Automatic Recognition of Regions of Intrinsically Poor Multiple Alignment Using Machine Learning
نویسندگان
چکیده
Phylogenetic analysis requires alignment of gene or protein sequences. Some regions of genes evolve fast and suffer numerous insertion and deletion events and cannot be aligned reliably with automatic alignment algorithms. Such regions of intrinsically uncertain alignment are currently detected and deleted manually before performing phylogenetic analysis. We present the results of a machine learning approach to detect regions of poor alignment automatically. We compare the results obtained from Naive Bayes (NB), C4.5 Decision Tree (C4.5) and Support Vector Machine (SVM) approaches.
منابع مشابه
Automating Recognition of Regions of Intrinsically Poor Multiple Alignment for Phylogenetic Analysis Using Machine Learning
Phylogenetic analysis requires alignment of gene sequences. Automatic alignment programs produced regions of intrinsically poor alignment that are currently detected and deleted manually. We present the results of a machine learning approach to detection of these regions of the alignment. We compare naive Bayes, standard decision trees, and support vector machines. The results show three algori...
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