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Small data methods in omics: the power of one
Over the last decade, biology has begun utilizing 'big data'approaches, resulting in large,
comprehensive atlases in modalities ranging from transcriptomics to neural connectomics …
comprehensive atlases in modalities ranging from transcriptomics to neural connectomics …
siVAE: interpretable deep generative models for single-cell transcriptomes
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 …
for the visualization and analysis of genomic data, but are limited in their interpretability: it is …
Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models
Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator
relationships. Understanding the GRN dynamics will unravel the complexity behind the …
relationships. Understanding the GRN dynamics will unravel the complexity behind the …
Explaining identity-aware graph classifiers through the language of motifs
Most methods for explaining black-box classifiers (eg, on tabular data, images, or time
series) rely on measuring the impact that removing/perturbing features has on the model …
series) rely on measuring the impact that removing/perturbing features has on the model …
Interpretable AI for inference of causal molecular relationships from omics data
The discovery of molecular relationships from high-dimensional data is a major open
problem in bioinformatics. Machine learning and feature attribution models have shown …
problem in bioinformatics. Machine learning and feature attribution models have shown …
Comparative transcriptome profiling reveals the basis of differential sheath blight disease response in tolerant and susceptible rice genotypes
Rice sheath blight (ShB) disease, caused by the fungal pathogen Rhizoctonia solani AG1-
IA, is one of the devastating diseases and causes severe yield losses all over the world. No …
IA, is one of the devastating diseases and causes severe yield losses all over the world. No …
[HTML][HTML] DiffBrainNet: Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions
Genome-wide gene expression analyses are invaluable tools for studying biological and
disease processes, allowing a hypothesis-free comparison of expression profiles …
disease processes, allowing a hypothesis-free comparison of expression profiles …
Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework
Advanced computational methods exploit gene expression and epigenetic datasets to
predict gene regulatory networks controlled by transcription factors (TFs). These methods …
predict gene regulatory networks controlled by transcription factors (TFs). These methods …
Research on gastric cancer's drug-resistant gene regulatory network model
Z Li, T Zhang, H Lei, L Wei, Y Liu, Y Shi, S Li… - Current …, 2020 - benthamdirect.com
Objective: Based on bioinformatics, differentially expressed gene data of drug-resistance in
gastric cancer were analyzed, screened and mined through modeling and network modeling …
gastric cancer were analyzed, screened and mined through modeling and network modeling …
A pseudo-value regression approach for differential network analysis of co-expression data
Background The differential network (DN) analysis identifies changes in measures of
association among genes under two or more experimental conditions. In this article, we …
association among genes under two or more experimental conditions. In this article, we …