王扬帆

发布者:杨光发布时间:2022-07-14浏览次数:12383







姓名:王扬帆,男,联系:yfwang@ouc.edu.cn

职务: 教授,博士生导师

工作单位:中国海洋大学海洋生命学院,海洋生物遗传与育种教育部重点实验室,包振民院士团队核心成员

研究方向:以基因芯片设计,全基因组选择为主的分子育种研究

产研结合:国际著名基因芯片分子育种公司——纽勤(Neogen)生物科技(美国)有限公司工作六个月,进行育种基因芯片设计及全基因组选择研究及应用,为公司新产品上市做出成绩。

教育经历:2007.06-2012.03,中国海洋大学、英国邓迪大学计算生物专业,博士、博士后

工作经历:2012.03-至今,中国海洋大学,经历讲师、副教授、教授

国外访学经历:赴国际动物遗传育种顶尖机构--美国威斯康星大学麦迪逊分校动物科学系访问一年

国家级别基金项目:

1 棘皮类分子育种共性技术/2018YFD0901,国家重点研发计划-蓝色粮仓项目-海参分子育种,任务主持

2 虾夷扇贝温度相关动态性状的全基因组选择分析/31772844,国家自然科学基金, 主持

3 栉孔扇贝近交衰退效应的全基因组选择分析/ 32072976,国家自然科学基金, 主持

4 虾夷扇贝重要经济性状的动态生长/31302182, 国家自然科学基金, 主持

工作简介:

主要从事扇贝、刺参全基因组选择分子育种技术,与人工智能育种方法。作为负责人主持国家自然基金项目3项(国家级)。作为任务负责人承担“十三五”国家重点研发计划“蓝色粮仓科技创新”重点专项,主要负责刺参分子育种技术开发。“十二五”期间,作为任务负责人,参加了863 计划“高值海珍品良种培育”,基于全基因组信息的贝类遗传选育。在中国海洋大学包振民院士的带领下,利用分子育种技术,培育了“蓬莱红2号”、“海益丰12”、两个扇贝新品种做出了贡献。近年来第一或通讯作者身份发表SCI收录论文40余篇,其中一区或二区论文11篇,SCI影响因子(IF)大于104篇。与国际著名遗传育种专家美国威斯康星大学麦迪逊分校Rosa教授长期合作,进行全基因组选择育种技术研究。纽勤(美国)生物技术公司访问科学家,从事基因芯片设计。 与分别与大连海洋大学常亚青教授、丁君教授、泰山产业领军人才杨建敏教授合作,分别在东营黄河三角洲海域的山东华春渔业有限公司和烟台海域的烟台市崆峒岛实业有限公司等企业合作。 开发了国际首款海参基因芯片“HaishenSNP24K”,将液相芯片技术与深度学习神经网络育种模型联合应用于海参全基因组选择育种,相关成果发表在国际遗传学领域权威期刊Genomics

科研成果

1.全基因组选择核心技术方面

全基因组选择育种是通过整个基因组SNP连锁分析找到与表型性状有关的标记或数量性状基因座,并估计个体全基因组估计育种值,成倍提高遗传进展的育种研究。本人与国际遗传育种知名学者美国威斯康星大学麦迪逊分校Professor Guilherme J. M. Rosa,构建深层稀疏结构神经网络对基因位点上位互作非线性效应进行了设计,扩展了全基因组选择育种值估计的计算模型,以第一作者身份共同发表于美国动物科学学会(American Society of Animal Science)会刊《Journal of Animal Science》 上。深度学习算法是基于神经网络全基因选择核心技术的关键问题,本人针对全基因组选择中可以存在大量位点效应值非常小或无互作的情况,构建稀疏结构神经网络进行描述标记稀疏互作,并利用研究在非凸约束神经网络深度学习算法及其收敛性方面做了大量的工作,以通讯作者身份分别发表TOP期刊——美国电子电器学会(IEEE)会刊《IEEE Transactions on Neural Networks and Learning Systems》与《Neural Networks》。

