AIMC Topic: Deep Learning

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Multimodal functional deep learning for multiomics data.

Briefings in bioinformatics
With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the...

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Briefings in bioinformatics
Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key fact...

DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs.

Briefings in bioinformatics
The internal ribosome entry site (IRES) is a cis-regulatory element that can initiate translation in a cap-independent manner. It is often related to cellular processes and many diseases. Thus, identifying the IRES is important for understanding its ...

A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

Briefings in bioinformatics
Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Her...

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

Briefings in bioinformatics
The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter...

HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network.

Briefings in bioinformatics
Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been...

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Briefings in bioinformatics
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, ...

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Briefings in bioinformatics
Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameter...

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Briefings in bioinformatics
Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine lear...

DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

Briefings in bioinformatics
PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experime...