AIMC Topic: Neoplasms

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address th...

Physician and Artificial Intelligence Chatbot Responses to Cancer Questions From Social Media.

JAMA oncology
IMPORTANCE: Artificial intelligence (AI) chatbots pose the opportunity to draft template responses to patient questions. However, the ability of chatbots to generate responses based on domain-specific knowledge of cancer remains to be tested.

A case of robot-assisted resection for cecum cancer with anomalous venous confluence.

Asian journal of endoscopic surgery
There are many reports on the positional relationship between the ileocolic artery and superior mesenteric vein (SMV). However, there have been no reports of anomalous venous confluence in the ileocecal vessel area. A 69-year-old man was diagnosed wi...

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

Bioinformatics (Oxford, England)
MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate dr...

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and ...

Study of prognostic splicing factors in cancer using machine learning approaches.

Human molecular genetics
Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing ...

Artificial intelligence.

Cancer cell
Experts discuss the challenges and opportunities of using artificial intelligence (AI) to study the evolution of cancer cells and their microenvironment, improve diagnosis, predict treatment response, and ensure responsible implementation in the clin...

How Artificial Intelligence Unravels the Complex Web of Cancer Drug Response.

Cancer research
The intersection of precision medicine and artificial intelligence (AI) holds profound implications for cancer treatment, with the potential to significantly advance our understanding of drug responses based on the intricate architecture of tumor cel...