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@article{2432079, author = {Buosi, Samuele and Timilsina, Mohan and Torrente, Maria and Provencio, Mariano and Fey, Dirk and Nováček, Vít}, article_number = {108398}, doi = {http://dx.doi.org/10.1016/j.compbiomed.2024.108398}, keywords = {Non-small-cell lung cancer; Tumor recurrence prediction; Knowledge graph embedding; Machine learning; Link prediction}, language = {eng}, issn = {0010-4825}, journal = {Computers in Biology and Medicine}, title = {Boosting predictive models and augmenting patient data with relevant genomic and pathway information}, url = {https://www.sciencedirect.com/science/article/pii/S0010482524004827}, volume = {2024}, year = {2024} }
TY - JOUR ID - 2432079 AU - Buosi, Samuele - Timilsina, Mohan - Torrente, Maria - Provencio, Mariano - Fey, Dirk - Nováček, Vít PY - 2024 TI - Boosting predictive models and augmenting patient data with relevant genomic and pathway information JF - Computers in Biology and Medicine VL - 2024 IS - 108398 SP - 1-9 EP - 1-9 PB - Pergamon-Elsevier Science Press SN - 00104825 KW - Non-small-cell lung cancer KW - Tumor recurrence prediction KW - Knowledge graph embedding KW - Machine learning KW - Link prediction UR - https://www.sciencedirect.com/science/article/pii/S0010482524004827 N2 - 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. ER -
BUOSI, Samuele, Mohan TIMILSINA, Maria TORRENTE, Mariano PROVENCIO, Dirk FEY and Vít NOVÁČEK. Boosting predictive models and augmenting patient data with relevant genomic and pathway information. \textit{Computers in Biology and Medicine}. Pergamon-Elsevier Science Press, 2024, vol.~2024, No~108398, p.~1-9. ISSN~0010-4825. Available from: https://dx.doi.org/10.1016/j.compbiomed.2024.108398.
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