AIMC Topic: Software

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iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks.

PloS one
Protein-DNA interactions play a crucial role in cellular biology, essential for maintaining life processes and regulating cellular functions. We propose a method called iProtDNA-SMOTE, which utilizes non-equilibrium graph neural networks along with p...

Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction.

PloS one
The increasing importance of deep learning in software development has greatly improved software quality by enabling the efficient identification of defects, a persistent challenge throughout the software development lifecycle. This study seeks to de...

SVM-LncRNAPro: An SVM-Based Method for Predicting Long Noncoding RNA Promoters.

IET systems biology
Long non-coding RNAs (lncRNAs) are closely associated with the regulation of gene expression, whose promoters play a crucial role in comprehensively understanding lncRNA regulatory mechanisms, functions and their roles in diseases. Due to limitations...

Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.

Methods in molecular biology (Clifton, N.J.)
The interactions between long noncoding RNA (lncRNA) and protein play crucial roles in various biological processes. Computational methods are essential for predicting lncRNA-protein interactions and deciphering their mechanisms. In this chapter, we ...

A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Human involvement remains critical in most instances of clinical decision-making. Recent advances in AI and machine learning opened the door for designing, implementing, and translating interactive AI systems to support clinicians in decision-making....

SG-ML-PLAP: A structure-guided machine learning-based scoring function for protein-ligand binding affinity prediction.

Protein science : a publication of the Protein Society
Computational methods to predict binding affinity of protein-ligand complex have been used extensively to design inhibitors for proteins selected as drug targets. In recent years machine learning (ML) is being increasingly used for design of drugs/in...

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Methods in molecular biology (Clifton, N.J.)
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularl...

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Methods in molecular biology (Clifton, N.J.)
Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data...

TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Methods in molecular biology (Clifton, N.J.)
Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agen...

MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

Methods in molecular biology (Clifton, N.J.)
MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first ...