AIMC Topic: Transcriptome

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Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Computers in biology and medicine Mar 31, 2023
Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to...
Machine Learning Gene Regulatory Networks Proteins Transcriptome Drug Discovery
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siVAE: interpretable deep generative models for single-cell transcriptomes.

Genome biology Feb 20, 2023
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding...
Neural Networks, Computer Transcriptome
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A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

BMC bioinformatics Feb 14, 2023
BACKGROUND: Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various s...
Neural Networks, Computer Software Humans Transcriptome Algorithms Multiomics
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LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

Gene Feb 1, 2023
OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secre...
Transcriptome Gene Expression Profiling Humans Algorithms Neural Networks, Computer
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Deep Learning Approach Based on Transcriptome Profile for Data Driven Drug Discovery.

Molecules and cells Jan 20, 2023
Deep Learning Transcriptome Drug Discovery
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Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

Seminars in cancer biology Dec 31, 2022
With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics tec...
Humans Artificial Intelligence Multiomics Neoplasms Transcriptome Precision Medicine
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Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Proceedings of the National Academy of Sciences of the United States of America Dec 28, 2022
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional...
Gene Expression Profiling Phenotype Robotic Surgical Procedures Robotics Deep Learning Transcriptome Image Processing, Computer-Assisted
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Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2022
Regulatory module mining methods divide genes into multiple gene subgroups and explore potential biological mechanisms from omics data. By transforming gene expression profile data into gene co-expression network, we transform the task of gene module...
Gene Expression Profiling Neural Networks, Computer Algorithms Gene Regulatory Networks Transcriptome
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Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.

Genes Dec 1, 2022
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated t...
Deep Learning SARS-CoV-2 Transcriptome Macrophages COVID-19 Humans
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Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

BMC bioinformatics Sep 13, 2022
The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are cur...
Deep Learning Gene Expression Profiling Humans Transcriptome RNA-Seq
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