Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features

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Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features

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im Januar 2018 veröffentlichter wissenschaftlicher Artikel

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wetenschappelijk artikel

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наукова стаття, опублікована у 2018

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Prediction of Antitubercular P ...... Classifier and Hybrid Features

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Prediction of Antitubercular P ...... Classifier and Hybrid Features

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Prediction of Antitubercular P ...... Classifier and Hybrid Features

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Prediction of Antitubercular P ...... Classifier and Hybrid Features

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Prediction of Antitubercular P ...... Classifier and Hybrid Features

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Prediction of Antitubercular P ...... Classifier and Hybrid Features

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P1476

Prediction of Antitubercular P ...... Classifier and Hybrid Features

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P2093

Gajendra P S Raghava

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Salman Sadullah Usmani

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Sherry Bhalla

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P2860

Tuberculosis: current situation, challenges and overview of its control programs in India

Lassomycin, a Ribosomally Synthesized Cyclic Peptide, Kills Mycobacterium tuberculosis by Targeting the ATP-Dependent Protease ClpC1P1P2

In silico approach for predicting toxicity of peptides and proteins

Nine- to Twelve-Month Anti-Tuberculosis Treatment Is Associated with a Lower Recurrence Rate than 6-9-Month Treatment in Human Immunodeficiency Virus-Infected Patients: A Retrospective Population-Based Cohort Study in Taiwan

The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000.

Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms

Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.

Analysis and prediction of antibacterial peptides.

Mycobacterium tuberculosis complex DNA from an extinct bison dated 17,000 years before the present.

The cyclic peptide ecumicin targeting ClpC1 is active against Mycobacterium tuberculosis in vivo.

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954

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scholarly article

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10.3389/FPHAR.2018.00954

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English

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P577

2018-01-01T00:00:00Z

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30210341

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P921

Farmakologi

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6121089

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