Learning with multiple pairwise kernels for drug bioactivity prediction.

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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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年學術文章

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2018年學術文章

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2018年學術文章

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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Learning with multiple pairwise kernels for drug bioactivity prediction.

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P1476

Learning with multiple pairwise kernels for drug bioactivity prediction.

@en

P2093

Antti Airola

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Heli Julkunen

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Juho Rousu

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Markus Heinonen

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Sandor Szedmak

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Tapio Pahikkala

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P2860

Identification of common molecular subsequences

Prediction of drug-target interactions and drug repositioning via network-based inference

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

A community effort to assess and improve drug sensitivity prediction algorithms.

Exploring chemical space for drug discovery using the chemical universe database.

Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay.

Key factors for successful data integration in biomarker research.

Multi-omic data integration enables discovery of hidden biological regularities.

Chemical substructures that enrich for biological activity

Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties

P304

i509-i518

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scholarly article

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10.1093/BIOINFORMATICS/BTY277

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P407

English

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P478

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P50

Tero Aittokallio

Anna Cichonska

P577

2018-07-01T00:00:00Z

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P698

29949975

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P932

6022556

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