2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于多智能体强化学习的乳腺癌致病基因预测

刘健 顾扬 程玉虎 王雪松

刘健, 顾扬, 程玉虎, 王雪松. 基于多智能体强化学习的乳腺癌致病基因预测. 自动化学报, 2022, 48(5): 1246−1258 doi: 10.16383/j.aas.c210583
引用本文: 刘健, 顾扬, 程玉虎, 王雪松. 基于多智能体强化学习的乳腺癌致病基因预测. 自动化学报, 2022, 48(5): 1246−1258 doi: 10.16383/j.aas.c210583
Liu Jian, Gu Yang, Cheng Yu-Hu, Wang Xue-Song. Prediction of breast cancer pathogenic genes based on multi-agent reinforcement learning. Acta Automatica Sinica, 2022, 48(5): 1246−1258 doi: 10.16383/j.aas.c210583
Citation: Liu Jian, Gu Yang, Cheng Yu-Hu, Wang Xue-Song. Prediction of breast cancer pathogenic genes based on multi-agent reinforcement learning. Acta Automatica Sinica, 2022, 48(5): 1246−1258 doi: 10.16383/j.aas.c210583

基于多智能体强化学习的乳腺癌致病基因预测

doi: 10.16383/j.aas.c210583
基金项目: 国家自然科学基金(61906198, 61976215, 62176259), 江苏省自然科学基金(BK20190622)资助
详细信息
    作者简介:

    刘健:中国矿业大学讲师. 2018年获中国矿业大学博士学位. 主要研究方向为机器学习和生物信息学. E-mail: liujiansqjxt@126.com

    顾扬:中国矿业大学博士研究生. 2016年获中国矿业大学学士学位. 主要研究方向为深度强化学习. E-mail: guyang@cumt.edu.cn

    程玉虎:中国矿业大学教授. 2005年获中国科学院自动化研究所博士学位. 主要研究方向为机器学习和智能系统. E-mail: chengyuhu@163.com

    王雪松:中国矿业大学教授. 2002年获中国矿业大学博士学位. 主要研究方向为机器学习和模式识别. 本文通信作者. E-mail: wangxuesongcumt@163.com

Prediction of Breast Cancer Pathogenic Genes Based on Multi-agent Reinforcement Learning

Funds: Supported by National Natural Science Foundation of China (61906198, 61976215, 62176259), Natural Science Foundation of Jiangsu Province (BK20190622)
More Information
    Author Bio:

    LIU Jian Lecturer at China University of Mining and Technology. He received his Ph.D. degree from China University of Mining and Technology in 2018. His research interest covers machine learning and bioinformatics

    GU Yang Ph.D. candidate at China University of Mining and Technology. He received his bachelor degree from China University of Mining and Technology in 2016. His main research interest is deep reinforcement learning

    CHENG Yu-Hu Professor at China University of Mining and Technology. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. His research interest covers machine learning and intelligent system

    WANG Xue-Song Professor at China University of Mining and Technology. She received her Ph.D. degree from China University of Mining and Technology in 2002. Her research interest covers machine learning and pattern recognition. Corresponding author of this paper

  • 摘要: 通过分析基因突变过程, 提出利用强化学习对癌症患者由正常状态至患病状态的过程进行推断, 发现导致患者死亡的关键基因突变. 首先, 将基因视为智能体, 基于乳腺癌突变数据设计多智能体强化学习环境; 其次, 为保证智能体探索到与专家策略相同的策略和满足更多智能体快速学习, 根据演示学习理论, 分别提出两种多智能体深度Q网络: 基于行为克隆的多智能体深度Q网络和基于预训练记忆的多智能体深度Q网络; 最后, 根据训练得到的多智能体深度Q网络进行基因排序, 实现致病基因预测. 实验结果表明, 提出的多智能体强化学习方法能够挖掘出与乳腺癌发生、发展过程密切相关的致病基因.
  • 图  1  乳腺癌突变数据

    Fig.  1  Breast cancer mutation data

    图  2  多智能体强化学习框架(以第k个智能体为例)

    Fig.  2  Multi-agent reinforcement learning framework (Take the k-th agent as an example)

    图  3  $ N = 188$时, BCDQN在53个死亡状态上的回报值

    Fig.  3  The rewards of BCDQN at 53 death states under the condition of $ N = 188$

    图  4  $ N = 188$时, BCDQN在53个死亡状态上的完成任务情况

    Fig.  4  The task completion status of BCDQN at 53 death states under the condition of $ N = 188$

    图  5  $ N = 420$时, PMDQN在81个死亡状态上的回报值

    Fig.  5  The rewards of PMDQN at 81 death states under the condition of $ N = 420$

    图  6  $ N = 420$时, PMDQN在81个死亡状态上的完成任务情况

    Fig.  6  The task completion status of PMDQN at 81 death states under the condition of $ N = 420$

