Understanding artificial intelligence and predictive analytics: a clinically focused review of machine learning techniques
Understanding Artificial Intelligence and Predictive Analyti... : JBJS Reviews Understanding
Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine …
Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine …
Netpro2vec: a graph embedding framework for biomedical applications
The ever-increasing importance of structured data in different applications, especially in the
biomedical field, has driven the need for reducing its complexity through projections into a …
biomedical field, has driven the need for reducing its complexity through projections into a …
Unraveling the glycosphingolipid metabolism by leveraging transcriptome-weighted network analysis on neuroblastic tumors
Abstract Background Glycosphingolipids (GSLs) are membrane lipids composed of a
ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL …
ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL …
Learning from metabolic networks: Current trends and future directions for precision medicine
Background: Systems biology and network modeling represent, nowadays, the hallmark
approaches for the development of predictive and targeted-treatment based precision …
approaches for the development of predictive and targeted-treatment based precision …
Representing ensembles of networks for fuzzy cluster analysis: a case study
As the statistical analysis of networks finds application in an increasing number of
disciplines, novel methodologies are needed to handle such complexity. In particular, cluster …
disciplines, novel methodologies are needed to handle such complexity. In particular, cluster …
Adversarial attacks on graph-level embedding methods: A case study
As the number of graph-level embedding techniques increases at an unprecedented speed,
questions arise about their behavior and performance when training data undergo …
questions arise about their behavior and performance when training data undergo …
Whole-graph embedding and adversarial attacks for life sciences
Networks provide a suitable model for many scientific and technological problems that
require the representation of complex entities and their relations. Life sciences applications …
require the representation of complex entities and their relations. Life sciences applications …
TumorMet: a repository of tumor metabolic networks derived from context-specific genome-scale metabolic models
Studies about the metabolic alterations during tumorigenesis have increased our knowledge
of the underlying mechanisms and consequences, which are important for diagnostic and …
of the underlying mechanisms and consequences, which are important for diagnostic and …
On whole-graph embedding techniques
Networks provide suitable representative models in many applications, ranging from social
to life sciences. Such representations are able to capture interactions and dependencies …
to life sciences. Such representations are able to capture interactions and dependencies …
Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis
Essential genes (EGs) are fundamental for the growth and survival of a cell or an organism.
Identifying EGs is an important issue in many areas of biomedical research, such as …
Identifying EGs is an important issue in many areas of biomedical research, such as …