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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
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Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > Seminar on Bioinformatics Energy entropy vector: an efficient method for coding and classifying unpaired gene sequences
Energy entropy vector: an efficient method for coding and classifying unpaired gene sequences
Organizer
Shing Toung Yau
Speaker
Hao Wang
Time
Saturday, January 6, 2024 9:30 PM - 10:00 PM
Venue
Online
Abstract
In this study, a novel gene encoding method, energy entropy vector, is proposed. The method does not require comparison or interception, and is able to map gene sequences of arbitrary length into 18-dimensional vectors, overcoming the limitations of traditional methods in feature extraction. We performed experimental validation on five microbial datasets and compared it with the natural vector method and the covariance natural vector method. The experimental results show that the proposed method performs well in convex packet classification, kingdom, and family classification tasks, especially in the family classification task, where the accuracy is improved by 15% to 30%.
Beijing Institute of Mathematical Sciences and Applications
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