Balancing Predictive Relevance of Ligand Biochemical Activities
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F22%3A00565234" target="_blank" >RIV/68145535:_____/22:00565234 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-95929-6_26" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-95929-6_26</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-95929-6_26" target="_blank" >10.1007/978-3-030-95929-6_26</a>
Alternative languages
Result language
angličtina
Original language name
Balancing Predictive Relevance of Ligand Biochemical Activities
Original language description
In this paper, we present a technique for balancing predictive relevance models related to supervised modelling ligand biochemical activities to biological targets. We train uncalibrated models employing conventional supervised machine learning technique, namely Support Vector Machines. Unfortunately, SVMs have a serious drawback. They are sensitive to imbalanced datasets, outliers and high multicollinearity among training samples, which could be a cause of preferencing one group over another. Thus, an additional calibration could be required for balancing a predictive relevance of models. As a technique for this balancing, we propose the Platt’s scaling. The achieved results were demonstrated on single-target models trained on datasets exported from the ExCAPE database. Unlike traditional used machine techniques, we focus on decreasing uncertainty employing deterministic solvers.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Uncertainty and impresision in decision making and decision support: new advances, challenges, and perspectives
ISBN
978-303095928-9
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
10
Pages from-to
338-348
Publisher name
Springer International Publishing AG
Place of publication
Cham
Event location
Warsaw
Event date
Dec 10, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000775291100026