AIMC Topic: Colorectal Neoplasms

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Optimization of deep learning models for inference in low resource environments.

Computers in biology and medicine
Artificial Intelligence (AI), and particularly deep learning (DL), has shown great promise to revolutionize healthcare. However, clinical translation is often hindered by demanding hardware requirements. In this study, we assess the effectiveness of ...

Ferroptosis-disulfidptosis-related CHMP6 is a clinico-immune target in colorectal cancer.

Biology direct
BACKGROUND: Ferroptosis and disulfidptosis are newly discovered forms of regulated cell death that play critical roles in cancer progression, metabolism, and immune evasion. However, their interplay and combined influence on colorectal cancer (CRC) p...

Nanopore full length 16S rRNA gene sequencing increases species resolution in bacterial biomarker discovery.

Scientific reports
Discovery of disease-related bacterial biomarkers could be a useful approach for early prevention or diagnosis of various afflictions, such as colorectal cancer. This typically involves analyzing small regions of the 16S rRNA gene (e.g. V3V4) through...

Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: With the widespread application of machine learning (ML) in the diagnosis and treatment of colorectal cancer (CRC), some studies have investigated the use of ML techniques for the diagnosis of KRAS (Kirsten rat sarcoma) mutation. Neverthe...

Cuproptosis induced by curcumin interfering with proliferation and energy metabolism in colorectal cancer: 3D tumor model and computational simulations reveal curcumin inhibition of HSPD1 and CALCOCO2.

European journal of pharmacology
PURPOSE: Colorectal cancer is a highly aggressive malignancy characterized by complex tumor micro-environments and significant drug resistance. We highlighted the critical role of curcumin in inhibiting tumor growth and migration, inducing cuproptosi...

Machine learning to evaluate the effects of non-clinical social determinant features in predicting colorectal Cancer mortality in a medically underserved Appalachian population.

Scientific reports
Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States (US). Rural Appalachia suffers the highest CRC incidence and mortality rates. There are several non-clinical health-related social determinant factors (SDOH) associ...

Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.

Scientific reports
Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses. The primary objective of our investigation was to develop a machin...

Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.

Scientific reports
Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosi...

AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.

Scientific reports
The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying novel anticancer targets and developing new drugs. Computational approaches can significantly quicken r...