Literature DB >> 25132640

SFAPS: an R package for structure/function analysis of protein sequences based on informational spectrum method.

Su-Ping Deng1, De-Shuang Huang2.   

Abstract

The R package SFAPS has been developed for structure/function analysis of protein sequences based on information spectrum method. The informational spectrum method employs the electron-ion interaction potential parameter as the numerical representation for the protein sequence, and obtains the characteristic frequency of a particular protein interaction after computing the Discrete Fourier Transform for protein sequences. The informational spectrum method is often used to analyze protein sequences, so we developed this software tool, which is implemented as an add-on package to the freely available and widely used statistical language R. Our package is distributed as open source code for Linux, Unix and Microsoft Windows. It is released under the GNU General Public License. The R package along with its source code and additional material are freely available at http://mlsbl.tongji.edu.cn/DBdownload.asp.
Copyright © 2014 Elsevier Inc. All rights reserved.

Keywords:  Informational spectrum method; Protein sequence; R package; Structure/function analysis

Mesh:

Year:  2014        PMID: 25132640     DOI: 10.1016/j.ymeth.2014.08.004

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  21 in total

1.  NmSEER V2.0: a prediction tool for 2'-O-methylation sites based on random forest and multi-encoding combination.

Authors:  Yiran Zhou; Qinghua Cui; Yuan Zhou
Journal:  BMC Bioinformatics       Date:  2019-12-24       Impact factor: 3.169

2.  RicENN: Prediction of Rice Enhancers with Neural Network Based on DNA Sequences.

Authors:  Yujia Gao; Yiqiong Chen; Haisong Feng; Youhua Zhang; Zhenyu Yue
Journal:  Interdiscip Sci       Date:  2022-02-21       Impact factor: 2.233

3.  Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction.

Authors:  Xiao-Rui Su; Lun Hu; Zhu-Hong You; Peng-Wei Hu; Bo-Wei Zhao
Journal:  BMC Bioinformatics       Date:  2022-06-16       Impact factor: 3.307

4.  In silico analysis suggests interaction between Ebola virus and the extracellular matrix.

Authors:  Veljko Veljkovic; Sanja Glisic; Claude P Muller; Matthew Scotch; Donald R Branch; Vladimir R Perovic; Milan Sencanski; Nevena Veljkovic; Alfonso Colombatti
Journal:  Front Microbiol       Date:  2015-02-19       Impact factor: 5.640

5.  Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data.

Authors:  Chen Peng; Ao Li; Minghui Wang
Journal:  Sci Rep       Date:  2017-11-15       Impact factor: 4.379

6.  PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation.

Authors:  Jun Zhang; Bin Liu
Journal:  Int J Mol Sci       Date:  2017-08-25       Impact factor: 5.923

7.  Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

Authors:  Zhen Shen; Wenzheng Bao; De-Shuang Huang
Journal:  Sci Rep       Date:  2018-10-15       Impact factor: 4.379

8.  Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease.

Authors:  Vince D Calhoun
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2017-09-04       Impact factor: 3.710

9.  Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

Authors:  Ji-Yong An; Fan-Rong Meng; Zhu-Hong You; Xing Chen; Gui-Ying Yan; Ji-Pu Hu
Journal:  Protein Sci       Date:  2016-08-09       Impact factor: 6.725

10.  Protein remote homology detection based on bidirectional long short-term memory.

Authors:  Shumin Li; Junjie Chen; Bin Liu
Journal:  BMC Bioinformatics       Date:  2017-10-10       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.