J 2024

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

BUOSI, Samuele, Mohan TIMILSINA, Maria TORRENTE, Mariano PROVENCIO, Dirk FEY et. al.

Basic information

Original name

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

Authors

BUOSI, Samuele, Mohan TIMILSINA, Maria TORRENTE, Mariano PROVENCIO, Dirk FEY and Vít NOVÁČEK (203 Czech Republic, guarantor, belonging to the institution)

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 7.700 in 2022

Organization unit

Faculty of Informatics

DOI

Keywords in English

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

Tags

Tags

International impact, Reviewed
Změněno: 13/9/2024 09:55, doc. Mgr. Bc. Vít Nováček, PhD

Abstract

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.