Latest AI and machine learning research in colon cancer for healthcare professionals.
Functional genetic screens have uncovered dependencies in many cancers, but experimentally screened models for most cancers are far outnumbered by molecularly-profiled tumors, particularly for rare cancers. We used machine learning to infer gene dependencies from tumor transcriptional profiles, applying our model to the TCGA (11,373 tumors; 28 lineages), rare cancers (1,034 tumors, including 17 ki...
Despite growing evidence implicating cellular senescence in tumor progression, methodological challenges in objectively quantifying senescent cell burden across cancer types continue to limit mechanistic studies and clinical translation. To address this, we developed the Predictive Cellular Senescence Model (PreCSenM), a machine learning-based tool that assigns a CS score by integrating senescence...
Cystic fibrosis (CF) alters gut physiology, yet its impact on microbial communities across colonic regions (ascending, transverse, descending colon) a...
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with early and accurate detection being critical for improving ...
The rational design of high-specificity binders to peptide–HLA (pHLA) complexes remains a major challenge in personalized immunotherapy, particularly ...
Traditional machine learning approaches for text or sequence classification rely on converting textual data into numerical representations. In this st...
Tertiary lymphoid structures (TLS) have been observed in solid tumors and have been associated with better outcomes in patients treated with immunothe...
A comprehensive understanding of cancer progression requires integrating tissue morphol-ogy with spatial molecular profiles. We present SHEST, a multi...
Colorectal cancer (CRC) is one of the most common and deadly cancers worldwide, underscoring the urgent need for novel biomarkers and therapeutic targ...
Complex multilineage organoid systems lack quantitative phenotyping methods preserving spatial architecture at high throughput. Current approaches com...
Applications of artificial intelligence (AI) to histopathology are now common, but most require supervision which inherently limits their scope. By us...
Pancreatic ductal adenocarcinoma (PDAC) lacks reliable prognostic biomarkers. RNA-based signatures suffer from poor reproducibility due to batch effec...
Prostate cancer is the second most common cancer in men across the United States, of which prostate adenocarcinoma (PRAD) is the most common subtype. ...
Due to the late detection, aggressive nature, and paucity of treatment options, pancreatic adenocarcinoma (PAAD) remains one of the most lethal cancer...
Dissecting the heterogeneous response of individual cells towards genetic and chemical perturbations is central to understanding the dynamic functions...
The progression of lung adenocarcinoma (LUAD) from precancerous lesions to invasive carcinoma entails extensive remodeling of tissue architecture and ...
Long non-coding RNAs (lncRNAs) regulate gene expression through chromatin remodeling, transcriptional control, and post-transcriptional modulation, in...
Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images. However, explainable artificial intelligence ap...
Chronic stress induces behavioral rigidity and neural circuit remodeling, yet the underlying computational mechanisms remain unclear. In this study, w...
Deep Learning (DL) has emerged as a powerful tool to predict genetic biomarkers directly from digitized Hematoxylin and Eosin (H&E) slides in colorect...