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BIB
2007
108views more  BIB 2007»
13 years 5 months ago
Bayesian methods in bioinformatics and computational systems biology
Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that is uncertain or subject to any kind of error or noise (including measurem...
Darren J. Wilkinson
APBC
2003
116views Bioinformatics» more  APBC 2003»
13 years 6 months ago
An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics
Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely ap...
Aik Choon Tan, David Gilbert
BIBE
2006
IEEE
138views Bioinformatics» more  BIBE 2006»
13 years 11 months ago
Assigning Schema Labels Using Ontology And Hueristics
Bioinformatics data is growing at a phenomenal rate. Besides the exponential growth of individual databases, the number of data depositories is increasing too. Because of the comp...
Xuan Zhang, Ruoming Jin, Gagan Agrawal
KDD
2004
ACM
163views Data Mining» more  KDD 2004»
14 years 5 months ago
Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods
We consider the problem of improving named entity recognition (NER) systems by using external dictionaries--more specifically, the problem of extending state-of-the-art NER system...
William W. Cohen, Sunita Sarawagi
BIBM
2007
IEEE
13 years 11 months ago
A Semi-supervised Learning Approach to Disease Gene Prediction
Discovering human disease-causing genes (disease genes in short) is one of the most challenging problems in bioinformatics and biomedicine, as most diseases are related in some wa...
Thanh Phuong Nguyen, Tu Bao Ho