Boosting predictive models and augmenting patient data with relevant genomic and pathway information

BUOSI, Samuele; Mohan TIMILSINA; Maria TORRENTE; Mariano PROVENCIO; Dirk FEY a Vít NOVÁČEK. Boosting predictive models and augmenting patient data with relevant genomic and pathway information. Computers in Biology and Medicine. Pergamon-Elsevier Science Press, 2024, roč. 174, č. 108398, s. 1-9. ISSN 0010-4825. Dostupné z: https://dx.doi.org/10.1016/j.compbiomed.2024.108398.

Základní údaje

Originální název

Boosting predictive models and augmenting patient data with relevant genomic and pathway information

Autoři

BUOSI, Samuele; Mohan TIMILSINA; Maria TORRENTE; Mariano PROVENCIO; Dirk FEY a Vít NOVÁČEK (203 Česká republika, garant, domácí)

Vydání

Computers in Biology and Medicine, Pergamon-Elsevier Science Press, 2024, 0010-4825

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Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 7.000 v roce 2023

Kód RIV

RIV/00216224:14330/24:00137106

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1016/j.compbiomed.2024.108398

EID Scopus

2-s2.0-85189940313

Klíčová slova anglicky

Non-small-cell lung cancer; Tumor recurrence prediction; Knowledge graph embedding; Machine learning; Link prediction

Štítky

Artificial Intelligence, machine learning, medical informatics

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

V originále

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and diverse patient responses to treatments. Personalized care and patient outcomes heavily rely on early relapse identification, yet current predictive models, despite their potential, lack comprehensive genetic data. This inadequacy fuels our research focus—integrating specific genetic information, such as pathway scores, into clinical data. Our aim is to refine machine learning models for more precise relapse prediction in early-stage non-small cell lung cancer. To address the scarcity of genetic data, we employ imputation techniques, leveraging publicly available datasets such as The Cancer Genome Atlas (TCGA), integrating pathway scores into our patient cohort from the Cancer Long Survivor Artificial Intelligence Follow-up (CLARIFY) project. Through the integration of imputed pathway scores from the TCGA dataset with clinical data, our approach achieves notable strides in predicting relapse among a held-out test set of 200 patients. By training machine learning models on enriched knowledge graph data, inclusive of triples derived from pathway score imputation, we achieve a promising precision of 82% and specificity of 91%. These outcomes highlight the potential of our models as supplementary tools within tumour, node, and metastasis (TNM) classification systems, offering improved prognostic capabilities for lung cancer patients. In summary, our research underscores the significance of refining machine learning models for relapse prediction in early-stage non-small cell lung cancer. Our approach, centered on imputing pathway scores and integrating them with clinical data, not only enhances predictive performance but also demonstrates the promising role of machine learning in anticipating relapse and ultimately elevating patient outcomes.