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

Showing 31 to 40 of 183 articles

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction.

Methods (San Diego, Calif.)
BACKGROUND: Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the year...

An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches.

Methods (San Diego, Calif.)
This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-sili...

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

Methods (San Diego, Calif.)
With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devot...

MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.

Methods (San Diego, Calif.)
Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interaction...

N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites.

Methods (San Diego, Calif.)
Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be us...

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

Methods (San Diego, Calif.)
RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated w...

mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Methods (San Diego, Calif.)
Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few ex...

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Methods (San Diego, Calif.)
Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate predic...

KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine β-Hydroxybutyrylation (Kbhb) modification sites.

Methods (San Diego, Calif.)
Lysine β-hydroxybutyrylation is an important post-translational modification (PTM) involved in various physiological and biological processes. In this research, we introduce a novel predictor KbhbXG, which utilizes XGBoost to identify β-hydroxybutyry...