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:
Title of Paper:Deep neural network models for cell type prediction based on single-cell Hi-C data
Journal:BMC supplements
Key Words:Deep neural networks; Single-cell Hi-C data; Cell type prediction; Cell classification
Summary:Background: Cell type prediction is crucial to cell type Identification of genomics, cancer diagnosis and drug development, and it can solve the difficult and timeconsuming problem of cell classification in biological experiments. Therefore, the computational methods are urgently needed to classify and predict cell types using single-cell Hi-C data. In previous studies, there is a lack of convenient and accurate method to predict cell types based on single-cell Hi-C data. Deep neural networks
can form complex representations of single-cell Hi-C data and make it possible to handle the multidimensional and sparse biological datasets.
Results: We compare the performance of SCANN with existing methods and analyze the model by using five different evaluation metrics. When using only ML1 and ML3 datasets, in terms of the ARI and NMI values, SCANN improve 14% and
11% over the baseline approach k-means++ respectively. However, when using all six libraries of data, the ARI and NMI values of SCANN improve 63% and 88% over the baseline approach k-means++ respectively. Therefore, SCANN is highly
accurate in predicting the type of independent cell samples using single-cell Hi-C data.
Conclusions: SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study
First Author:Bing Zhou
Correspondence Author:Hao Wu
All the Authors:Meili Wang,Quanzhong Liu
Discipline:Engineering
First-Level Discipline:Software Engineering
Document Type:J
Volume:12
Issue:3
Page Number:1-12
Impact Factor:3.8
Translation or Not:No
Date of Publication:2021-04
Included Journals:SCI
Release Time:2021-04-21
