CPSM: R-package of an Automated Machine Learning Pipeline for Predicting the Survival Probability of Single Cancer Patient DOI Creative Commons
Harpreet Kaur, PrasantaKumar Das, Kevin Camphausen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Accurate survival prediction is vital for optimizing treatment strategies in clinical practice. The advent of high-throughput multi-omics data and computational methods has enabled machine learning (ML) models analysis. However, handling high-dimensional omics remains challenging. This study introduces the Cancer Patient Survival Model (CPSM), an R package developed to provide individualized predictions through a fully integrated reproducible pipeline. CPSM encompasses nine modules that streamline modeling workflow, organized into four key stages: (1) Data Preprocessing Normalization, (2) Feature Selection, (3) Prediction Development, (4) Visualization. visual tools facilitate interpretation predictions, enhancing decision-making. By providing end-to-end solution integration analysis, not only enhances precision but also aids discovering clinically relevant biomarkers. Availability Implementation Package freely available at GitHub URL: https://github.com/hks5august/CPSM

Language: Английский

Unraveling the complexities of colorectal cancer and its promising therapies – An updated review DOI

Sayan Saha,

Shreya Ghosh, Suman Ghosh

et al.

International Immunopharmacology, Journal Year: 2024, Volume and Issue: 143, P. 113325 - 113325

Published: Oct. 14, 2024

Language: Английский

Citations

7
Enhancing informed consent in oncological surgery through digital platforms and artificial intelligence DOI Creative Commons
Alex Boddy

Clinical Surgical Oncology, Journal Year: 2025, Volume and Issue: unknown, P. 100080 - 100080

Published: March 1, 2025

Language: Английский

Citations

0
CPSM: R-package of an Automated Machine Learning Pipeline for Predicting the Survival Probability of Single Cancer Patient DOI Creative Commons
Harpreet Kaur, PrasantaKumar Das, Kevin Camphausen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Accurate survival prediction is vital for optimizing treatment strategies in clinical practice. The advent of high-throughput multi-omics data and computational methods has enabled machine learning (ML) models analysis. However, handling high-dimensional omics remains challenging. This study introduces the Cancer Patient Survival Model (CPSM), an R package developed to provide individualized predictions through a fully integrated reproducible pipeline. CPSM encompasses nine modules that streamline modeling workflow, organized into four key stages: (1) Data Preprocessing Normalization, (2) Feature Selection, (3) Prediction Development, (4) Visualization. visual tools facilitate interpretation predictions, enhancing decision-making. By providing end-to-end solution integration analysis, not only enhances precision but also aids discovering clinically relevant biomarkers. Availability Implementation Package freely available at GitHub URL: https://github.com/hks5august/CPSM

Language: Английский

Citations

0