Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology

Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology

10.2174/157489307780618222

Saved in:

Bibliographic Details
Main Authors:	Cui, J., Han, L., Lin, H., Tang, Z., Ji, Z., Cao, Z., Li, Y., Chen, Y.
Other Authors:	PHARMACY
Format:	Review
Published:	2014
Subjects:	Machine learning method Neural network Peptide prediction Protein function prediction Protein-protein interaction Support vector machine
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/106607
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	National University of Singapore

Similar Items

Homology-free prediction of functional class of proteins and peptides by support vector machines
by: Zhu, F., et al.
Published: (2014)

Prediction of pathogenic and antigenic proteins and peptides by machine learning approach
by: CUI JUAN
Published: (2019)

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity
by: Lin, H.H., et al.
Published: (2014)

Prediction of functional class of novel plant proteins by a statistical learning method
by: Han, L.Y., et al.
Published: (2014)

Enzyme Family Classification by Support Vector Machines
by: Cai, C.Z., et al.
Published: (2014)

Effect of training datasets on support vector machine prediction of protein-protein interactions
by: Lo, S.L., et al.
Published: (2014)

Protein function and inhibitor prediction by statistical learning approach
by: HAN LIANYI
Published: (2010)

Protein function prediction via protein-protein interaction - a Support Vector Machine approach
by: LO SIAW LING
Published: (2010)

EnsembleFam: towards more accurate protein family prediction in the twilight zone
by: Kabir, MN, et al.
Published: (2023)

Prediction of functional class of the SARS coronavirus proteins by a statistical learning method
by: Cai, C.Z., et al.
Published: (2014)

Effect of training datasets on support vector machine prediction of protein-protein interactions
by: LO, Siaw Ling, et al.
Published: (2005)

Prediction of ligand-binding proteins and their inhibitors: machine learning approach
by: LIN HONGHUANG
Published: (2014)

Support vector machine classification of physical and biological datasets
by: Cai, C.-Z., et al.
Published: (2014)

Remote homolog detection using local sequence-structure correlations
by: Hou, Y., et al.
Published: (2013)

Prediction of protein allergenicity using local description of amino acid sequence
by: Tong, J.C., et al.
Published: (2014)

Improving protein solubility and activity by introducing small peptide tags designed with machine learning models
by: Han, X., et al.
Published: (2021)

TOWARDS MORE ACCURATE PROTEIN FUNCTION PREDICTION IN THE TWILIGHT ZONE
by: MOHAMMAD NEAMUL KABIR
Published: (2023)

Prediction of transporter family from protein sequence by support vector machine approach
by: Lin, H.H., et al.
Published: (2014)

Predictive analytics for biomineralization peptide binding affinity
by: Janairo, Jose Isagani B.
Published: (2019)

Protein function classification via support vector machine approach
by: Cai, C.Z., et al.
Published: (2014)

Machine learning-based prediction of survival prognosis in cervical cancer
by: Ding, Dongyan, et al.
Published: (2022)

Prediction of functional class of novel viral proteins by a statistical learning method irrespective of sequence similarity
by: Han, L.Y., et al.
Published: (2014)

Application of support vector machine to prediction of cross-reactive allergens and recognition of viral sequences
by: MUH HON CHENG
Published: (2010)

Prediction of RNA-binding proteins from primary sequence by a support vector machine approach
by: Han, L.Y., et al.
Published: (2014)

PREDICTIVE MODELS TO DISCOVER NOVEL RNA-BINDING PROTEINS
by: JIN WENHAO
Published: (2018)

Prediction of torsade-causing potential of drugs by support vector machine approach
by: Yap, C.W., et al.
Published: (2014)

An HV-SVM classifier to infer TF-TF interactions using protein domains and GO annotations
by: Li, X.-L., et al.
Published: (2014)

Splice Site Prediction Using Support Vector Machines with a Bayes Kernel
by: ZHANG, Ya, et al.
Published: (2006)

Prediction of novel biochemical class disease related proteins and microRNAs by machine learning approach
by: ZHANG HAILEI
Published: (2010)

More Than 1,001 problems with protein domain databases: Transmembrane regions, signal peptides and the issue of sequence homology
by: Wong W.-C., et al.
Published: (2020)

Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
by: Le, N.Q.K., et al.
Published: (2021)

Methods to Improve Virtual Screening of Potential Drug Leads for Specific Pharmacodynamic and Toxicological Properties
by: LIEW CHIN YEE
Published: (2012)

Prediction of relatedness in stack overflow: Deep learning vs. SVM: A reproducibility study
by: XU, Bowen, et al.
Published: (2018)

Learning the drug target-likeness of a protein
by: Xu, H., et al.
Published: (2014)

Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties
by: Cui, J., et al.
Published: (2014)

Applying support vector machines to predict building energy consumption in tropical region
by: Dong, B., et al.
Published: (2014)

Enlarging the terminal region of nonlinear model predictive control using the support vector machine method
by: Ong, C.J., et al.
Published: (2014)

Probabilistic prediction of protein-protein interactions from the protein sequences
by: Chinnasamy, A., et al.
Published: (2013)

QSAR and predictors of eye and skin effects
by: Liew, C.Y., et al.
Published: (2014)

Flow categorization model for improving forecasting
by: Sivapragasam, C., et al.
Published: (2014)