AIMC Topic: Gene Expression

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Advancing ocular gene therapy: a machine learning approach to enhance delivery, uptake and gene expression.

Drug discovery today
Ocular gene therapy offers a promising approach for treating various eye diseases, centered on the process of transfection, including delivery, cellular uptake and gene expression. This study addresses anatomical and physiological barriers, such as t...

Gene age gap estimate (GAGE) for major depressive disorder: A penalized biological age model using gene expression.

Neurobiology of aging
Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" bet...

Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.

Genetic epidemiology
Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the pred...

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi-omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi-omics machine ...

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences.

ACS synthetic biology
N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current...

Deep model predictive control of gene expression in thousands of single cells.

Nature communications
Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observa...

DRLM: A Robust Drug Representation Learning Method and its Applications.

IEEE/ACM transactions on computational biology and bioinformatics
Learning representations from data is a fundamental step for machine learning. High-quality and robust drug representations can broaden the understanding of pharmacology, and improve the modeling of multiple drug-related prediction tasks, which furth...

SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data.

Computer methods and programs in biomedicine
BACKGROUND: In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intellig...

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Nature genetics
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the ful...