DeepDTAF: a deep learning method to predict protein–ligand binding affinity
Biomolecular recognition between ligand and protein plays an essential role in drug
discovery and development. However, it is extremely time and resource consuming to …
discovery and development. However, it is extremely time and resource consuming to …
Community detection in protein-protein interaction networks and applications
I Manipur, M Giordano, M Piccirillo… - IEEE/ACM …, 2021 - ieeexplore.ieee.org
The ability to identify and characterize not only the protein-protein interactions but also their
internal modular organization through network analysis is fundamental for understanding …
internal modular organization through network analysis is fundamental for understanding …
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding
Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200
nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs …
nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs …
A data-driven clustering recommendation method for single-cell RNA-sequencing data
Y Tian, R Zheng, Z Liang, S Li… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it
possible to solve biological problems at the single-cell resolution. One of the critical steps in …
possible to solve biological problems at the single-cell resolution. One of the critical steps in …
Temporal-spatial analysis of the essentiality of hub proteins in protein-protein interaction networks
Hubs are generally defined as nodes with a high degree centrality, and they are important
for maintaining the stability of complex networks. Previous studies have shown that hub …
for maintaining the stability of complex networks. Previous studies have shown that hub …
AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein–protein interaction network
Identifying unknown protein functional modules, such as protein complexes and biological
pathways, from protein–protein interaction (PPI) networks, provides biologists with an …
pathways, from protein–protein interaction (PPI) networks, provides biologists with an …
MOFSocialNet: Exploiting metal-organic framework relationships via social network analysis
The number of metal-organic frameworks (MOF) as well as the number of applications of this
material are growing rapidly. With the number of characterized compounds exceeding …
material are growing rapidly. With the number of characterized compounds exceeding …
CACO: A core-attachment method with cross-species functional ortholog information to detect human protein complexes
Protein complexes play an essential role in living cells. Detecting protein complexes is
crucial to understand protein functions and treat complex diseases. Due to high time and …
crucial to understand protein functions and treat complex diseases. Due to high time and …
A new tool to study the binding behavior of intrinsically disordered proteins
A Upadhyay, C Ekenna - International Journal of Molecular Sciences, 2023 - mdpi.com
Understanding the binding behavior and conformational dynamics of intrinsically disordered
proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes …
proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes …
Molecular complex detection in protein interaction networks through reinforcement learning
MV Palukuri, RS Patil, EM Marcotte - BMC bioinformatics, 2023 - Springer
Background Proteins often assemble into higher-order complexes to perform their biological
functions. Such protein–protein interactions (PPI) are often experimentally measured for …
functions. Such protein–protein interactions (PPI) are often experimentally measured for …