224 related articles for article (PubMed ID: 26233307)
1. Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC.
Ahmad S; Kabir M; Hayat M
Comput Methods Programs Biomed; 2015 Nov; 122(2):165-74. PubMed ID: 26233307
[TBL] [Abstract][Full Text] [Related]
2. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition.
Feng PM; Chen W; Lin H; Chou KC
Anal Biochem; 2013 Nov; 442(1):118-25. PubMed ID: 23756733
[TBL] [Abstract][Full Text] [Related]
3. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.
Jing XY; Li FM
Comput Math Methods Med; 2020; 2020():8894478. PubMed ID: 33029195
[TBL] [Abstract][Full Text] [Related]
4. Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine.
Hayat M; Iqbal N
Comput Methods Programs Biomed; 2014 Oct; 116(3):184-92. PubMed ID: 24997484
[TBL] [Abstract][Full Text] [Related]
5. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.
Ahmad K; Waris M; Hayat M
J Membr Biol; 2016 Jun; 249(3):293-304. PubMed ID: 26746980
[TBL] [Abstract][Full Text] [Related]
6. Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model.
Khan ZU; Hayat M; Khan MA
J Theor Biol; 2015 Jan; 365():197-203. PubMed ID: 25452135
[TBL] [Abstract][Full Text] [Related]
7. iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition.
Arif M; Hayat M; Jan Z
J Theor Biol; 2018 Apr; 442():11-21. PubMed ID: 29337263
[TBL] [Abstract][Full Text] [Related]
8. A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition.
Tripathi P; Pandey PN
J Theor Biol; 2017 Jul; 424():49-54. PubMed ID: 28476562
[TBL] [Abstract][Full Text] [Related]
9. PredHSP: Sequence Based Proteome-Wide Heat Shock Protein Prediction and Classification Tool to Unlock the Stress Biology.
Kumar R; Kumari B; Kumar M
PLoS One; 2016; 11(5):e0155872. PubMed ID: 27195495
[TBL] [Abstract][Full Text] [Related]
10. [Cooperation between heat shock proteins in organizing of proteins spatial structure].
Wyżewski Z; Gregorczyk KP; Szulc-Dąbrowska L; Struzik J; Szczepanowska J; Niemiałtowski M
Postepy Hig Med Dosw (Online); 2014 Jun; 68():793-807. PubMed ID: 24934537
[TBL] [Abstract][Full Text] [Related]
11. Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC.
Khan M; Hayat M; Khan SA; Iqbal N
J Theor Biol; 2017 Feb; 415():13-19. PubMed ID: 27939596
[TBL] [Abstract][Full Text] [Related]
12. Computational genome-wide identification of heat shock protein genes in the bovine genome.
Ajayi OO; Peters SO; De Donato M; Sowande SO; Mujibi FDN; Morenikeji OB; Thomas BN; Adeleke MA; Imumorin IG
F1000Res; 2018; 7():1504. PubMed ID: 30542619
[No Abstract] [Full Text] [Related]
13. Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes.
Zhou XB; Chen C; Li ZC; Zou XY
J Theor Biol; 2007 Oct; 248(3):546-51. PubMed ID: 17628605
[TBL] [Abstract][Full Text] [Related]
14. ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On
Meher PK; Sahu TK; Gahoi S; Rao AR
Front Genet; 2017; 8():235. PubMed ID: 29379521
[TBL] [Abstract][Full Text] [Related]
15. Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition.
Ali F; Hayat M
J Theor Biol; 2015 Nov; 384():78-83. PubMed ID: 26297889
[TBL] [Abstract][Full Text] [Related]
16. Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC.
Javed F; Hayat M
Genomics; 2019 Dec; 111(6):1325-1332. PubMed ID: 30196077
[TBL] [Abstract][Full Text] [Related]
17. [The pleiotropic activity of heat-shock proteins].
Kaźmierczuk A; Kiliańska ZM
Postepy Hig Med Dosw (Online); 2009 Oct; 63():502-21. PubMed ID: 19940329
[TBL] [Abstract][Full Text] [Related]
18. HSPIR: a manually annotated heat shock protein information resource.
R RK; N S N; S P A; Sinha D; Veedin Rajan VB; Esthaki VK; D'Silva P
Bioinformatics; 2012 Nov; 28(21):2853-5. PubMed ID: 22923302
[TBL] [Abstract][Full Text] [Related]
19. Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins.
Chen W; Feng P; Liu T; Jin D
Curr Drug Metab; 2019; 20(3):224-228. PubMed ID: 30378494
[TBL] [Abstract][Full Text] [Related]
20. iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families.
Kabir M; Ahmad S; Iqbal M; Hayat M
Genomics; 2020 Jan; 112(1):276-285. PubMed ID: 30779939
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]