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Hongyao Zhu

dblp:76/1814 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · unresolved

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

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

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 · 77% Computational science and engineering · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
multiple sequence alignment		 0.012000
MASIA: recognition of common patterns and properties in multiple aligned protein sequences · Bioinform. 2000
Computational science and engineering
pattern recognition		 0.012000
MASIA: recognition of common patterns and properties in multiple aligned protein sequences · Bioinform. 2000
Bioinformatics and computational biology
protein sequence analysis		 0.012000
MASIA: recognition of common patterns and properties in multiple aligned protein sequences · Bioinform. 2000
Bioinformatics and computational biology
protein structure prediction		 0.012000
MASIA: recognition of common patterns and properties in multiple aligned protein sequences · Bioinform. 2000
Bioinformatics and computational biology › protein sequence analysis
amino acid property analysis		 0.012000
MASIA: recognition of common patterns and properties in multiple aligned protein sequences · Bioinform. 2000

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

statistical significance testing · 0.0
YearPublicationVenuePosition
2000 MASIA: recognition of common patterns and properties in multiple aligned protein sequences
abstract
SUMMARY: MASIA is a software tool for pattern recognition in multiple aligned protein sequences. MASIA converts a sequence to a properties matrix that can be scanned in both vertical and horizontal steps. Consistent patterns are recognized based on the statistical significance of their occurrence. Preset macros can be altered on-line to seek any combination of amino acid properties or sequence characteristics. MASIA output can be used directly by our programs to predict the 3D structure of proteins. AVAILABILITY: Access MASIA at http://www.scsb.utmb.edu/masia/ma sia.html.
Hongyao Zhu, Catherine H. Schein, Werner Braun
 Bioinform.1