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Results for
""Sequence Analysis, Protein""[mesh_terms]

613 Results

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Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.

Tayyaba Asif, Muhammad Nabeel Asim, Faiza Hassan, Andreas Dengel

Database : the journal of biological databases and curation 2025;2025():

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pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC.

Xiang Cheng, Xuan Xiao, Kuo-Chen Chou

Genomics 2018;110(1):50-58

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A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry.

T Chen, M Y Kao, M Tepel, J Rush, G M Church

Journal of computational biology : a journal of computational molecular cell biology 2001;8(3):325-37

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Recognition of Protein Pupylation Sites by Adopting Resampling Approach.

Tao Li, Yan Chen, Taoying Li, Cangzhi Jia

Molecules (Basel, Switzerland) 2019;23(12):3097

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The alkylated thiohydantoin method for C-terminal sequence analysis.

D R Dupont, M Bozzini, V L Boyd

EXS 2000;88():119-31

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Hydrophobicity profiles for protein sequence analysis.

W J Metzler, S R Krystek, J Novotny

Current protocols in protein science 2008;Chapter 2():Unit2.2

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Lipoprotein computational prediction in spirochaetal genomes.

David A Haake, James Matsunaga, Marcelo Reis, João C Setubal

Microbiology (Reading, England) 2006;152(Pt 1):113-121

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Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.

Zong Dai, Xuan Zhou, Zhanchao Li, Xiaoyong Zou

BMC bioinformatics 2010;11():325

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Compositionally Biased Dark Matter in the Protein Universe.

Paul M Harrison

Proteomics 2019;18(21-22):e1800069

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FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

M E Shiri, Mehdi Ghatee, Elham Abbasi

Computers in biology and medicine 2014;43(9):1182-91

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A computational method to predict carbonylation sites in yeast proteins.

R L Liu, J G Zheng, J Q Han, J Liu, H Q Lv

Genetics and molecular research : GMR 2017;15(2):

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pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

Jianhua Jia, Liuxia Zhang, Zi Liu, Xuan Xiao, Kuo-Chen Chou

Bioinformatics (Oxford, England) 2017;32(20):3133-3141

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Exploring the Power of Machine Learning in Analysing Protein-Protein Sequences.

Anindya Nag, Riya Sil, Md Mehedi Hassan, Tamanna Haque Ritu, Biva Das, Nilanjana Roy, Anurag Sinha, Dron Guin, Nishchal Maurya

IET systems biology 2026;20(1):e70066

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Biological function in the twilight zone of sequence conservation.

Chris P Ponting

BMC biology 2018;15(1):71

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Sequence-based analysis of protein degradation rates.

Miguel Correa Marrero, Aalt D J van Dijk, Dick de Ridder

Proteins 2017;85(9):1593-1601

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Results: 1 – 15 / 613

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Results for ""Sequence Analysis, Protein""[mesh_terms]

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.

pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC.

A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry.

Recognition of Protein Pupylation Sites by Adopting Resampling Approach.

The alkylated thiohydantoin method for C-terminal sequence analysis.

Hydrophobicity profiles for protein sequence analysis.

Lipoprotein computational prediction in spirochaetal genomes.

Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.

Compositionally Biased Dark Matter in the Protein Universe.

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

A computational method to predict carbonylation sites in yeast proteins.

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

Exploring the Power of Machine Learning in Analysing Protein-Protein Sequences.

Biological function in the twilight zone of sequence conservation.

Sequence-based analysis of protein degradation rates.

Results for
""Sequence Analysis, Protein""[mesh_terms]