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Zehong Zou

dblp:57/8541 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · unresolved

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein function prediction
allergenicity prediction
0.112012
SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides · Bioinform. 2012
Bioinformatics and computational biology › protein function prediction
protein classification
0.112012
SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides · Bioinform. 2012

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

support vector machine · 0.1BLAST E-value · 0.1
YearPublicationVenuePosition
2012 SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides
abstract
UNLABELLED: SORTALLER is an online allergen classifier based on allergen family featured peptide (AFFP) dataset and normalized BLAST E-values, which establish the featured vectors for support vector machine (SVM). AFFPs are allergen-specific peptides panned from irredundant allergens and harbor perfect information with noise fragments eliminated because of their similarity to non-allergens. SORTALLER performed significantly better than other existing software and reached a perfect balance with high specificity (98.4%) and sensitivity (98.6%) for discriminating allergenic proteins from several independent datasets of protein sequences of diverse sources, also highlighting with the Matthews correlation coefficient (MCC) as high as 0.970, fast running speed and rapidly predicting a batch of amino acid sequences with a single click. AVAILABILITY AND IMPLEMENTATION: http://sortaller.gzhmc.edu.cn/.
Lida Zhang, Yuyi Huang, Zehong Zou, Ximo Chen, Ailin Tao
Bioinform.3