2.全基因组选择中表型性状分析方面

精确、快速、高通量的数量性状提取是全基因选择研究的重要前提,本人利用计算机成像处理技术对扇贝外形、纹路等数量性状,进行了精细提取和描述;提出了基于高斯核匹配滤波结合多尺度PDE的全变分模型对贝壳图像中管状纹路进行迭代分割提取处理新方法,在不同尺度下得到了的精细纹路,得到了大量扇贝生长点的信息,并拟合扇贝生长过程Logistic函数;同时,成功实现了不同种类外形相似的扇贝类别区分,为扇贝趋同进化中形态学分类提供了有效方法,相关内容以通讯作者身份,发表在著名期刊——英国生态学会(British Ecological Society)会刊《Ecology and Evolution》及美国数学科学研究所(American institute of mathematical sciences  AIMS)会刊《Inverse Problem and Imaging

3.全基因选择在扇贝育种应用方面

本人利用上的2b-rad技术和全基因组选择中对扇贝进行遗传参数,育种值估计,抗逆性状遗传解析方面做了大量原创工作,相关内容以第一或通讯作者身份发表于水产领域TOP期刊 《Aquaculture》,Marine Biotechnology》,《Frontiers in Genetics

4.全基因选择软件开发方面。

发表了相关软件《贝类全基因组选择遗传育种分析评估系统》,《基于深度学习神经网络赤潮灾害预测软件》软件著作权,构建了基于LAMPLinuxApacheMySQLPHPWeb框架平台的贝类全基因组选择遗传育种分析评估系统,实现了估计育种值的快速计算并分析评估,有利于进一步应用于实际生产,提供了全基因选择育种的技术支持。

附录:代表性论著

软件著作

1. “贝类全基因组选择遗传育种分析评估系统”,中国海洋大学,2017SR093088,第1

2. “基于深度学习神经网络赤潮灾害预测软件”,中国海洋大学, 2016SR175844,第1

专利

“一种扇贝贝壳生长纹路的分割与识别方法”,中国海洋大学,ZL201610958374.3,第2

论文(第一或者通讯*

1 Qifan Zeng, Baojun Zhao, Hao Wang, Mengqiu Wang, Mingxuan Teng, Jingjie Hu, Zhenmin Bao, Yangfan Wang* Aquaculture Molecular Breeding Platform (AMBP): a comprehensive web server for genotype imputation and genetic analysis in aquacultureNucleic Acids Res. 2022 May 25;50(W1):W66-W74. doi: 10.1093/nar/gkac424 (一区TOP IF 19.2)

2. Qi Yao Ping Lin; Linshan Wang; Yangfan Wang* (王扬帆),Practical Exponential Stability of Impulsive Stochastic Reaction-Diffusion Systems With Delays 2022. IEEE Transactions on Cybernetics  PP(99)VOL. 52, NO. 5, MAY 2022 (一区TOP IF 19.1

3 Jia Lv#, , Yangfan Wang#(王扬帆), Ping Ni, Ping Lin, Hu Hou, Jun Ding,, Yaqing Chang, Jingjie Hu*, Shi Wang, Zhenmin BaoDevelopment of a high-throughput SNP array for sea cucumber (Apostichopus japonicus) and its application in genomic selection with MCP regularized deep neural networks. Genomics  Follow journal DOI: 10.1016/j.ygeno.2022.110426  (IF 5.6) (通讯、共一)

4 Xinghai Zhu, Ping Ni, Marc Sturrock, Yangfan Wang* (王扬帆), Jun Ding, Yaqing Chang,  Jingjie Hu,  Zhenmin Bao. Fine mapping and association analysis of candidate genes for papilla number in sea cucumber, Apostichopus japonicus, Marine Life Science & Technology . 2022 https://doi.org/10.1007/s42995-022-00139-w (IF 5.0 )

5 刘洋,王扬帆*(通讯作者),胡景杰,包振民,丁君,常亚青,杨建敏,侯虎, “基于卷积神经网络的仿刺参非侵入式标记方法的初步研究”,  中国水产科学 2022 JFSC2022-0173 中文核心

6 倪萍、任强、王静、王扬帆*(通讯作者)、丁君*、常亚青、胡景杰、包振民、“仿刺参疣足数量 SNP 遗传力评估” 2021                    中文核心

7 Yangfan Wang(王扬帆), Q. Ren, L. Zhao et.al.,Estimating genetic parameters of muscle imaging trait with 2b-RAD SNP markers in Zhikong scallop (Chlamys farreri)July 2021 Aquaculture 2021. AQUA_737715 DOI: 10.1016/j.aquaculture.2021.737715 IF 4.2 一区TOP

