Sparse Omics-network Regularization to Increase Interpretability and Performance of SVM-based Predictive Models

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sparse Omics-network Regularization to Increase Interpretability and Performance of SVM-based Predictive Models

Popis výsledku v původním jazyce

To fully profit from development of high-throughput omics technologies, there is a strict need for accurate, stable and comprehensible biomarkers. The biomarkers are features of mostly molecular character, which enable to predict end interpret the individual?s state. However, the task of highthroughput data analysis is still challenging. Small sample size together large feature space often causes overfitting. Next, resulting model are difficult to interpret due to complex nature of omics processes. In this paper we propose a framework for effective implementation of large scale optimization problem within machine learning complex. The core algorithm is intended to improve SVM based linear models of gene expression as to the accuracy and especially thecomprehensibility. The algorithm, called SNSVM, uses regularization to achieve these objectives. The regularization is implemented through prior known feature interactions and additional sparsity term. The results suggest that prior knowl

Název v anglickém jazyce

Sparse Omics-network Regularization to Increase Interpretability and Performance of SVM-based Predictive Models

Popis výsledku anglicky

To fully profit from development of high-throughput omics technologies, there is a strict need for accurate, stable and comprehensible biomarkers. The biomarkers are features of mostly molecular character, which enable to predict end interpret the individual?s state. However, the task of highthroughput data analysis is still challenging. Small sample size together large feature space often causes overfitting. Next, resulting model are difficult to interpret due to complex nature of omics processes. In this paper we propose a framework for effective implementation of large scale optimization problem within machine learning complex. The core algorithm is intended to improve SVM based linear models of gene expression as to the accuracy and especially thecomprehensibility. The algorithm, called SNSVM, uses regularization to achieve these objectives. The regularization is implemented through prior known feature interactions and additional sparsity term. The results suggest that prior knowl

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 19th International Scientific Student Conferenece POSTER 2015

ISBN

978-80-01-05499-4

ISSN

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e-ISSN

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Počet stran výsledku

1

Strana od-do

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Název nakladatele

Czech Technical University in Prague

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

14. 5. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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