Literature DB >> 15906142

Support vector machines for predicting apoptosis proteins types.

Jing Huang1, Feng Shi.   

Abstract

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death, and their function is related to their types. According to the classification scheme by Zhou and Doctor (2003), the apoptosis proteins are categorized into the following four types: (1) cytoplasmic protein; (2) plasma membrane-bound protein; (3) mitochondrial inner and outer proteins; (4) other proteins. A powerful learning machine, the Support Vector Machine, is applied for predicting the type of a given apoptosis protein by incorporating the sqrt-amino acid composition effect. High success rates were obtained by the re-substitute test (98/98 = 100 %) and the jackknife test (89/98 = 90.8%).

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Year:  2005        PMID: 15906142     DOI: 10.1007/s10441-005-7002-5

Source DB:  PubMed          Journal:  Acta Biotheor        ISSN: 0001-5342            Impact factor:   1.774


  3 in total

1.  Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network.

Authors:  Jian Cui; Jinghua Liu; Yuhua Li; Tieliu Shi
Journal:  PLoS One       Date:  2011-01-31       Impact factor: 3.240

2.  MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.

Authors:  Xiao Wang; Hui Li; Rong Wang; Qiuwen Zhang; Weiwei Zhang; Yong Gan
Journal:  Comput Intell Neurosci       Date:  2017-07-04

3.  Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization.

Authors:  Junzhe Cao; Wenqi Liu; Jianjun He; Hong Gu
Journal:  PLoS One       Date:  2013-06-26       Impact factor: 3.240

  3 in total

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