Development and Optimization of a Multi-Label SVM for Chemogenomics

Kód výsledku v 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>

Výsledek na webu

<a href="https://zenodo.org/record/2809567" target="_blank" >https://zenodo.org/record/2809567</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Development and Optimization of a Multi-Label SVM for Chemogenomics

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Development and Optimization of a Multi-Label SVM for Chemogenomics

Popis výsledku anglicky

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.

Druh

V_souhrn - Souhrnná výzkumná zpráva

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Počet stran výsledku

12

Místo vydání

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Název nakladatele resp. objednatele

PRACE - Partnership for Advanced Computing in Europe.

Verze

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