Unsupervised gene set testing based on random matrix theory

Optimized Unsupervised Image Classification Based on Neutrosophic Set Theory

In this paper, a new technique is used to an unsupervised learning image classification based on integration between neutrosophic sets and optimization linear programming. Neutrosophic sets are used to segment the image into three main components namely objects (O), edges (E), and Background (B). The neutrosophic image components   B E O , , are corresponding to the neutrosophic sets componen...

A global test for gene‐gene interactions based on random matrix theory

Statistical interactions between markers of genetic variation, or gene-gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene-gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For ma...

Notes on Random Matrix Theory

3 Universality 4 3.1 Macroscopic behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.1.1 Wigner’s semicircle law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Microscopic behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2.1 Bulk universality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...

Sampling of Multiple Variables Based on Partially Ordered Set Theory

We introduce a new method for ranked set sampling with multiple criteria. The method relaxes the restriction of selecting just one individual variable from each ranked set. Under the new method for ranking, units are ranked in sets based on linear extensions in partially order set theory with considering all variables simultaneously. Results willbe evaluated by a relatively extensive simulation...

Lectures on Random Matrix Theory