Mice offer a number of advantages and are extensively used to model human diseases and drug responses. Selective breeding and genetic manipulation of mice have made many different genotypes and phenotypes available for research. However, in many cases, mouse models have failed to be predictive. Important sources of the prediction problem have been the failure to consider the evolutionary basis ...

Machine learning techniques are increasingly popular tools for understanding complex biological data. Prior research has demonstrated the power of simple statistical clustering algorithms for disease class discovery and prediction. In this work we examine the efficacy of spectral and divisive clustering on gene expression microarray data. In particular we consider simultaneous expression cluste...

<span lang="EN-US">The problem with using microarray technology to detect diseases is that not each analytically necessary. The presence of non-essential gene data adds a computing load the detection method. Therefore, purpose this study reduce high-dimensional size by determining most critical genes involved in Alzheimer's disease progression. A also aims predict patients subset cause di...

Cocaine-associated biomedical and psychosocial problems are substantial twenty-first century global burdens of disease. This burden is largely driven by a cocaine dependence process that becomes engaged with increasing occasions of cocaine product use. For this reason, the development of a risk-prediction model for cocaine dependence may be of special value. Ultimately, success in building such...

The discovery of a mutation responsible for Huntington's disease (HD) offers the possibility of accurate predictive testing, as well as hope for treatment or prevention of this disease. We urge caution in the use of this new test as considerable ethical and counselling problems still exist, and new issues have arisen. The current guidelines for predictive testing should still apply, since it re...

A major challenge in the post-genomic era is to understand the specific cellular functions of individual genes and how dysfunctions of these genes lead to different diseases. As an emerging area of systems biology, gene networks have been used to shed light on gene function and human disease. In this chapter, first the existence of functional association for genes working in a common biological...

Many new disease genes can be identified through high-throughput sequencing. Yet, variant interpretation for the large amounts of genomic data remains a challenge given variation of uncertain significance and genes that lack disease annotation. As clinically significant disease genes may be subject to negative selection, we developed a prediction method that measures paucity of non-synonymous v...