AI Medical Compendium Topic:
Genomics

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Natural Language Processing Approaches for Retrieval of Clinically Relevant Genomic Information in Cancer.

Studies in health technology and informatics
The accelerating impact of genomic data in clinical decision-making has generated a paradigm shift from treatment based on the anatomic origin of the tumor to the incorporation of key genomic features to guide therapy. Assessing the clinical validity...

AutoDC: an automatic machine learning framework for disease classification.

Bioinformatics (Oxford, England)
MOTIVATION: The emergence of next-generation sequencing techniques opens up tremendous opportunities for researchers to uncover the basic mechanisms of disease at the molecular level. Recently, automatic machine learning (AutoML) frameworks have been...

Artificial Intelligence-Assisted Serial Analysis of Clinical Cancer Genomics Data Identifies Changing Treatment Recommendations and Therapeutic Targets.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Given the pace of predictive biomarker and targeted therapy development, it is unknown whether repeat annotation of the same next-generation sequencing data can identify additional clinically actionable targets that could be therapeutically ...

seqgra: principled selection of neural network architectures for genomics prediction tasks.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their con...

The Essentials of Multiomics.

The oncologist
Within the last decade, the science of molecular testing has evolved from single gene and single protein analysis to broad molecular profiling as a standard of care, quickly transitioning from research to practice. Terms such as genomics, transcripto...

Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.

Toxicological sciences : an official journal of the Society of Toxicology
Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology toward "reducing, refining, and replacing" animal use. Here, we proposed a deep ...

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in differe...

Universal encoding of pan-cancer histology by deep texture representations.

Cell reports
Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented the direct comparison and accumulation of large-scale datasets. Here, we show successful universal encod...

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression ...

Uncertainty-aware and interpretable evaluation of Cas9-gRNA and Cas12a-gRNA specificity for fully matched and partially mismatched targets with Deep Kernel Learning.

Nucleic acids research
The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specificity remains challenging. Lack of transparency and focus on point estimates of efficiency disregarding...