Cargando…

Predicting protein function by machine learning on amino acid sequences – a critical evaluation

BACKGROUND: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are abl...

Descripción completa

Detalles Bibliográficos
Autores principales:	Al-Shahib, Ali, Breitling, Rainer, Gilbert, David R
Formato:	Texto
Lenguaje:	English
Publicado:	BioMed Central 2007
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1847686/ https://www.ncbi.nlm.nih.gov/pubmed/17374164 http://dx.doi.org/10.1186/1471-2164-8-78

Ejemplares similares

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis
por: You, Zhu-Hong, et al.
Publicado: (2013)

Machine learning integration for predicting the effect of single amino acid substitutions on protein stability
por: Özen, Ayşegül, et al.
Publicado: (2009)

snp-search: simple processing, manipulation and searching of SNPs from high-throughput sequencing
por: Al-Shahib, Ali, et al.
Publicado: (2013)

Predicting protein disorder by analyzing amino acid sequence
por: Yang, Jack Y, et al.
Publicado: (2008)

PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants
por: Gou, Xiangjian, et al.
Publicado: (2022)

Whole genome sequencing of Streptococcus pneumoniae: development, evaluation and verification of targets for serogroup and serotype prediction using an automated pipeline
por: Kapatai, Georgia, et al.
Publicado: (2016)

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition
por: Tamura, Takeyuki, et al.
Publicado: (2007)

Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach
por: Braghetto, Anna, et al.
Publicado: (2023)

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach
por: Mollaei, Parisa, et al.
Publicado: (2023)

Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition
por: Habib, Tanwir, et al.
Publicado: (2008)

Consensus Prediction of Protein Conformational Disorder from Amino Acidic Sequence
por: Kumar, Suresh, et al.
Publicado: (2008)

Predicting DNA-binding sites of proteins from amino acid sequence
por: Yan, Changhui, et al.
Publicado: (2006)

Prediction of RNA-binding amino acids from protein and RNA sequences
por: Choi, Sungwook, et al.
Publicado: (2011)

AMINO ACIDS PREDICT COGNITION BEYOND CLINICAL METABOLIC MARKERS: A MACHINE LEARNING APPROACH
por: Alwerdt, Jessie, et al.
Publicado: (2019)

An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences
por: Hu, Siquan, et al.
Publicado: (2019)

Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs
por: Zhang, Haoran, et al.
Publicado: (2022)

A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences
por: Wang, Xue, et al.
Publicado: (2019)

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction
por: Hirose, Shuichi, et al.
Publicado: (2010)

PrDOS: prediction of disordered protein regions from amino acid sequence
por: Ishida, Takashi, et al.
Publicado: (2007)

A Machine Learning Approach for Identifying Amino Acid Signatures in the HIV Env Gene Predictive of Dementia
por: Holman, Alexander G., et al.
Publicado: (2012)

FOLD-RATE: prediction of protein folding rates from amino acid sequence
por: Gromiha, M. Michael, et al.
Publicado: (2006)

The Use of Orthologous Sequences to Predict the Impact of Amino Acid Substitutions on Protein Function
por: Marini, Nicholas J., et al.
Publicado: (2010)

Prediction of antifreeze proteins using machine learning
por: Khan, Adnan, et al.
Publicado: (2022)

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
por: Chen, Zhen, et al.
Publicado: (2021)

An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features()
por: Campos, Tulio L., et al.
Publicado: (2019)

Machine learning on normalized protein sequences
por: Heider, Dominik, et al.
Publicado: (2011)

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine
por: Giannakoulias, Sam, et al.
Publicado: (2021)

Sequence feature-based prediction of protein stability changes upon amino acid substitutions
por: Teng, Shaolei, et al.
Publicado: (2010)

Classifying COVID-19 based on amino acids encoding with machine learning algorithms
por: Alkady, Walaa, et al.
Publicado: (2022)

Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine
por: Kumar, Ravindra, et al.
Publicado: (2017)

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins
por: Jamal, Salma, et al.
Publicado: (2021)

Distinguishing Proteins From Arbitrary Amino Acid Sequences
por: Yau, Stephen S.-T., et al.
Publicado: (2015)

Variations in Total Protein and Amino Acids in the Sequenced Sorghum Mutant Library
por: Khan, Adil, et al.
Publicado: (2023)

Stochastic deposition of amino acids into microcavities via microparticles
por: Popov, Roman, et al.
Publicado: (2019)

Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model
por: Kumar, Suresh
Publicado: (2017)

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates
por: Sobolev, Boris, et al.
Publicado: (2010)

Predicting Protein–Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids
por: Kuo, Tzu-Hao, et al.
Publicado: (2016)

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach
por: Nedyalkova, Miroslava, et al.
Publicado: (2023)

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes
por: Cadet, Frédéric, et al.
Publicado: (2018)

Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases
por: Samman, Nour, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_pnpbe06ahlngc9blv8g5ekp76h