Single-cell omics: experimental workflow, data analyses and applications
Cells are the fundamental units of biological systems and exhibit unique development
trajectories and molecular features. Our exploration of how the genomes orchestrate the …
trajectories and molecular features. Our exploration of how the genomes orchestrate the …
An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics
Multi-omic analyses are necessary to understand the complex biological processes taking
place at the tissue and cell level, but also to make reliable predictions about, for example …
place at the tissue and cell level, but also to make reliable predictions about, for example …
Contrastively generative self-expression model for single-cell and spatial multimodal data
Advances in single-cell multi-omics technology provide an unprecedented opportunity to
fully understand cellular heterogeneity. However, integrating omics data from multiple …
fully understand cellular heterogeneity. However, integrating omics data from multiple …
Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis
Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for
comprehensive characterization of the molecular circuitry that underpins cell identity and …
comprehensive characterization of the molecular circuitry that underpins cell identity and …
scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data
The recent advances in high-throughput single-cell sequencing have created an urgent
demand for computational models which can address the high complexity of single-cell …
demand for computational models which can address the high complexity of single-cell …
Integrating single-cell RNA-seq datasets with substantial batch effects
Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard
part of the analysis, with conditional variational autoencoders (cVAE) being among the most …
part of the analysis, with conditional variational autoencoders (cVAE) being among the most …
Panpipes: a pipeline for multiomic single-cell data analysis
Single-cell multiomic analysis of the epigenome, transcriptome and proteome allows for
comprehensive characterisation of the molecular circuitry that underpins cell identity, cell …
comprehensive characterisation of the molecular circuitry that underpins cell identity, cell …
Deep generative models in single-cell omics
Abstract Deep Generative Models (DGMs) are becoming instrumental for inferring
probability distributions inherent to complex processes, such as most questions in …
probability distributions inherent to complex processes, such as most questions in …
Leveraging attention-enhanced variational autoencoders: Novel approach for investigating latent space of aptamer sequences
Aptamers are increasingly recognized as potent alternatives to antibodies for diagnostic and
therapeutic applications. The application of deep learning, particularly attention-based …
therapeutic applications. The application of deep learning, particularly attention-based …
[HTML][HTML] Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort
VM Pienkowski, P Skoczylas, A Zaremba… - Frontiers in …, 2024 - pmc.ncbi.nlm.nih.gov
Purpose Developing innovative precision and personalized cancer therapeutics is essential
to enhance cancer survivability, particularly for prevalent cancer types such as colorectal …
to enhance cancer survivability, particularly for prevalent cancer types such as colorectal …