-
姓 名：张富豪
职 称：副教授
办公室：信息工程学院408
邮 箱：fhzhang@nwafu.edu.cn
基本信息 张富豪，博士，副教授，于2023年获得中南大学计算机科学与技术博士学位。2024年1月入职西北农林科技大学信息工程学院。主要研究方向是生物信息学，研究工作集中在利用和开发人工智能方法对生物信息学中的交叉前沿和热点问题进行探索和研究，具体研究内容包括蛋白质功能预测、蛋白质作用位点预测、固有无序蛋白质功能性分析、蛋白质相变等，目前在Nucleic Acids Research、Bioinformatics、Briefings in Bioinformatics等国际期刊和会议上发表学术论文10余篇。
研究方向 生物信息学，人工智能 学术成果 代表性论文
[1] Zhang, F., Li, M., Zhang, J. and Kurgan, L., 2023. HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins. Nucleic Acids Research, 51(5), pp.e25-e25.
[2] Zhang, F., Li, M., Zhang, J., Shi, W. and Kurgan, L., 2023. DeepPRObind: Modular deep learner that accurately predicts structure and disorder-annotated protein binding residues. Journal of Molecular Biology, p.167945.
[3] Zhang, F., Zhao, B., Shi, W., Li, M. and Kurgan, L., 2022. DeepDISOBind: accurate prediction of RNA-, DNA-and protein-binding intrinsically disordered residues with deep multi-task learning. Briefings in bioinformatics, 23(1), p.bbab521.
[4] Zhang, F., Shi, W., Zhang, J., Zeng, M., Li, M. and Kurgan, L., 2020. PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection. Bioinformatics, 36(Supplement_2), pp.i735-i744.
[5] Zhang, F., Song, H., Zeng, M., Wu, F.X., Li, Y., Pan, Y. and Li, M., 2020. A deep learning framework for gene ontology annotations with sequence-and network-based information. IEEE/ACM transactions on computational biology and bioinformatics, 18(6), pp.2208-2217.
出版书籍
[1] Li, M., Zhang, F. and Kurgan, L., 2023. Machine learning methods for predicting protein-nucleic acids interactions. In Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics (pp. 265-287).
社会兼职 [1] IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 的 Program Committee Member
[2] Briefings in Bioinformatics等国际期刊审稿人
获奖情况 [1] 2020年，阿里云-蛋白质结构预测挑战赛 冠军
招生情况 欢迎对人工智能和生物信息学感兴趣的本科生与我联系，期望能够保证每周投入一定的工作时长。对于踏实努力的同学，我将在工作和继续深造上提供大力支持。
-
Name：
Professional Title：
Office：
Tel：
Email：
Personal Information Research Directions Curriculum Academic Achievement Current Undergraduate Graduated Student