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

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 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

Язык: Английский

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

Sayan Saha,

Shreya Ghosh, Suman Ghosh

и другие.

International Immunopharmacology, Год журнала: 2024, Номер 143, С. 113325 - 113325

Опубликована: Окт. 14, 2024

Язык: Английский

Процитировано

8
Enhancing informed consent in oncological surgery through digital platforms and artificial intelligence DOI Creative Commons
Alex Boddy

Clinical Surgical Oncology, Год журнала: 2025, Номер unknown, С. 100080 - 100080

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

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

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 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

Язык: Английский

Процитировано

0