PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning

Abstract Background Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meanin...

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Autores principales:	Ayyüce Begüm Bektaş, Mehmet Gönen
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Machine learning Multiple kernel learning Support vector regression Gene set analysis Cancer biology Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/3bdf0d9723b14774a875cc2b5b3bfcca
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spelling	oai:doaj.org-article:3bdf0d9723b14774a875cc2b5b3bfcca2021-11-07T12:22:22ZPrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning10.1186/s12859-021-04460-61471-2105https://doaj.org/article/3bdf0d9723b14774a875cc2b5b3bfcca2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04460-6https://doaj.org/toc/1471-2105Abstract Background Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. Results In this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. Conclusions PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.Ayyüce Begüm BektaşMehmet GönenBMCarticleMachine learningMultiple kernel learningSupport vector regressionGene set analysisCancer biologyComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Multiple kernel learning Support vector regression Gene set analysis Cancer biology Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Machine learning Multiple kernel learning Support vector regression Gene set analysis Cancer biology Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Ayyüce Begüm Bektaş Mehmet Gönen PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
description	Abstract Background Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. Results In this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. Conclusions PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.
format	article
author	Ayyüce Begüm Bektaş Mehmet Gönen
author_facet	Ayyüce Begüm Bektaş Mehmet Gönen
author_sort	Ayyüce Begüm Bektaş
title	PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
title_short	PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
title_full	PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
title_fullStr	PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
title_full_unstemmed	PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
title_sort	prognosit: pathway/gene set-based tumour volume prediction using multiple kernel learning
publisher	BMC
publishDate	2021
url	https://doaj.org/article/3bdf0d9723b14774a875cc2b5b3bfcca
work_keys_str_mv	AT ayyucebegumbektas prognositpathwaygenesetbasedtumourvolumepredictionusingmultiplekernellearning AT mehmetgonen prognositpathwaygenesetbasedtumourvolumepredictionusingmultiplekernellearning
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PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning

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