Studying biological networks, such as protein-protein interactions, is key to understanding
complex biological activities. Various types of large-scale biological datasets have been …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

The most important way of conveying new findings in biomedical research is scientific
publication. Extraction of protein–protein interactions (PPIs) reported in scientific …

Protein function prediction is a complex multiclass multilabel classification problem,
characterized by multiple issues such as the incompleteness of the available annotations …

Gene function prediction is a complex computational problem, characterized by several
items: the number of functional classes is large, and a gene may belong to multiple classes; …

Traditional flat classification methods (eg, binary or multi-class classification) neglect the
structural information between different classes. In contrast, Hierarchical Multi-label …

Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks Page 1
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks …

Rapid advances in high-throughout genome sequencing technologies have resulted in
millions of protein-encoding gene sequences with no functional characterization. Automated …

Gene function prediction is a complex multilabel classification problem with several
distinctive features: the hierarchical relationships between functional classes, the presence …

Node classification is the task of inferring or predicting missing node attributes from
information available for other nodes in a network. This paper presents a general prediction …