[HTML][HTML] Deep learning applications in single-cell genomics and transcriptomics data analysis
Traditional bulk sequencing methods are limited to measuring the average signal in a group
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …
Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system
PSL Schäfer, D Dimitrov, EJ Villablanca… - Nature …, 2024 - nature.com
The immune system comprises diverse specialized cell types that cooperate to defend the
host against a wide range of pathogenic threats. Recent advancements in single-cell and …
host against a wide range of pathogenic threats. Recent advancements in single-cell and …
Supervised discovery of interpretable gene programs from single-cell data
Factor analysis decomposes single-cell gene expression data into a minimal set of gene
programs that correspond to processes executed by cells in a sample. However, matrix …
programs that correspond to processes executed by cells in a sample. However, matrix …
Learning causal representations of single cells via sparse mechanism shift modeling
Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to
tool for analyzing biological data, especially in the field of single-cell genomics. One …
tool for analyzing biological data, especially in the field of single-cell genomics. One …
Characterization of Wnt signaling pathway under treatment of Lactobacillus acidophilus postbiotic in colorectal cancer using an integrated in silico and in vitro …
Colorectal cancer (CRC) is a prevalent and life-threatening cancer closely associated with
the gut microbiota. Probiotics, as a vital microbiota group, interact with the host's colonic …
the gut microbiota. Probiotics, as a vital microbiota group, interact with the host's colonic …
Causal identification of single-cell experimental perturbation effects with CINEMA-OT
Recent advancements in single-cell technologies allow characterization of experimental
perturbations at single-cell resolution. While methods have been developed to analyze such …
perturbations at single-cell resolution. While methods have been developed to analyze such …
Reliable interpretability of biology-inspired deep neural networks
W Esser-Skala, N Fortelny - NPJ Systems Biology and Applications, 2023 - nature.com
Deep neural networks display impressive performance but suffer from limited interpretability.
Biology-inspired deep learning, where the architecture of the computational graph is based …
Biology-inspired deep learning, where the architecture of the computational graph is based …
PAUSE: principled feature attribution for unsupervised gene expression analysis
JD Janizek, A Spiro, S Celik, BW Blue, JC Russell… - Genome Biology, 2023 - Springer
As interest in using unsupervised deep learning models to analyze gene expression data
has grown, an increasing number of methods have been developed to make these models …
has grown, an increasing number of methods have been developed to make these models …
Deep learning in spatially resolved transcriptomics: A comprehensive technical view
Spatially resolved transcriptomics (SRT) has evolved rapidly through various technologies,
enabling scientists to investigate both morphological contexts and gene expression profiling …
enabling scientists to investigate both morphological contexts and gene expression profiling …
A spectrum of explainable and interpretable machine learning approaches for genomic studies
The advancement of high‐throughput genomic assays has led to enormous growth in the
availability of large‐scale biological datasets. Over the last two decades, these increasingly …
availability of large‐scale biological datasets. Over the last two decades, these increasingly …