8  Zhu, X. , Ni, P. , Xing, Q. , Yangfan Wang* (王扬帆), & Zhenmin Bao. “Genomic prediction of growth traits in scallop using convolutional neural networks” July 2021 Aquaculture 545(3):737171 DOI: 10.1016/j.aquaculture.2021.737171 IF 4.2 一区TOP

Yangfan Wang (王扬帆)Xiao-Lin WuZhi LiZhenmin BaoGuilherme J. M. RosaEstimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture Models. Fronties in Genetics. 11:576.IF 4.6 权威)

10 Yangfan Wang(王扬帆)X MiGJM RosaZ ChenP LinS WangZhenmin Bao *“An R package for Fitting Sparse Neural Networks with Application in Animal Breeding”.Journal of Animal Science. 2018.96:2016–2026. 中科院,SCI二区TOP IF 1.813

11. T WeiP LinQ ZhuL WangYangfan Wang *(王扬帆),“DynamicalBehavior of Nonautonomous Stochastic Reaction–Diffusion Neural-Network Models”. IEEE Transactions on Neural Networks and Learning Systems.2018. (中科院,SCI一区TOP IF 11.683

12.  XiaoLiangLinshanYangfan Wang*(王扬帆)Ruili“Dynamical Behavior of Delayed Reaction-Diffusion Hopfield Neural Networks Driven by Infinite Dimensional Wiener Processes”.IEEE Transactions on Neural Networks and Learning Systems. 2016. 27(9):1816-1826.中科院,SCI一区TOP IF 11.683

13. T WeiP LinYangfan Wang*(王扬帆)L Wang“Stability of stochastic impulsive reaction diffusion neural networks with S-type Distributed delays and its application to Image encryption”.Neural Networks . 2019/8/116.中科院,SCI一区TOP 5.78

14. H Guo, Q ZengY LiYangfan Wang*(王扬帆)Z ChenL PingW ShiZhenmin Bao, “Estimating realized heritability for growth in Zhikong scallop (Chlamysfarreri) using genome-wide complex trait analysis”.Aquaculture, 2018. 497:103–108. 中科院,SCI二区TOP

15. YangfanWang(王扬帆)GuidongSunQifanZengZhihuiChenXiaoliHu, Zhenmin Bao. Predicting growth traits with genomic selection methods in Zhikong scallop (Chlamysfarreri).”Marine Biotechnology. 2018, DOI: 10.1007/s10126-018-9847-z .中科院,SCI二区

16. Qi YaoLinshan WangYangfan Wang*(王扬帆),“Existence-uniqueness and stability of reaction-diffusion stochastic Hopfield neural networks with S-type distributed time delays”. Neurocomputing.2017. 275 :470–477中科院,SCI二区

17. T WeiL WangYangfan Wang*(王扬帆) ,“Existence, uniqueness andstability of mild solutions to stochastic reaction–diffusion Cohen–Grossberg neural networks with delays and Wiener processes”.Neurocomputing,2017. 275 :470–477中科院,SCI二区

18. Guo Z. , Lin P., Yangfan Wang*(王扬帆),Retinal vesselsegmentation using a finite element based binary level set method”.Inverse Problems & Imaging, 2014, 8 (2) : 459-473.中科院,SCI二区

19. Q XingT WeiZ ChenYangfan Wang*(王扬帆)Y LuS WangL ZhangZ Bao “Using a multiscale image processing method to characterize the periodic growth patterns on scallop shells.”Ecology and Evolution. 2017. 7(5): 1616–1626.SCI

20. T WeiYangfan Wang*(王扬帆)L Wang“Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping”.Neural Processing Letters 2017 .doi.org/10.1007/s11063-017-9756-6SCI

  

21. T WeiL WangL PingJ ChenYangfan Wang*(王扬帆)H Zheng.“Learning non-negativity constrained variation for image denoising and deblurring”. Numerical Mathematics Theory Methods & Applications, 10(4), 852-871.SCI

22 Guo, Z. ,  Lin, P. , &  Yangfan Wang*(王扬帆). (2014). Continuous finite element schemes for a phase field model in two-layer fluid bénardmarangoni convection computations. Computer Physics Communications, 185(1), 63-78.

