Literature DB >> 28285094

Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties.

Nguyen-Quoc-Khanh Le1, Trinh-Trung-Duong Nguyen2, Yu-Yen Ou3.   

Abstract

The electron transport proteins have an important role in storing and transferring electrons in cellular respiration, which is the most proficient process through which cells gather energy from consumed food. According to the molecular functions, the electron transport chain components could be formed with five complexes with several different electron carriers and functions. Therefore, identifying the molecular functions in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. This work includes two phases for discriminating electron transport proteins from transport proteins and classifying categories of five complexes in electron transport proteins. In the first phase, the performances from PSSM with AAIndex feature set were successful in identifying electron transport proteins in transport proteins with achieved sensitivity of 73.2%, specificity of 94.1%, and accuracy of 91.3%, with MCC of 0.64 for independent data set. With the second phase, our method can approach a precise model for identifying of five complexes with different molecular functions in electron transport proteins. The PSSM with AAIndex properties in five complexes achieved MCC of 0.51, 0.47, 0.42, 0.74, and 1.00 for independent data set, respectively. We suggest that our study could be a power model for determining new proteins that belongs into which molecular function of electron transport proteins.
Copyright © 2017 Elsevier Inc. All rights reserved.

Keywords:  Annotation; Electron transport proteins; Feature selection; Transporter

Mesh:

Substances:

Year:  2017        PMID: 28285094     DOI: 10.1016/j.jmgm.2017.01.003

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  6 in total

1.  Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis.

Authors:  Dandan Jin; Yujie Jiao; Jie Ji; Wei Jiang; Wenkai Ni; Yingcheng Wu; Runzhou Ni; Cuihua Lu; Lishuai Qu; Hongbing Ni; Jinxia Liu; Weisong Xu; MingBing Xiao
Journal:  PeerJ       Date:  2020-06-15       Impact factor: 2.984

2.  Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement.

Authors:  Hyoun-Joong Kong; Sungwan Kim; Minwoo Cho; Jee Hyun Kim; Kyoung Sup Hong; Joo Sung Kim
Journal:  PeerJ       Date:  2019-07-29       Impact factor: 2.984

3.  An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets.

Authors:  Jamshid Pirgazi; Mohsen Alimoradi; Tahereh Esmaeili Abharian; Mohammad Hossein Olyaee
Journal:  Sci Rep       Date:  2019-12-09       Impact factor: 4.379

4.  A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data.

Authors:  Wenbing Chang; Yinglai Liu; Yiyong Xiao; Xinglong Yuan; Xingxing Xu; Siyue Zhang; Shenghan Zhou
Journal:  Diagnostics (Basel)       Date:  2019-11-07

5.  SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data.

Authors:  Nguyen Quoc Khanh Le; Van-Nui Nguyen
Journal:  PeerJ Comput Sci       Date:  2019-02-25

6.  TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings.

Authors:  Trinh-Trung-Duong Nguyen; Nguyen-Quoc-Khanh Le; Quang-Thai Ho; Dinh-Van Phan; Yu-Yen Ou
Journal:  BMC Med Genomics       Date:  2020-10-22       Impact factor: 3.063

  6 in total

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