AI Medical Compendium Topic

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A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

Computational and mathematical methods in medicine
We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific targe...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Nature communications
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA.

Scientific data
Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,19...

Methods for correcting inference based on outcomes predicted by machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accoun...

Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.

Scientific reports
For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability with...

SilencerDB: a comprehensive database of silencers.

Nucleic acids research
Gene regulatory elements, including promoters, enhancers, silencers, etc., control transcriptional programs in a spatiotemporal manner. Though these elements are known to be able to induce either positive or negative transcriptional control, the comm...

Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology.

Cells
Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Art...

Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities.

Genome biology
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay mul...