AI Medical Compendium Journal:
Methods (San Diego, Calif.)

Showing 51 to 60 of 183 articles

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues.

Methods (San Diego, Calif.)
N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection ov...

MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction.

Methods (San Diego, Calif.)
The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairin...

miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction.

Methods (San Diego, Calif.)
MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to in...

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction.

Methods (San Diego, Calif.)
DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-bindi...

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

Methods (San Diego, Calif.)
The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to ...

Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

Methods (San Diego, Calif.)
The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites,...

CLCAP: Contrastive learning improves antigenicity prediction for influenza A virus using convolutional neural networks.

Methods (San Diego, Calif.)
Influenza viruses are detected year-round over the world and the viruses will usually circulate during fall and winter, causing the seasonal flu. The growing novel variants of influenza viruses pose a significant concern to public health annually. Ho...

Integrating Pre-Trained protein language model and multiple window scanning deep learning networks for accurate identification of secondary active transporters in membrane proteins.

Methods (San Diego, Calif.)
Secondary active transporters play pivotal roles in regulating ion and molecule transport across cell membranes, with implications in diseases like cancer. However, studying transporters via biochemical experiments poses challenges. We propose an eff...

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors.

Methods (San Diego, Calif.)
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but i...

Dimensional emotion recognition from camera-based PRV features.

Methods (San Diego, Calif.)
Heart rate variability (HRV) is an important indicator of autonomic nervous system activity and can be used for the identification of affective states. The development of remote Photoplethysmography (rPPG) technology has made it possible to measure p...