AIMC Topic: Neoplasms

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In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models.

Proceedings of the National Academy of Sciences of the United States of America
There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein...

Artificial intelligence in cancer diagnosis: Opportunities and challenges.

Pathology, research and practice
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large v...

ChatGPT as an aid for pathological diagnosis of cancer.

Pathology, research and practice
Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of arti...

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language.

BMC medical informatics and decision making
PURPOSE: Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persi...

Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method.

Journal of computational biology : a journal of computational molecular cell biology
Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cel...

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.

Journal of hematology & oncology
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highl...

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Journal of hazardous materials
Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessme...

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures.

Computers in biology and medicine
Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achi...

Using published pathway figures in enrichment analysis and machine learning.

BMC genomics
Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in...

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

Neuroradiology
PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications i...