AIMC Topic: Transcriptome

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Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.

Nature biomedical engineering May 1, 2023
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-dri...
Humans Machine Learning Gene Expression Profiling Transcriptome
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DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently ...
Animals Transcriptome Deep Learning Mammals Adenosine
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Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, i...
Neural Networks, Computer Transcriptome Deep Learning Supervised Machine Learning
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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 Drug Discovery Transcriptome Gene Regulatory Networks Proteins
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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...
Humans Neural Networks, Computer Software Algorithms Transcriptome 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...
Humans Neural Networks, Computer Algorithms Gene Expression Profiling Transcriptome
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Deep Learning Approach Based on Transcriptome Profile for Data Driven Drug Discovery.

Molecules and cells Jan 20, 2023
Drug Discovery Transcriptome Deep Learning
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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 Precision Medicine Neoplasms Transcriptome Multiomics
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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...
Phenotype Gene Expression Profiling Transcriptome Deep Learning Image Processing, Computer-Assisted Robotics Robotic Surgical Procedures
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