Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …

Disease modeling and target identification are the most crucial initial steps in drug
discovery, and influence the probability of success at every step of drug development …

Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …

Recent extensions of single-cell studies to multiple data modalities raise new questions
regarding experimental design. For example, the challenge of sparsity in single-omics data …

Deep generative models can learn the underlying structure, such as pathways or gene
programs, from omics data. We provide an introduction as well as an overview of such …

Unveiling functional relationships between various molecular cell phenotypes from data
using machine learning models is a key promise of multiomics. Existing methods either use …

Abstract Deep Generative Models (DGMs) are becoming instrumental for inferring
probability distributions inherent to complex processes, such as most questions in …

Generative models trained with Differential Privacy (DP) are becoming increasingly
prominent in the creation of synthetic data for downstream applications. Existing literature …

Single-cell data is increasingly used for gene regulatory network (GRN) inference, and
benchmarks for this have been developed based on simulated data. However, existing …

The analysis and comprehension of multi-omics data has emerged as a prominent topic in
the field of bioinformatics and data science. However, the sparsity characteristics and high …