AIMC Topic: Deep Learning

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SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.

Briefings in bioinformatics
Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of str...

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view.

Briefings in bioinformatics
Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expre...

Prediction of protein-ligand binding affinity via deep learning models.

Briefings in bioinformatics
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has br...

A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches.

Briefings in bioinformatics
Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for com...

CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.

Briefings in bioinformatics
CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, ...

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Briefings in bioinformatics
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 5...

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, lead...

Deqformer: high-definition and scalable deep learning probe design method.

Briefings in bioinformatics
Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing d...

scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.

Briefings in bioinformatics
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by s...

MyoV: a deep learning-based tool for the automated quantification of muscle fibers.

Briefings in bioinformatics
Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatic...