AIMC Topic: Deep Learning

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DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computationa...

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Nucleic acids research
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Nucleic acids research
Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene t...

[ protein design in the age of artificial intelligence].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent ...

[Investigation of the impact of the deep learning based CT fractional flow reserve on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease].

Zhonghua xin xue guan bing za zhi
To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease. In this single-center retrospective cohort study, cons...

Noninvasive fetal genotyping using deep neural networks.

Briefings in bioinformatics
Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. In obstetrics, maternal plasma cfDNA have already proven its utility, enabling noninvasive prenatal ...

DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.

Briefings in bioinformatics
The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq...

ESM-BBB-Pred: a fine-tuned ESM 2.0 and deep neural networks for the identification of blood-brain barrier peptides.

Briefings in bioinformatics
Blood-brain barrier peptides (BBBP) could significantly improve the delivery of drugs to the brain, paving the way for new treatments for central nervous system (CNS) disorders. The primary challenge in treating CNS disorders lies in the difficulty p...

Do protein language models learn phylogeny?

Briefings in bioinformatics
Deep machine learning demonstrates a capacity to uncover evolutionary relationships directly from protein sequences, in effect internalising notions inherent to classical phylogenetic tree inference. We connect these two paradigms by assessing the ca...