    图  7  $ N = 188$时, BCDQN预测的前10个致病基因的富集分析圈图

    Fig.  7  The enrichment analysis circle diagram of the top 10 pathogenic genes predicted by BCDQN under the condition of $ N = 188$

    图  8  $ N = 420$时, PMDQN预测的前10个致病基因的富集分析圈图

    Fig.  8  The enrichment analysis circle diagram of the top 10 pathogenic genes predicted by PMDQN under the condition of $ N = 420$

    表  1  BCDQN和PMDQN预测的前10个致病基因

    Table  1  Top 10 pathogenic genes predicted by BCDQN and PMDQN

    序号BCDQNPMDQN
    1TP53TP53
    2FAM91A1PIK3CA
    3TNFRSF11BTG
    4KCNQ3HHLA1
    5MYCASAP1
    6COL14A1 CASC8
    7CCDC26SNORA12
    8CCN3MYC
    9PVT1PVT1
    10DSCC1RN7SL329
    下载: 导出CSV
  • [1] Wisesty U N, Mengko T R, Purwarianti A. Gene mutation detection for breast cancer disease: A review. IOP Conference Series: Materials Science and Engineering, 2020, 830(3): 032051 doi: 10.1088/1757-899X/830/3/032051
    [2] Kohler S, Bauer S, Horn D, Robinson P N. Walking the interactome for prioritization of candidate pathogenic genes. The American Journal of Human Genetics, 2008, 82(4): 949-958 doi: 10.1016/j.ajhg.2008.02.013
    [3] Xu B, Liu Y, Yu S, Wang L, DOng J, Lin H, et al. A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network. BMC Medical Genomics, 2019, 12(Suppl 10): 188
    [4] Han P, Yang P, Zhao P, Shang s, Liu Y, Zhou J, et al. GCN-MF: Disease-gene association identification by graph convolutional networks and matrix factorization. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Anchorage, AK, USA: ACM, 2019. 705−713
    [5] Nagarajan N, Dhillon I S. Inductive matrix completion for predicting gene–disease associations. Bioinformatics, 2014, 30(12): i60-i68 doi: 10.1093/bioinformatics/btu269
    [6] Sahu B, Mishra D. A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Engineering, 2012, 38: 27-31 doi: 10.1016/j.proeng.2012.06.005
    [7] Malar B, Nadarajan R, Thangam G J. A hybrid isotonic separation training algorithm with correlation-based isotonic feature selection for binary classification. Knowledge and Information Systems, 2019, 59(3): 651-683 doi: 10.1007/s10115-018-1226-6
    [8] AliazKovic E, Subasi A. Breast cancer diagnosis using ga feature selection and rotation forest. Neural Computing and Applications, 2017, 28(4): 753-763 doi: 10.1007/s00521-015-2103-9
    [9] Sangaiah I, Kumar A V A. Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: Application to breast cancer prediction. Cluster Computing, 2019, 22(3): 6899-6906
    [10] Alomari O A, Khader A T, Al-Betar M A, Alyasseri Z A A. A hybrid filter-wrapper gene selection method for cancer classification. In: Proceedings of the 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS). Porto, Portugal: IEEE, 2018. 113−118
    [11] Alzubaidi A, Cosma G, Brown D, Pockley A G. Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information. In: Proceedings of 2016 International Conference on Interactive Technologies and Games (ITAG). Nottingham, UK: IEEE, 2016. 70−76
    [12] Hamim M, Moudden I E, Moutachaouik H, Mustapha H. Gene selection for cancer classification: A new hybrid filter-C5.0 approach for breast cancer risk prediction. Advances in Science, Technology and Engineering Systems Journal, 2021, 6(1): 871-878 doi: 10.25046/aj060196
    [13] Liu J, Su R, Zhang J, Wei L. Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network. Briefings in Bioinformatics, DOI: 10.1093/bib/bbaa395
    [14] Zhao Q, Zhang Y. Reverse construction and analysis of gene logical network based on information entropy. International Journal of Pattern Recognition and Artificial Intelligence, 2020, 34(2): 2059004 doi: 10.1142/S0218001420590041
    [15] Sutton R S, Barto A G. Reinforcement learning. A Bradford Book, 1998, 15(7): 665-685
    [16] 庞文砚, 范家璐, 姜艺, Lewis Frank Leroy. 基于强化学习的部分线性离散时间系统的最优输出调节. 自动化学报, DOI: 10.16383/j.aas.c190853

    Pang Wen-Yan, Fan Jia-Lu, Jiang Yi, Lewis Frank Leroy. Optimal output regulation of partially linear discrete-time systems using reinforcement learning. Acta Automatica Sinica10.16383/j.aas.c190853" target="_blank">, DOI: 10.16383/j.aas.c190853
    [17] 施伟, 冯旸赫, 程光权, 黄红蓝, 黄金才, 刘忠, 等. 基于深度强化学习的多机协同空战方法研究. 自动化学报, 2021, 47(7): 1610−1623

