Development and Optimization of a Multi-Label SVM for Chemogenomics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F19%3A10243313" target="_blank" >RIV/61989100:27740/19:10243313 - isvavai.cz</a>
Result on the web
<a href="https://zenodo.org/record/2809567" target="_blank" >https://zenodo.org/record/2809567</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Development and Optimization of a Multi-Label SVM for Chemogenomics
Original language description
Support vector machine (SVM) based machine learning is used in a wide range of domains. It represents a family of supervised machine learning algorithms and is most commonly used for binary classification tasks. It can also be extended to multi-label problems which are specializations of multi-task classification. We use an early stage SVM implementation, called PermonSVM, to implement a one versus all multi-label method to classify and predict protein-compound activities in chemogenomics. The white paper highlights the VI-HPS tools Score-P, Cube and Vampir, as used during the early development and improvement processes of PermonSVM. We apply those tools to identify and analyze a bottleneck in the early PermonSVM implementation, and verify its final iteration.
Czech name
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Czech description
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Classification
Type
V<sub>souhrn</sub> - Summary research report
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Number of pages
12
Place of publication
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Publisher/client name
PRACE - Partnership for Advanced Computing in Europe.
Version
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