AIMC Topic: Software

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Coding genomes with gapped pattern graph convolutional network.

Bioinformatics (Oxford, England)
MOTIVATION: Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. Howe...

ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface.

Bioinformatics (Oxford, England)
SUMMARY: Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the...

TemStaPro: protein thermostability prediction using sequence representations from protein language models.

Bioinformatics (Oxford, England)
MOTIVATION: Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep...

SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Briefings in bioinformatics
Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph conv...

TransPTM: a transformer-based model for non-histone acetylation site prediction.

Briefings in bioinformatics
Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been develo...

DeepSS2GO: protein function prediction from secondary structure.

Briefings in bioinformatics
Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation hav...

From tradition to innovation: conventional and deep learning frameworks in genome annotation.

Briefings in bioinformatics
Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the...

[Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV).

pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with...

NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, part...