Sparse regressions applied to cancer diagnosis suffer from noise reduction, gene grouping, and group significance evaluation. This paper presented the weighted group regularized logistic regression (WGRLR) for dealing with the above problems. Clean data ...

The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in ...

We consider the problem of determining the <italic>mutational support and distribution</italic> of the SARS-CoV-2 viral genome in the small-sample regime. The mutational support refers to the unknown number of sites that may eventually mutate in the SARS-...

High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause ...

Analyzing single-cell sequencing data from large cohorts is challenging. Discrepancies across experiments and differences among participants often lead to omissions and false discoveries in differentially expressed genes. We find that the Van Elteren test,...

Many physiological and pathological pathways are dependent on gene-specific on/off regulation of transcription. Some genes are repressed, while others are activated. Although many previous studies have analyzed the mechanisms of gene-specific repression ...

Gene regulatory networks (GRNs) play a key role in biological processes. However, GRNs are diverse under different biological conditions. Reconstructing gene regulatory networks (GRNs) from gene expression has become an important opportunity and challenge ...

We propose a procedure for modeling a phenotype using QTLs which estimate the additive and dominance effects of genotypes and epistasis. The estimation of the model is implemented through a Bayesian approach which uses the data-driven reversible jump (...

Real-time cellular analyzer (RTCA) has been generally applied to test the cytotoxicity of chemicals. However, several factors impact the experimental quality. A non-negligible factor is the abnormal time-dependent cellular response curves (TCRCs) of the ...

Many properties of molecules vary systematically with changes in the structural formula and can thus be estimated from regression models defined on small structural building blocks, usually functional groups. Typically, such approaches are limited to a ...

Many current RNA-sequencing data analysis methods compare expressions one gene at a time, taking little consideration of the correlations among genes. In this study, we propose a method to convert such an one-dimensional comparison approach into a two-...

Brain functional connectivity (FC) has shown great potential in becoming biomarkers of brain status. However, the problem of accurately estimating FC from complex-noisy fMRI time series remains unsolved. Usually, a regularization function is more ...

Detecting gene&#x2013;gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case&#x2013;control studies. Herein, we propose a balance ...

RNA-Seq analysis has revolutionized researchers&#x2019; understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying ...

Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of ...

The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization is an essential step in differential expression (DE) analysis. The ...

Exploring different quantum chemical quantities for lead compounds is an ongoing approach in identifying crucial structural activity related features that are contributing into their biological activities. Herein, activity-related quantum chemical ...

With the development of high throughput technology, it has become feasible and common to profile tens of thousands of gene activities simultaneously. These genomic data typically have sample size of hundreds or fewer, which is much less than the feature ...

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 predictor variables in a linear model that may have more unknown parameters than ...

Structural domains are evolutionary and functional units of proteins and play a critical role in comparative and functional genomics. Computational assignment of domain function with high reliability is essential for understanding whole-protein ...