Graph representation learning in bioinformatics: trends, methods and applications
Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
A survey on computational models for predicting protein–protein interactions
Proteins interact with each other to play critical roles in many biological processes in cells.
Although promising, laboratory experiments usually suffer from the disadvantages of being …
Although promising, laboratory experiments usually suffer from the disadvantages of being …
Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
Graph-based prediction of protein-protein interactions with attributed signed graph embedding
Abstract Background Protein-protein interactions (PPIs) are central to many biological
processes. Considering that the experimental methods for identifying PPIs are time …
processes. Considering that the experimental methods for identifying PPIs are time …
A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations
Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and
various cellular processes. The identification of disease-related miRNAs provides great …
various cellular processes. The identification of disease-related miRNAs provides great …
A fast non-negative latent factor model based on generalized momentum method
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks
Growth and remodelling impact the network topology of complex systems, yet a general
theory explaining how new links arise between existing nodes has been lacking and little is …
theory explaining how new links arise between existing nodes has been lacking and little is …
To embed or not: network embedding as a paradigm in computational biology
Current technology is producing high throughput biomedical data at an ever-growing rate. A
common approach to interpreting such data is through network-based analyses. Since …
common approach to interpreting such data is through network-based analyses. Since …
Generalized nesterov's acceleration-incorporated, non-negative and adaptive latent factor analysis
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …
Network embedding in biomedical data science
Owning to the rapid development of computer technologies, an increasing number of
relational data have been emerging in modern biomedical research. Many network-based …
relational data have been emerging in modern biomedical research. Many network-based …