AI Medical Compendium Journal:
Computational biology and chemistry

Showing 31 to 40 of 191 articles

PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number.

Computational biology and chemistry
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitati...

An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations.

Computational biology and chemistry
Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and implicated in the numerous diseases. By exploring the subcellular localizations of lncRNAs, we can not only gain crucial insights into the molecular mec...

CPI-GGS: A deep learning model for predicting compound-protein interaction based on graphs and sequences.

Computational biology and chemistry
BACKGROUND: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown...

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Computational biology and chemistry
Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenoca...

Development of a centrosome amplification-associated signature in kidney renal clear cell carcinoma based on multiple machine learning models.

Computational biology and chemistry
BACKGROUND: Centrosome amplification (CA) has been shown to be capable of initiating tumorigenesis with metastatic potential and enhancing cell invasion. We were interested in discovering how centrosome amplification-associated signature affects the ...

Improving binding affinity prediction by emphasizing local features of drug and protein.

Computational biology and chemistry
Binding affinity prediction has been considered as a fundamental task in drug discovery. Despite much effort to improve accuracy of binding affinity prediction, the prior work considered only macro-level features that can represent the characteristic...

Machine learning approaches for predicting craniofacial anomalies with graph neural networks.

Computational biology and chemistry
This study explores the use of machine learning algorithms, including traditional approaches and graph neural networks (GNNs), to predict certain diseases by analyzing protein-protein interactions. Protein-protein interactions (PPIs) are complex, mul...

Dynamics of infectious disease mathematical model through unsupervised stochastic neural network paradigm.

Computational biology and chemistry
The viruses has spread globally and have been impacted lives of people socially and economically, which causes immense suffering throughout the world. Thousands of people died and millions of illnesses were brought, by the outbreak worldwide. In orde...

A multi-perspective deep learning framework for enhancer characterization and identification.

Computational biology and chemistry
Enhancers are vital elements in the genome that boost the transcriptional activity of neighboring genes and are essential in regulating cell-specific gene expression. Therefore, accurately identifying and characterizing enhancers is essential for com...

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction.

Computational biology and chemistry
Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend on feature design. It is foundational to representing biological sequences and automatically extracting their key features for improving SE identifica...