Many complex data analysis problems within and beyond the scientific domain involve discovering graphical structures and functional relationships within data. Nonlinear variance decomposition with ...

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus ...

Understanding the muscle actions and resultant skin movement can enable more safe and effective botulinum toxin injection for the treatment of forehead wrinkles. We aimed to investigate skin ...

The optical tweezers toolbox can be used to calculate optical forces and torques of particles using the T-matrix formalism in a vector spherical wave basis. The toolbox includes codes for calculating ...

Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix ...

Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies ...

Adeno-associated virus (AAV) vectors are preeminent in emerging clinical gene therapies. Generalizing beyond the most tractable genetic diseases will require modulation of cell specificity and immune ...

Abstract: Support vector machine (SVM) is a popular classification method for the analysis of wide range of data including big data. Many SVM methods with feature selection have been developed under ...

Activation mapping of scar-related atrial tachycardias (ATs) can be difficult to interpret because of inaccurate time annotation of complex electrograms and passive diastolic activity. We examined ...

In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In ...