Article-Journal

Which chemical features are captured by ChemBERTa's attention?
Transformer-based models such as ChemBERTa have demonstrated strong performance across a wide range of molecular prediction tasks by …
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Exploring feature limitations in antimicrobial resistance prediction: machine learning and deep learning in A. baumannii
Antimicrobial resistance phenotype prediction (AMRPP) provides a computational alternative to conventional susceptibility testing. …
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Diagnosing Multiple Sclerosis from Magnetic Resonance Imaging Images: Highlights from the Second Isfahan Artificial Intelligence Event 2024

Background:

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system which is the main reason of disabilities of …

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SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling
Identifying synergistic drug combinations is crucial for enhancing the effectiveness and safety of cancer treatments. However, many …
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SiaScoreNet: a siamese neural network-based model integrating prediction scores for HLA-peptide interaction prediction

Motivation

Cancer immunotherapy uses the immune system to recognize and eliminate tumor cells by presenting tumor antigens through …

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DRP-VEM: Drug repositioning prediction using voting ensemble
Conventional approaches to drug discovery are both expensive and time-intensive. To circumvent these challenges, drug repurposing or …
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PHLA-SiNet: A novel peptide-HLA binding prediction model using heterogeneous Siamese neural networks
Predicting peptide–HLA binding is crucial for advancing immunotherapy; however, current models face several challenges, including …
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Prediction of human-virus protein-protein interaction using heterogeneous siamese neural network
Viral infections represent pathological conditions arising from the intrusion of viruses into host cells and their replication. The …
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SimPep and OP-AND: A deep learning framework and curated database for predicting osteogenic peptides
Bone health is a growing concern in aging populations, and bioactive peptides in dairy products offer a promising approach to …
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SimPep and OP-AND: A deep learning framework and curated database for predicting osteogenic peptides
Molecular landscape of endometrioid Cancer: Integrating multiomics and deep learning for personalized survival prediction
Background The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the …
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