Tu, and Z. Zhu*，Evolutionary many-task optimization based on multisource knowledge transfer, IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 319-333, 2022.
16. X. Ma, Y. Zheng, X. Li, L. Wang, Y. Qi, J. Yang and Z. Zhu*, Improving evolutionary multitasking optimization by leveraging inter-task gene similarity and mirror transformation, IEEE Computational Intelligence Magazine, vol. 16, no. 4, pp.38-51, 2021.
17. Z. Liang, T. Luo, K. Hu, X. Ma, and Z. Zhu*, An indicator-based many-objective evolutionary algorithm with boundary protection, IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4553-2566, 2021.
18. Z. Liang, K. Hu, X. Ma, and Z. Zhu*, A many-objective evolutionary algorithm based on a two-round selection strategy, IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1417-1429, 2021.
19. Z.-A. Huang, J. Zhang, Z. Zhu*, E. Q. Wu, and K. C. Tan*, Identification of autistic risk candidate genes and toxic chemicals via multi-label learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 3971-3984, 2021.
20. Z.-A. Huang, Z. Zhu*, C. Yau, and K. C. Tan*, Identifying autism spectrum disorder from resting-state fMRI using deep belief network, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2847-2861, 2021.
21. Q. Lin, W. Lin, Z. Zhu*, M. Gong, J. Li, and C. A. Coello Coello, Multimodal multi-objective evolutionary optimization with dual clustering in decision and objective spaces, IEEE Transactions on Evolutionary Computation, vol. 25, no. 1, pp. 130-144, 2021.
22. X. Ma, Y. Yu, X. Li, Y. Qi, and Z. Zhu*, A survey of weight vector adjustment methods for decomposition based multi-objective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.24, no.4, pp. 634-649, 2020
23. X. Ma, X. Li, Q. Zhang, K. Tang, Z. Liang, W. Xie, and Z. Zhu*, A survey on cooperative co-evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol. 23, no. 3, pp. 421-441, 2019.
24. R. Guo, Y.-R. Li, S. He, L. Ou-Yang, Y. Sun*, and Z. Zhu*, RepLong - de novo repeat identification using long read sequencing data, Bioinformatics, vol. 34, no. 7, pp. 1099-1107, 2018.
25. X. Ma, Q. Zhang, G. Tian, J. Yang, and Z. Zhu*, On Tchebycheff decomposition approaches for multi-objective evolutionary optimization, IEEE Transactions on Evolutionary Computation, vol. 22, no. 2, pp. 226-244, 2018.
26. Z.-H, You, Z.-A. Huang, Z. Zhu*, G.-Y. Yan, Z.-W. Li, Z. Wen, and X. Chen*, PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction, PLoS Computational Biology, vol. 13, no. 3, artical no. e1005455, 2017.
27. Z.-A. Huang, Z. Wen, Q. Deng, Y. Chu, Y. Sun, and Z. Zhu*,LW-FQZip 2: a parallelized reference-based compression of FASTQ files, BMC Bioinformatics, vol. 18, no. 1, pp. 179:1-179:8, 2017.
28. Z. Zhu, L. Li, Y. Zhang, Y. Yang, and X. Yang, CompMap: a reference-based compression program to speed up read mapping to related reference sequences, Bioinformatics, vol. 31, no. 3, pp. 426-428, 2015.
29. Z. Zhu, Y. Zhang, Z. Ji, S. He, and X. Yang, High-throughput DNA sequence data compression, Briefings in Bioinformatics, vol. 16, no. 1, pp. 1-15, 2015.
30. Y. Zhang, L. Li, Y. Yang, X. Yang, S. He and Z. Zhu*, Light-weight reference-based compression of FASTQ data, BMC Bioinformatics, vol. 16, pp.188, 2015.
31. Z. Zhu, J. Zhou, Z. Ji, and Y.-H. Shi, DNA sequence compression using adaptive particle swarm optimization-based memetic algorithm, IEEE Transactions on Evolutionary Computation, vol. 15, no. 5, pp. 643-558, 2011.
32. Z. Zhu, S. Jia, and Z. Ji, Towards a memetic feature selection paradigm, IEEE Computational Intelligence Magazine, vol. 5, no. 2, pp. 41-53, 2010.
33. Z. Zhu, Y. S. Ong and M. Zurada, Identification of full and partial class relevant genes, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 2, pp. 263-277, 2010.
34. W. Du, H. Jin, C. Yu, G. Yin*. Deep Reinforcement Learning for Bandit Arm Localization. IEEE International Conference on Big Data. 2022: 5243-5252.
35. H. Jin, W. Du, G. Yin*. Approximate Bayesian computation design for phase I clinical trials. Statistical Methods in Medical Research. 2022. 31(12): 2310-2322
36. J. Ji, J. Zhou, Z. Yang, Q. Lin, and C. Coello. AutoDock Koto: A Gradient Boosting Differential Evolution for Molecular Docking. IEEE Transactions on Evolutionary Computation (2022).
37. J. Ji, Y. Tang, L. Ma, J. Li, Q. Lin, Z. Tang, and Y. Todo. Accuracy Versus Simplification in an Approximate Logic Neural Model. IEEE Transactions on Neural Networks and Learning Systems 32, no. 11 (2020): 5194-5207.
38. J. Ji, J. Zhao, Q. Lin, and K. C. Tan. Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model. IEEE Transactions on Cybernetics (2022).
