AIMC Topic: Algorithms

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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...

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-...

Integrating physics in deep learning algorithms: a force field as a PyTorch module.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields...

MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target ...

A Bayesian convolutional neural network-based generalized linear model.

Biometrics
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in pred...

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...

Contrastive learning for enhancing feature extraction in anticancer peptides.

Briefings in bioinformatics
Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering reco...

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...