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Tsai-Yun Lin

dblp:70/6816 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1

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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene regulation › gene regulatory network
transcription regulatory network
0.012004
Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle · Bioinform. 2004
Bioinformatics and computational biology
gene expression analysis
0.012004
Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle · Bioinform. 2004
Bioinformatics and computational biology › gene expression analysis › time-series gene expression analysis
time-course microarray analysis
0.012004
Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle · Bioinform. 2004

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

dynamic modeling · 0.0correlation analysis · 0.0
YearPublicationVenuePosition
2004 Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle
abstract
MOTIVATION: Genome-wide gene expression programs have been monitored and analyzed in the yeast Saccharomyces cerevisiae, but how cells regulate global gene expression programs in response to environmental changes is still far from being understood. We present a systematic approach to quantitatively characterize the transcriptional regulatory network of the yeast cell cycle. For the interpretative purpose, 20 target genes were selected because their expression patterns fluctuated in a periodic manner concurrent with the cell cycle and peaked at different phases. In addition to the most significant five possible regulators of each specific target gene, the expression pattern of each target gene affected by synergy of the regulators during the cell cycle was characterized. Our first step includes modeling the dynamics of gene expression and extracting the transcription rate from a time-course microarray data. The second step embraces finding the regulators that possess a high correlation with the transcription rate of the target gene, and quantifying the regulatory abilities of the identified regulators. RESULTS: Our network discerns not only the role of the activator or repressor for each specific regulator, but also the regulatory ability of the regulator to the transcription rate of the target gene. The highly coordinated regulatory network has identified a group of significant regulators responsible for the gene expression program through the cell cycle progress. This approach may be useful for computing the regulatory ability of the transcriptional regulatory networks in more diverse conditions and in more complex eukaryotes. SUPPLEMENTARY INFORMATION: Matlab code and test data are available at http://www.ee.nthu.edu.tw/~bschen/quantitative/regulatory_network.htm
Hong-Chu Chen, Hsiao-Ching Lee, Tsai-Yun Lin, Wen-Hsiung Li, Bor-Sen Chen
Bioinform.3