39. J. Ji, M. Dong, Q. Lin, and K. C. Tan. Noninvasive Cuffless Blood Pressure Estimation With Dendritic Neural Regression. IEEE Transactions on Cybernetics (2022).
40. J. Ji, M. Dong, Q. Lin, and K. C. Tan. Forecasting Wind Speed Time Series Via Dendritic Neural Regression. IEEE Computational Intelligence Magazine 16, no. 3 (2021): 50-66.
41. J. Ji, S. Song, Y. Tang, S. Gao, Z. Tang and Y. Todo, Approximate logic neuron model trained by states of matter search algorithm. Knowledge-Based Systems, 163 (2019): 120-130.
42. J. Ji, S. Song, C. Tang, S. Gao, Z. Tang and Y. Todo, An artificial bee colony algorithm search guided by scale-free networks. Information Sciences, 473 (2019): 142-165.
43. J. Ji, S. Gao, J. Cheng, Z. Tang, and Y. Todo, An approximate logic neuron model with a dendritic structure. Neurocomputing 173 (2016): 1775-1783.
44. J. Ji, C. Tang, J. Zhao, Z. Tang, and Y. Todo. A survey on dendritic neuron model: Mechanisms, algorithms and practical applications. Neurocomputing (2022).
45. W. Zhou, Y. Liu, M. Li, Y. Wang, Z. Shen, L. Feng* and Z. Zhu. Dynamic Multi-Objective Optimization Framework With Interactive Evolution for Sequential Recommendation. IEEE Transactions on Emerging Topics in Computational Intelligence. 2023, Early Access: 1-14.
46. W. Zhou, L. Feng*, K. C. Tan, M. Jiang and Y. Liu. Evolutionary Search With Multiview Prediction for Dynamic Multiobjective Optimization. IEEE Transactions on Evolutionary Computation. 2022, 26(5): 911-925.
47. L. Feng*, W. Zhou, W. Liu, Y.-S. Ong and K. C. Tan. Solving Dynamic Multiobjective Problem via Autoencoding Evolutionary Search. IEEE Transactions on Cybernetics. 2022, 52, (5): 2649-2662.
48. W. Zhou, L. Feng, Z. Zhu, K. Liu, C. Chen and Z. Wu. Tracking Moving Optima of Dynamic Multi-objective Problem via Prediction in Objective Space. in IEEE Congress on Evolutionary Computation (CEC), 2020, Glasgow, UK, 1-8.
49. J. Zhang, K. Yan, Q. Chen, and B. Liu*. PreRBP-TL: prediction of species-specific RNA-binding proteins based on transfer learning. Bioinformatics. 2022, 38(8): 2135–2143.
50. J. Zhang, Q. Chen, and B. Liu*. NCBRPred: predicting nucleic acid binding residues in proteins based on multilabel learning. Briefings in Bioinformatics. 2021, 22(5): bbaa397.
51. J. Zhang, Q. Chen, and B. Liu*. iDRBP_MMC: Identifying DNA-Binding Proteins and RNA-Binding Proteins Based on Multi-Label Learning Model and Motif-Based Convolutional Neural Network. Journal of Molecular Biology. 2020, 432(22): 5860-5875.
52. J. Zhang, Q. Chen, and B. Liu*. DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2021, 18(4): 1451-1463.
53. J. Zhang and B. Liu*. PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation. International Journal of Molecular Sciences. 2017, 18(9): 1856.
54. J. Zhang and B. Liu*. A review on the recent developments of sequence-based protein feature extraction methods. Current Bioinformatics. 2019, 14(3): 190-199.
55. J. Zhang and B. Liu*. Identification of DNA-binding proteins via a voting strategy. Current Proteomics, 2018, 15(5): 363-373.
承担项目
1. 国家重点研发计划课题：DNA存储实时编解码关键技术研发，2022-2025，2022YFF1202104
2. 国家自然科学基金面上项目：基于自组装参考基因组的高通量长读测序数据压缩和比对集成研究，2019-2022，61871272
3. 国家自然科学基金面上项目：基于高通量RNA-Seq和多目标协同演化模因计算的疾病模块识别研究，2015-2018，61471246
4. 国家自然科学基金-青年科学基金项目：树突状神经网络的学习算法及其在疾病诊断和监测的应用研究，2022 -2024，62106151
5. 广东省区域联合基金-青年基金项目：基于树突神经网络的计算机辅助医疗诊断研究，2020 -2022，2019A1515111139
6. 深圳市科技计划项目-面上项目：脑启发式神经网络建模及其在医疗数据挖掘上的应用研究，2022-2025，JCYJ20220531101614031
7. 国家重点研发计划-子课题：高通量基因组数据智能高效压缩与传输及自主国家标准制定，2020-2022
8. 深圳市科技创新委项目-2019年后基础研究（面上项目）：基于模因自动机的超多任务演化优化研究及应用，2020-2023，JCYJ20190808173617147
9. 深圳市科技创新委项目-基础研究（2001至2018）：数据驱动的智能动态物流配送路径规划研究，2017-2019，JCYJ20170302154328155
10. 广东省科技计划项目-广东省自然科学基金面上项目：基于语义理解的核酸结合蛋白及其作用位点识别研究，2024-2026，2024A1515011681
11. 国家自然科学基金项目-青年科学基金项目：基于结构语言模型的蛋白质表征及功能预测方法研究，2024-2026，62302311
12. 国家自然科学基金项目-青年科学基金项目：硬件感知模型压缩的多目标方法，2022-2024，62106098
