AI Medical Compendium Journal:
BMC biology

Showing 1 to 10 of 32 articles

NeuroScale: evolutional scale-based protein language models enable prediction of neuropeptides.

BMC biology
BACKGROUND: Neuropeptides (NPs) are critical signaling molecules involved in various physiological and behavioral processes, including development, metabolism, and memory. They function within both the nervous and endocrine systems and have emerged a...

EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.

BMC biology
BACKGROUND: Identifying GPCR-compound interactions (GCI) plays a significant role in drug discovery and chemogenomics. Machine learning, particularly deep learning, has become increasingly influential in this domain. Large molecular models, due to th...

Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.

BMC biology
BACKGROUND: Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. Using traditional biomedical experiments to discover and confi...

Domain-specific AI segmentation of IMPDH2 rod/ring structures in mouse embryonic stem cells.

BMC biology
BACKGROUND: Inosine monophosphate dehydrogenase 2 (IMPDH2) is an enzyme that catalyses the rate-limiting step of guanine nucleotides. In mouse embryonic stem cells (ESCs), IMPDH2 forms large multi-protein complexes known as rod-ring (RR) structures t...

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.

BMC biology
BACKGROUND: Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to ...

iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.

BMC biology
BACKGROUND: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. Howe...

A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.

BMC biology
BACKGROUND: Essential genes are crucial for the development, inheritance, and survival of species. The exploration of these genes can unravel the complex mechanisms and fundamental life processes and identify potential therapeutic targets for various...

DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.

BMC biology
BACKGROUND: Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experim...

RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.

BMC biology
BACKGROUND: RNA-binding proteins (RBPs) play crucial roles in many biological processes, and computationally identifying RNA-RBP interactions provides insights into the biological mechanism of diseases associated with RBPs.

MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.

BMC biology
BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over th...