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Zhu-Jin Zhang

dblp:51/7065 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequence visualization
DNA sequence visualization
0.112009
DV-Curve: a novel intuitive tool for visualizing and analyzing DNA sequences · Bioinform. 2009
Bioinformatics and computational biology
sequence analysis
0.112009
DV-Curve: a novel intuitive tool for visualizing and analyzing DNA sequences · Bioinform. 2009

Methods — techniques the papers use, named apart from their topics

dual-vector curve · 0.1
YearPublicationVenuePosition
2022 Imputing DNA Methylation by Transferred Learning Based Neural Network
Xinfeng Wang, Jiahua Rao, Zhu-Jin Zhang, Yuedong Yang
J. Comput. Sci. Technol.4
2009 DV-Curve: a novel intuitive tool for visualizing and analyzing DNA sequences
abstract
MOTIVATION: The rapid growth of DNA sequence data creates a need to find graphic tools to study DNA sequence in a perceivable form. A large number of scholars work hard in this field. However, it is difficult to handle the following problems in one graph: (i) degeneracy, (ii) loss of information, (iii) difficulty of observing in multi-dimensional graph, (iv) difficulty of visualization when representing long DNA sequences and (v) need to reflect useful information. RESULTS: DV-Curve (Dual-Vector Curve) using two vectors to represent one alphabet of DNA sequences not only avoids degeneracy and loss of information, but also has good visualization no matter whether sequences are long, and can reflect the length of DNA sequence. The applications of the DV-Curve on mutation analysis and two types of similarity analysis are presented in detail. DV-Curve is a significative tool by which biologists could find useful biological knowledge. AVAILABILITY: The corresponding software of DV-Curve is available at http://bmchust.3322.org/Data/Soft/332-DV-Curve2.0.zip.
Zhu-Jin Zhang
Bioinform.1