A novel feature ranking method for prediction of cancer stages using proteomics data.

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A novel feature ranking method for prediction of cancer stages using proteomics data.

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2017 nî lūn-bûn

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2017年論文

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2017年論文

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2017年論文

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A novel feature ranking method for prediction of cancer stages using proteomics data.

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A novel feature ranking method for prediction of cancer stages using proteomics data.

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A novel feature ranking method for prediction of cancer stages using proteomics data.

@en

A novel feature ranking method for prediction of cancer stages using proteomics data.

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A novel feature ranking method for prediction of cancer stages using proteomics data.

@en

A novel feature ranking method for prediction of cancer stages using proteomics data.

@nl

P1476

A novel feature ranking method for prediction of cancer stages using proteomics data

@en

P2093

Ehsan Saghapour

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P2860

Random Forests

Gastric cancer: prevention, risk factors and treatment

iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition

iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition

iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins

Comparison of the predicted and observed secondary structure of T4 phage lysozyme

Hidden Markov models

Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition.

Immunosignature system for diagnosis of cancer.

TCPA: a resource for cancer functional proteomics data

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e0184203

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scholarly article

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10.1371/JOURNAL.PONE.0184203

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English

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P478

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Mohammadreza Sehhati

S Kermani

P577

2017-09-21T00:00:00Z

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P698

28934234

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5608217

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