Literature DB >> 16342046

Support vector machines for novel class detection in Bioinformatics.

Eduardo J Spinosa1, André C P L F de Carvalho.   

Abstract

Novelty detection techniques might be a promising way of dealing with high-dimensional classification problems in Bioinformatics. We present preliminary results of the use of a one-class support vector machine approach to detect novel classes in two Bioinformatics databases. The results are compatible with theory and inspire further investigation.

Mesh:

Year:  2005        PMID: 16342046

Source DB:  PubMed          Journal:  Genet Mol Res        ISSN: 1676-5680


  4 in total

1.  A Machine-Learning Approach to Detecting Unknown Bacterial Serovars.

Authors:  Ferit Akova; Murat Dundar; V Jo Davisson; E Daniel Hirleman; Arun K Bhunia; J Paul Robinson; Bartek Rajwa
Journal:  Stat Anal Data Min       Date:  2010-10       Impact factor: 1.051

2.  An fMRI normative database for connectivity networks using one-class support vector machines.

Authors:  João Ricardo Sato; Maria da Graça Morais Martin; André Fujita; Janaina Mourão-Miranda; Michael John Brammer; Edson Amaro
Journal:  Hum Brain Mapp       Date:  2009-04       Impact factor: 5.038

3.  Learning from positive examples when the negative class is undetermined--microRNA gene identification.

Authors:  Malik Yousef; Segun Jung; Louise C Showe; Michael K Showe
Journal:  Algorithms Mol Biol       Date:  2008-01-28       Impact factor: 1.405

4.  A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.

Authors:  Ashenafi Zebene Woldaregay; Ilkka Kalervo Launonen; David Albers; Jorge Igual; Eirik Årsand; Gunnar Hartvigsen
Journal:  J Med Internet Res       Date:  2020-08-12       Impact factor: 5.428

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.