AI Medical Compendium Topic

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Genomics

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A pitfall for machine learning methods aiming to predict across cell types.

Genome biology
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to p...

Machine-Learning and Chemicogenomics Approach Defines and Predicts Cross-Talk of Hippo and MAPK Pathways.

Cancer discovery
Hippo pathway dysregulation occurs in multiple cancers through genetic and nongenetic alterations, resulting in translocation of YAP to the nucleus and activation of the TEAD family of transcription factors. Unlike other oncogenic pathways such as RA...

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

Scientific reports
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust re...

Genome-enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods.

Animal genetics
This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregna...

SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

Genome biology
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity score...

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model.

PLoS computational biology
Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not direct...

Deep learning approach for predicting functional Z-DNA regions using omics data.

Scientific reports
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using ...

A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data.

G3 (Bethesda, Md.)
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used ...

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Trends in pharmacological sciences
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques...

Application of ensemble learning to genomic selection in chinese simmental beef cattle.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
Genomic selection (GS) using the whole-genome molecular makers to predict genomic estimated breeding values (GEBVs) is revolutionizing the livestock and plant breeding. Seeking out novel strategies with higher prediction accuracy for GS has been the ...