107 related articles for article (PubMed ID: 32975233)
1. VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques.
Yadav AK; Singla D
J Biosci; 2020; 45():. PubMed ID: 32975233
[TBL] [Abstract][Full Text] [Related]
2. Identification of plant vacuole proteins by exploiting deep representation learning features.
Jiao S; Zou Q
Comput Struct Biotechnol J; 2022; 20():2921-2927. PubMed ID: 35765653
[TBL] [Abstract][Full Text] [Related]
3. SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models.
Wang X; Li C; Li F; Sharma VS; Song J; Webb GI
BMC Bioinformatics; 2019 Nov; 20(1):602. PubMed ID: 31752668
[TBL] [Abstract][Full Text] [Related]
4. Positive-unlabelled learning of glycosylation sites in the human proteome.
Li F; Zhang Y; Purcell AW; Webb GI; Chou KC; Lithgow T; Li C; Song J
BMC Bioinformatics; 2019 Mar; 20(1):112. PubMed ID: 30841845
[TBL] [Abstract][Full Text] [Related]
5. SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments.
Savojardo C; Martelli PL; Fariselli P; Casadio R
Bioinformatics; 2017 Feb; 33(3):347-353. PubMed ID: 28172591
[TBL] [Abstract][Full Text] [Related]
6. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.
Wu ZC; Xiao X; Chou KC
Mol Biosyst; 2011 Dec; 7(12):3287-97. PubMed ID: 21984117
[TBL] [Abstract][Full Text] [Related]
7. An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.
Liu ML; Su W; Guan ZX; Zhang D; Chen W; Liu L; Ding H
Curr Protein Pept Sci; 2020; 21(12):1229-1241. PubMed ID: 31957607
[TBL] [Abstract][Full Text] [Related]
8. LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.
Feng S; Liang Y; Du W; Lv W; Li Y
Int J Mol Sci; 2020 Oct; 21(19):. PubMed ID: 33019721
[TBL] [Abstract][Full Text] [Related]
9. PluriPred: AWeb server for predicting proteins involved in pluripotent network.
Mandal SD; Saha S
J Biosci; 2016 Dec; 41(4):743-750. PubMed ID: 27966493
[TBL] [Abstract][Full Text] [Related]
10. Protein subcellular localization prediction using multiple kernel learning based support vector machine.
Hasan MA; Ahmad S; Molla MK
Mol Biosyst; 2017 Mar; 13(4):785-795. PubMed ID: 28247893
[TBL] [Abstract][Full Text] [Related]
11. Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor.
Saravanan V; Gautham N
OMICS; 2015 Oct; 19(10):648-58. PubMed ID: 26406767
[TBL] [Abstract][Full Text] [Related]
12. Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning.
Mei S
J Theor Biol; 2012 Oct; 310():80-7. PubMed ID: 22750634
[TBL] [Abstract][Full Text] [Related]
13. SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.
Suresh V; Parthasarathy S
Protein Pept Lett; 2014; 21(8):736-42. PubMed ID: 23855661
[TBL] [Abstract][Full Text] [Related]
14. A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search.
Garg A; Raghava GP
In Silico Biol; 2008; 8(2):129-40. PubMed ID: 18928201
[TBL] [Abstract][Full Text] [Related]
15. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.
Zhang S; Liang Y
J Theor Biol; 2018 Nov; 457():163-169. PubMed ID: 30179589
[TBL] [Abstract][Full Text] [Related]
16. MSLVP: prediction of multiple subcellular localization of viral proteins using a support vector machine.
Thakur A; Rajput A; Kumar M
Mol Biosyst; 2016 Jul; 12(8):2572-86. PubMed ID: 27272007
[TBL] [Abstract][Full Text] [Related]
17. PSCL: predicting protein subcellular localization based on optimal functional domains.
Wang K; Hu LL; Shi XH; Dong YS; Li HP; Wen TQ
Protein Pept Lett; 2012 Jan; 19(1):15-22. PubMed ID: 21919864
[TBL] [Abstract][Full Text] [Related]
18. NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families.
Kumar R; Kumari B; Srivastava A; Kumar M
Sci Rep; 2014 Oct; 4():6810. PubMed ID: 25351274
[TBL] [Abstract][Full Text] [Related]
19. Bioinformatics Analysis of Protein Secretion in Plants.
Chen L
Methods Mol Biol; 2017; 1662():33-43. PubMed ID: 28861815
[TBL] [Abstract][Full Text] [Related]
20. An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.
Xia J; Peng Z; Qi D; Mu H; Yang J
Bioinformatics; 2017 Mar; 33(6):863-870. PubMed ID: 28039166
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]