Hao Wu
Professor Supervisor of Doctorate Candidates Supervisor of Master's Candidates
Name (Simplified Chinese):吴昊
Name (English):Hao Wu
Name (Pinyin):Wu Hao
E-Mail:
Date of Employment:2020-10-14
School/Department:Shandong University
Administrative Position:Associate Professor
Education Level:With Certificate of Graduation for Doctorate Study
Business Address:215a Office of scientific research building in Software Park Campus of Shandong University
No.1500, Shunhua Road, high tech Zone, Jinan City, Shandong Province
Gender:Male
Contact Information:
Degree:Doctor
Status:Employed
Academic Titles:Teaching and research, master's degree Supervisor
Other Post:Senior member of China Computer Association (CCF); Young and general project evaluation experts of NSFC; National Master's thesis evaluation expert; More than ten reviewers and guest editors of SCI Journals
Alma Mater:Xidian University
College:School of Software
Discipline:Other Majors of Software Engineering
Computer Applications Technology
Computer Science and Technology
Honor
2021 山东大学软件学院优秀教师
2020 山东大学教师教学创新大赛三等奖
2018 西北农林科技大学信息工程学院本科毕业设计优秀指导教师
2016 西北农林科技大学青年教师讲课比赛二等奖，比赛现场评分第一名
2016 西北农林科技大学信息工程学院“特殊课时津贴”奖
2013 教改项目《计算机网络应用技术课程建设改革与实践》荣获西北农林科技大学教学成果二等奖
2009 西北农林科技大学 “优秀班主任”
2008 西北农林科技大学“校级优秀教师”
Hits:
Institution:软件学院
Title of Paper:StackTADB: a stacking-based ensemble learning model for predicting the boundaries of topologically associating domains (TADs) accurately in fruit f lies
Journal:Briefings in Bioinformatics
Key Words:topologically associating domains (TADs), ensemble learning, machine learning, sequence analysis
Summary:Chromosome is composed of many distinct chromatin domains, referred to variably as topological domains or topologically associating domains (TADs). The domains are stable across different cell types and highly conserved across species, thus these chromatin domains have been considered as the basic units of chromosome folding and regarded as an important secondary structure in chromosome organization. However, the identification of TAD boundaries is still a great challenge due to the high cost and low resolution of Hi-C data or experiments. In this study, we propose a novel ensemble learning framework, termed as StackTADB, for predicting the boundaries of TADs. StackTADB integrates four base classifiers including Random Forest, Logistic Regression, K-NearestNeighbor and Support Vector Machine. From the analysis of a series of examinations on the data set in the previous study, it is concluded that StackTADB has optimal performance in six metrics, AUC, Accuracy, MCC, Precision, Recall and F1 score, and it is superior to the existing methods. In addition, the comparison of the performance of multiple features shows that Kmers-based features play an essential role in predicting TADs boundaries of fruit flies, and we also apply the SHapley Additive exPlanations (SHAP) framework to interpret the predictions of StackTADB to identify the reason why Kmers-based features are vital. The experimental results show that the subsequences matching the BEAF-32 motif play a crucial role in predicting the boundaries of TADs. The source code is freely available at https://github.com/HaoWuLab-Bioinformatics/StackTADB and the webserver of StackTADB is freely available at http://hwtad.sdu.edu.cn:8002/StackTADB.
First Author:Hao Wu*
All the Authors:Pengyu Zhang,Zhaoheng Ai,Leyi Wei,Hongming Zhang*,Fan Yang*,Lizhen Cui*
Document Code:24DDA04061DE4D03B344C2068828930D
Discipline:Engineering
First-Level Discipline:Software Engineering
Document Type:J
Volume:24
Issue:1
Page Number:bbac023
Impact Factor:11.622
DOI Number:10.1093/bib/bbac023
Number of Words:12
Translation or Not:No
Date of Publication:2022-02
Included Journals:SCI
Links to Published Journals:https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac023/6531900
Release Time:2022-03-03
Attachments
