Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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Artificial Intelligence, Physiological Genomics, and Precision Medicine.Ensuring the Safety and Security of Frozen Lung Cancer Tissue Collections through the Encapsulation of Dried DNA.

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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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http://www.wikidata.org/entity/Q47173069

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2018 nî lūn-bûn

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2018年の論文

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年學術文章

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2018年學術文章

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name

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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Applications of Support Vector Machine

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type

Item

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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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Applications of Support Vector Machine

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prefLabel

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

@en

Applications of Support Vector Machine

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P1476

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

@en

P2093

Nianguang Cai

http://www.w3.org/2001/XMLSchema#string

Pedro Penzuti Pacheco

http://www.w3.org/2001/XMLSchema#string

Shavira Narrandes

http://www.w3.org/2001/XMLSchema#string

Shujun Huang

http://www.w3.org/2001/XMLSchema#string

Wayne Xu

http://www.w3.org/2001/XMLSchema#string

Yang Wang

http://www.w3.org/2001/XMLSchema#string

P2860

Cancer genome landscapes

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries

Potential Compounds for Oral Cancer Treatment: Resveratrol, Nimbolide, Lovastatin, Bortezomib, Vorinostat, Berberine, Pterostilbene, Deguelin, Andrographolide, and Colchicine

A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine

Analysis of molecular profile data using generative and discriminative methods.

Nonparametric methods for identifying differentially expressed genes in microarray data.

A pilot study on the application of statistical classification procedures to molecular epidemiological data.

A spline function approach for detecting differentially expressed genes in microarray data analysis.

Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer.

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41-51

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scholarly article

review article

P356

10.21873/CGP.20063

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P433

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P478

http://www.w3.org/2001/XMLSchema#string

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2018-01-01T00:00:00Z

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29275361

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machine learning

support vector machine

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5822181

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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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P2860

P2860

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P577

P698

P921

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P577

P698

P921

P932