AIMC Topic: Software

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CoCoNet: an efficient deep learning tool for viral metagenome binning.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It con...

An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models.

Bioinformatics (Oxford, England)
MOTIVATION: Organization of the organoid models, imaged in 3D with a confocal microscope, is an essential morphometric index to assess responses to stress or therapeutic targets. In fact, differentiating malignant and normal cells is often difficult ...

Venn diagrams in bioinformatics.

Briefings in bioinformatics
Venn diagrams are widely used tools for graphical depiction of the unions, intersections and distinctions among multiple datasets, and a large number of programs have been developed to generate Venn diagrams for applications in various research areas...

DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

Briefings in bioinformatics
Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many comp...

DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...

NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Briefings in bioinformatics
Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative way...

ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism.

Briefings in bioinformatics
MOTIVATION: Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxic...

A spatial-temporal gated attention module for molecular property prediction based on molecular geometry.

Briefings in bioinformatics
MOTIVATION: Geometry-based properties and characteristics of drug molecules play an important role in drug development for virtual screening in computational chemistry. The 3D characteristics of molecules largely determine the properties of the drug ...

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...