AIMC Topic: Neoplasms

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NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

Bioinformatics (Oxford, England)
MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen. Identifying immunogenic neoantigens is crucial for cancer immunotherapy d...

Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

JCO clinical cancer informatics
PURPOSE: Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (QMs), are difficult to obtain. Natural language processing (NLP) enables efficient quality measurement and is not yet used for children with serious i...

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has drive...

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Clinical and translational science
Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect imp...

Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology.

Studies in health technology and informatics
Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of tr...

Pre-Processing of Categorical Features Within Medical Analysis Systems.

Studies in health technology and informatics
The complexity of the cancer problem domain presents challenges not only to the medical analysis systems tasked with its analysis, but also to the users of such systems. While it is desirable to assist users in operating these medical analysis system...

Comparison of Ensemble Learning Methods for Classification in Cancer Registries.

Studies in health technology and informatics
Significant developments are currently underway in the field of cancer research, particularly in Germany, regarding cancer registration and the use of medical information systems. The use of such systems contributes significantly to quality assurance...

Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study.

Studies in health technology and informatics
With cancer being a leading cause of death globally, epidemiological and clinical cancer registration is paramount for enhancing oncological care and facilitating scientific research. However, the heterogeneous landscape of medical data presents sign...