Recent advances in deep learning for protein-protein interaction analysis: A comprehensive review
M Lee - Molecules, 2023 - mdpi.com
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative
imprint across multiple disciplines. Within computational biology, it is expediting progress in …
imprint across multiple disciplines. Within computational biology, it is expediting progress in …
Cellenboost: a boosting-based ligand-receptor interaction identification model for cell-to-cell communication inference
Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The
identification of communication between cancer cells themselves and one between cancer …
identification of communication between cancer cells themselves and one between cancer …
MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction
Abstract Background and Objective Protein-protein interaction (PPI) is a vital process in all
living cells, controlling essential cell functions such as cell cycle regulation, signal …
living cells, controlling essential cell functions such as cell cycle regulation, signal …
Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis
R Yao, Z Shen, X Xu, G Ling, R Xiang, T Song… - Frontiers in …, 2024 - frontiersin.org
Introduction In recent years, graph neural network has been extensively applied to drug
discovery research. Although researchers have made significant progress in this field, there …
discovery research. Although researchers have made significant progress in this field, there …
Response score-based protein structure analysis for cancer prediction aided by the Internet of Things
Medical diagnosis through prediction and analysis is par excellence in integrating modern
technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical …
technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical …
Variable importance in high-dimensional settings requires grouping
Explaining the decision process of machine learning algorithms is nowadays crucial for both
model's performance enhancement and human comprehension. This can be achieved by …
model's performance enhancement and human comprehension. This can be achieved by …
Exploring the alignment landscape: Llms and geometric deep models in protein representation
Latent representation alignment has become a foundational technique for constructing
multimodal large language models (MLLM) by mapping embeddings from different …
multimodal large language models (MLLM) by mapping embeddings from different …
MVSF-AB: Accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning
Motivation Predicting the binding affinity between antigens and antibodies accurately is
crucial for assessing therapeutic antibody effectiveness and enhancing antibody …
crucial for assessing therapeutic antibody effectiveness and enhancing antibody …
Learning protein language contrastive models with multi-knowledge representation
W Xu, Y Xia, B Sun, Z Zhao, L Tang, X Zhou… - Future Generation …, 2025 - Elsevier
Protein representation learning plays a crucial role in obtaining a comprehensive
understanding of biological regulatory mechanisms and in developing proteins and drugs …
understanding of biological regulatory mechanisms and in developing proteins and drugs …
An Ensemble Classifiers for Improved Prediction of Native–Non-Native Protein–Protein Interaction
In this study, we present an innovative approach to improve the prediction of protein–protein
interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on …
interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on …