  

23 T. Dumitruc, M. Chaplain, P. Lin, Yangfan Wang*(王扬帆). A multiscale moving boundary model arising in cancer invasion, Multiscale Modelling Simulation: A SIAM Interdisciplinary J. 11(1), 2013, pp.309-335. (* corresponding author). SCI 一区)

  

24 Yangfan Wang(王扬帆), G. Ji., P. Lin, M. Emanual, Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition, Pattern Recognition, available online 10 Feb 2013. ( First author) SCI二区,TOP

  

25 Yangfan Wang(王扬帆), P. Lin, L. Wang. Exponential stability of reactiondiffusion high-order Markovian jump Hopfield neural networks with time-varying delays, Nonlinear Analysis: Real World Applications 13 (2012), No 3, pp 1353-1361.( First author) SCI一区)

  

26 Yangfan Wang(王扬帆), L. Wang. Global exponential stability of high-order Hopfield-type neural networks with S-type distributed time delays , Communications in Nonlinear Science and Numerical Simulation ,Vol.16(2011)3319-3325. ( First author)SCI一区)

  

27 Yangfan Wang(王扬帆), L. Wang. LMI-Based Approach for Exponential Robust Stability of High-Order Hopfield Neural Networks with Time-Varying Delays. J. Applied Mathematics (2012) ( First author)SCI

  

28 SU HailinLI HengdeS WangYangfan Wang*(王扬帆)Zhenmin Bao “Performance comparison of two efficient genomic selection methods (gsbay&MixP) applied in aquacultural organisms”. Journal of Ocean University of China , 2017 , 16 (1) :137-144SCI

  

29 Li, Y. ,  Wang, R. ,  Xun, X. ,  Wang, J. ,  Bao, L. , &  Thimmappa, R. ,Yangfan Wang(王扬帆), Zhenmin Bao. Sea cucumber genome provides insights into saponin biosynthesis and aestivation regulation. Cell Discovery.

  

30 Zhang, M. ,  Yangfan Wang(王扬帆), Y. ,  Li, Y. ,  Li, W. ,  Li, R. , &  Xie, X. , Zhenmin Bao. (2018). Identification and characterization of neuropeptides by transcriptome and proteome analyses in a bivalve mollusc patinopecten yessoensis. Frontiers in Genetics, 9, 197.

  

31  Guo, H. ,  Li, Y. ,  Zhang, M. ,  Li, R. ,  Li, W. , &  Lou, J. , Yangfan Wang*(王扬帆), Zhenmin Bao. (2018). Expression of cathepsin f in response to bacterial challenges in yesso scallop patinopecten yessoensis. Fish & Shellfish Immunology, 80, 141-147.

  

32 Li, Y. ,  Sun, X. ,  Hu, X. ,  Xun, X. ,  Zhang, J. , &  Guo, X. , Yangfan Wang(王扬帆), Zhenmin Bao. (2017). Scallop genome reveals molecular adaptations to semi-sessile life and neurotoxins. Nature Communications, 8(1), 1721.

  

33 Dou, J. ,  Li, X. ,  Fu, Q. ,  Jiao, W. ,  Li, Y. , &  Li, T. , Yangfan Wang(王扬帆), Zhenmin Bao. (2016). Evaluation of the 2b-rad method for genomic selection in scallop breeding. Scientific Reports, 6, 19244.

  

34 Tian, M. ,  Li, Y. ,  Jing, J. ,  Mu, C. ,  Du, H. , &  Dou, J. ,Yangfan Wang(王扬帆), Zhenmin Bao. (2015). Construction of a high-density genetic map and quantitative trait locus mapping in the sea cucumber apostichopus japonicus. Scientific Reports,  5, 14852.

  

35 Jiao, W. ,  Fu, X. ,  Dou, J. ,  Li, H. ,  Su, H. , &  Mao, J. , Yangfan Wang(王扬帆), Zhenmin Bao. (2013). High-resolution linkage and quantitative trait locus mapping aided by genome survey sequencing: building up an integrative genomic framework for a bivalve mollusc. DNA Research,21,1(2013-10-9)(1), 85-101.

  

36 R Nian, J, Yu., Yangfan Wang*(王扬帆), Zhenmin Bao. ROV-based Underwater Vision System for Intelligent Fish Ethology Research[J]. International Journal of Advanced Robotic Systems, 2013, 10. corresponding authorSCI