AI Medical Compendium Topic

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Genomics

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Machine learning in rare disease.

Nature methods
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types ...

EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations.

Genome biology
Deep neural networks (DNNs) hold promise for functional genomics prediction, but their generalization capability may be limited by the amount of available data. To address this, we propose EvoAug, a suite of evolution-inspired augmentations that enha...

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Sensors (Basel, Switzerland)
Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This ...

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BMC genomic data
BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental met...

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

PLoS computational biology
The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging...

A scoping review on deep learning for next-generation RNA-Seq. data analysis.

Functional & integrative genomics
In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfol...

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Scientific reports
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examinati...

Foundation models for generalist medical artificial intelligence.

Nature
The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI)....

Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health.

Orthodontics & craniofacial research
There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of...

Enzyme function prediction using contrastive learning.

Science (New York, N.Y.)
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied protei...