Most attempts to fit a supervised machine learning (ML) model in bioinformatics try to predict
the full range of trait or response values. While such prediction tasks effectively capture the …

Motivation Extreme phenotype sampling (EPS) is a broadly-used design to identify
candidate genetic factors contributing to the variation of quantitative traits. By enriching the …

GWAS are popular approaches to associate genetic variations on a population of individuals
with particular traits. Despite widely applications, GWAS are heavily depend on the accuracy …

Finding informative predictive features in high-dimensional biological case–control datasets
is challenging. The Extreme Pseudo-Sampling (EPS) algorithm offers a solution to the …

Screening biomolecular markers from high-dimensional biological data is one of the long-
standing tasks for biomedical translational research. With its advantages in both feature …

In life sciences, high-throughput techniques typically lead to high-dimensional data and
often the number of covariates is much larger than the number of observations. This …

The method of Sorted L-One Penalized Estimation, or SLOPE, is a sparse regression
method recently introduced by Bogdan et. al.[1]. It can be used to identify significant …

With the accumulation of various high-throughput biological data, an urgent task is to
develop efficient methods to explore useful bioinformatics from massive data. Various …

With recent advances in technology, deep sequencing data will be widely used to further the
understanding of genetic influence on traits of interest. Therefore not only common variants …

Genome-Wide association studies (GWAS), based on testing one single nucleotide
polymorphism (SNP) at a time, have revolutionized our understanding of the genetics of …