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» Comparisons of sequence labeling algorithms and extensions
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BMCBI
2008
112views more  BMCBI 2008»
13 years 8 months ago
A simplified approach to disulfide connectivity prediction from protein sequences
Background: Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on differen...
Marc Vincent, Andrea Passerini, Matthieu Labb&eacu...
IISWC
2006
IEEE
14 years 2 months ago
Performance Analysis of Sequence Alignment Applications
— Recent advances in molecular biology have led to a continued growth in the biological information generated by the scientific community. Additionally, this area has become a m...
Friman Sánchez, Esther Salamí, Alex ...
BMCBI
2007
135views more  BMCBI 2007»
13 years 8 months ago
Automatic extraction of reliable regions from multiple sequence alignments
Background: High quality multiple alignments are crucial in the transfer of annotation from one genome to another. Multiple alignment methods strive to achieve ever increasing lev...
Timo Lassmann, Erik L. L. Sonnhammer
PAMI
2011
13 years 3 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
GECCO
2003
Springer
100views Optimization» more  GECCO 2003»
14 years 1 months ago
Studying the Advantages of a Messy Evolutionary Algorithm for Natural Language Tagging
The process of labeling each word in a sentence with one of its lexical categories (noun, verb, etc) is called tagging and is a key step in parsing and many other language processi...
Lourdes Araujo