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This paper reports on the evaluation of different machine learning techniques for the automated classification of coding gene sequences obtained from several organisms in terms of their functional role as adhesins. Diverse, biologically-meaningful, sequence-based features were extracted from the sequences and used as inputs to the in silico prediction models. Another contribution of this work is the...
Data preprocessing is important in machine learning, data mining, and pattern recognition. In particular, selecting relevant features in high- dimensional data is often necessary to efficiently construct models that accurately describe the data. For example, many lazy learning algorithms (like k- Nearest Neighbor) rely on feature-based distance metrics to compare input patterns for the purpose of...
Summary form only given. The current molecular biology and systems biology is featured by the rapid accumulation of high-throughput genomics and proteomics data like microarray and mass spectrometry (MS) data. Through our study on microarray and MS data, we have observed that the cancer classification and gene/biomarker selection task has many unique characteristics that distinguish itself from other...
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