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The accuracy of automated facial landmarking - a comparative study between Cliniface software and patch-based Convoluted Neural Network algorithm.

European journal of orthodontics
BACKGROUND: Automatic landmarking software packages simplify the analysis of the 3D facial images. Their main deficiency is the limited accuracy of detecting landmarks for routine clinical applications. Cliniface is readily available open-access soft...

deepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.

Briefings in bioinformatics
MOTIVATION: Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current method...

CasPro-ESM2: Accurate identification of Cas proteins integrating pre-trained protein language model and multi-scale convolutional neural network.

International journal of biological macromolecules
Cas proteins (CRISPR-associated protein) are the core components of the CRISPR-Cas system, playing critical roles in defending against foreign DNA and RNA invasions. Identifying Cas proteins can provide deeper insights into the immune mechanisms of t...

PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.

Briefings in bioinformatics
Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due to the complex and multi-omics nature of cancer data. Existing methods often struggle to capture the comprehensive range of informative features requir...

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

Genome biology
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to in...

BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.

Briefings in bioinformatics
RNA biomarkers enable early and precise disease diagnosis, monitoring, and prognosis, facilitating personalized medicine and targeted therapeutic strategies. However, identification of RNA biomarkers is hindered by the challenge of analyzing relative...

scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.

Genome biology
Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensio...

TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.

Nature communications
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we...

CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...

Lit-OTAR framework for extracting biological evidences from literature.

Bioinformatics (Oxford, England)
SUMMARY: The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionize drug discovery by extracting evidence from scientific literature for drug target identification and valid...