Balancing Predictive Relevance of Ligand Biochemical Activities

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Balancing Predictive Relevance of Ligand Biochemical Activities

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Balancing Predictive Relevance of Ligand Biochemical Activities

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

Počet stran výsledku

10

Strana od-do

338-348

Název nakladatele

Springer International Publishing AG

Místo vydání

Cham

Místo konání akce

Warsaw

Datum konání akce

10. 12. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000775291100026