With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …

Recent technological advances have expanded the breadth of available omic data, from
whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic …

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive
amounts of data in multiple dimensions (eg, genomics, epigenomics, transcriptomic …

In light of recent advances in biomedical computing, big data science, and precision
medicine, there is a mammoth demand for establishing algorithms in machine learning and …

In this chapter, we present a survey of algorithms for dense subgraph discovery. The
problem of dense subgraph discovery is closely related to clustering though the two …

A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Objective Many machine learning models have aided medical specialists in diagnosis and
prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the …

Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high
rate of death and long-term impairment. Despite years of studies, there are still no stroke …

Background Prognostic studies of breast cancer survivability have been aided by machine
learning algorithms, which can predict the survival of a particular patient based on historical …

Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single
level of genomic data fully elucidates tumor behavior due to the presence of numerous …