One of the primary goals in this era of precision medicine is to understand the biology of
human diseases and their treatment, such that each individual patient receives the best …

As data science becomes more mainstream, there will be an ever-growing demand for data
science tools that are more accessible, flexible, and scalable. In response to this demand …

As the field of data science continues to grow, there will be an ever-increasing demand for
tools that make machine learning accessible to non-experts. In this paper, we introduce the …

Background The selection, development, or comparison of machine learning methods in
data mining can be a difficult task based on the target problem and goals of a particular …

Over the past decade, data science and machine learning has grown from a mysterious art
form to a staple tool across a variety of fields in academia, business, and government. In this …

Modern biomedical data mining requires feature selection methods that can (1) be applied
to large scale feature spaces (eg 'omics' data),(2) function in noisy problems,(3) detect …

In this era of genome-wide association studies (GWAS), the quest for understanding the
genetic architecture of complex diseases is rapidly increasing more than ever before. The …

Algorithmic scalability is a major concern for any machine learning strategy in this age of 'big
data'. A large number of potentially predictive attributes is emblematic of problems in …

Motivation: The existing methods for genetic-interaction detection in genome-wide
association studies are designed from different paradigms, and their performances vary …

Feature combination selection is used in object classification to select complementary
features that can produce a powerful combination. One active area of selecting feature …