TAILIEUCHUNG - Báo cáo sinh học: "Learning from positive examples when the negative class is undetermined- microRNA gene identification"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Learning from positive examples when the negative class is undetermined- microRNA gene identification. | Algorithms for Molecular Biology BioMed Central Research Learning from positive examples when the negative class is undetermined- microRNA gene identification Malik Yousef1 3 Segun Jung1 2 4 Louise C Showe1 and Michael K Showe 1 Open Access Address 1 Systems Biology Division The Wistar Institute Philadelphia PA 19104 USA 2School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia PA 19104 USA 3Computer Science The College of Sakhnin Sakhnin Israel and 4Sackler Institute of Graduate Biomedical Sciences School of Medicine New York NY 10016 USA Email MalikYousef-yousef@ Segun Jung-sj801@ Louise C Showe - lshowe@ Michael K Showe - showe@ Corresponding author Published 28 January 2008 Received 22 June 2007 Algorithms for Molecular Biology 2008 3 2 doi I748-7I88-3-2 Accepted 28 January 2008 This article is available from http content 3 1 2 2008 Yousef et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract__ Background The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We and others have described the use of two-class machine learning to identify novel miRNAs. These methods require the generation of an artificial negative class. However designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and or yield a biased estimate of performance. We present a study using one-class machine learning for microRNA miRNA discovery and compare one-class to

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