    Shi Wei, Feng Yang-He, Cheng Guang-Quan, Huang Hong-Lan, Huang Jin-Cai, Liu Zhong, et al. Research on multi-aircraft cooperative air combat method based on deep reinforcement learning. Acta Automatica Sinica, 2021, 47(7): 1610−1623
    [18] Wu T, Zhou P, Wang B, Li A, Tang X, Xu Z, et al. Joint traffic control and multi-channel reassignment for core backbone network in sdn-iot: A multi-agent deep reinforcement learning approach. IEEE Transactions on Network Science and Engineering, 2021, 8(1): 231-245 doi: 10.1109/TNSE.2020.3036456
    [19] Volodymyr M, Koray K, David S, Rusu A A, Joel V, Bellemare M G, et al. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540): 529-533 doi: 10.1038/nature14236
    [20] Martinez D, Alenya G, Torras, C. Relational reinforcement learning with guided demonstrations. Artificial Intelligence, 2017, 247: 295-312 doi: 10.1016/j.artint.2015.02.006
    [21] Torabi F, Warnell G, Stone P. Behavioral cloning from observation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Stockholm, Sweden: IJCAI, 2018. 4950−4957
    [22] Silwal-Pandit L, Vollan H K M, Chin S F, Rueda O M, McKinney S, Osako T, et al. TP53 mutation spectrum in breast cancer is subtype specific and has distinct prognostic relevance. Clinical Cancer Research, 2014, 20(13): 3569-3580 doi: 10.1158/1078-0432.CCR-13-2943
    [23] Funda M B, Zheng X, Shariati M, Damodaran S, Wathoo C, Brusco L, et al. Survival outcomes by TP53 mutation status in metastatic breast cancer. JCO Precision Oncology, 2018, 2: 1-15
    [24] Han B, Yu J, Jia S, Liu X, Liang X, Li H. Prognostic value of the TP53 mutation location in metastatic breast cancer as detected by next-generation sequencing. Cancer Management and Research, 2021, 13: 3303-3316 doi: 10.2147/CMAR.S298729
    [25] Xu J, Chen Y, Olopade O I. MYC and breast cancer. Genes and Cancer, 2010, 1(6): 629-640 doi: 10.1177/1947601910378691
    [26] Terunuma A, Putluri N, Mishra P, Mathe E A, Ambs S. MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis. Journal of Clinical Investigation, 2014, 124(1): 398-412 doi: 10.1172/JCI71180
    [27] Camarda R, Zhou A Y, Kohnz R A, Balakrishnan S, Mahieu C, Anderton B, et al. Inhibition of fatty acid oxidation as a therapy for myc-overexpressing triple-negative breast cancer. Nature Medicine, 2016, 22(4): 427-432 doi: 10.1038/nm.4055
    [28] Cho S W, Xu J, Sun R, Mumbach M R, Carter A C, Chen Y G, et al. Promoter of LncRNA gene PVT1 is a tumor-suppressor DNA boundary element. Cell, 2018, 173(6): 1398-1412 doi: 10.1016/j.cell.2018.03.068
    [29] Tang J, Li Y, Sang Y, Yu B, Lv D, Zhang W, et al. LncRNA PVT1 regulates triple-negative breast cancer through KLF5/Beta-catenin signaling. Oncogene, 2018, 37(34): 4723-4734 doi: 10.1038/s41388-018-0310-4
    [30] Wang Y, Zhou J, Wang Z, Wang P, Li S. Upregulation of SOX2 activated LncRNA PVT1 expression promotes breast cancer cell growth and invasion. Biochemical and Biophysical Research Communications, 2017, 493(1): 429-436 doi: 10.1016/j.bbrc.2017.09.005
    [31] Anindya B. Abstract IA13: It takes two to tango: The PVT1-MYC alliance in human cancer. Cancer Research, 2016, 76(6): IA13
    [32] Roychowdhury A, Samadder S, Das P, Mazumder D I, Chatterjee A, Addya S, et al. Deregulation of H19 is associated with cervical carcinoma. Genomics, 2019, 112(1): 961-970
    [33] Li Z, Teng J, Jia Z, Zhang G, Ai X. The long non-coding RNA PCAL7 promotes prostate cancer by strengthening androgen receptor signaling. Journal of Clinical Laboratory Analysis, 2021, 35(2): e23645
  • 加载中
图(8) / 表(1)
计量
  • 文章访问数:  263
  • HTML全文浏览量:  185
  • PDF下载量:  80
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-06-27
  • 录用日期:  2021-11-26
  • 网络出版日期:  2022-02-05
  • 刊出日期:  2022-05-13

目录

    /

    返回文章
    返回