AIMC Topic: Rare Diseases

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Integrating Machine Learning-Based Approaches into the Design of ASO Therapies.

Genes
Rare diseases impose a significant burden on affected individuals, caregivers, and healthcare systems worldwide. Developing effective therapeutics for these small patient populations presents substantial challenges. Antisense oligonucleotides (ASOs) ...

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

International journal of computer assisted radiology and surgery
PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to ...

Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.

PloS one
Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the var...

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.

JMIR medical informatics
BACKGROUND: Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases do not have specific International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Editio...

AI-Driven Drug Discovery for Rare Diseases.

Journal of chemical information and modeling
Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, m...

Ontology-based integration and querying of heterogeneous rare disease data sources - POLVAS perspective.

Computers in biology and medicine
The integration of rare disease medical databases belonging to different countries is an important problem, as a large number of observations are required for reliable statistical inference of patient data in order to facilitate clinical research. Su...

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.

Molecular systems biology
Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we r...

Artificial intelligence empowering rare diseases: a bibliometric perspective over the last two decades.

Orphanet journal of rare diseases
OBJECTIVE: To conduct a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in Rare diseases (RDs), with a focus on analyzing publication output, identifying leading contributors by country, assessing the extent of ...

DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity...

Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BMC neurology
BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, ...