Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review).

Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid ß plaques and unstable microtubule­associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.

Genetic underpinnings in Alzheimer's disease - a review.

In this review, we discuss the genetic etiologies of Alzheimer's disease (AD). Furthermore, we review genetic links to protein signaling pathways as novel pharmacological targets to treat AD. Moreover, we also discuss the clumps of AD-m ediated genes according to their single nucleotide polymorphism mutations. Rigorous data mining approaches justified the significant role of genes in AD prevalence. Pedigree analysis and twin studies suggest that genetic components are part of the etiology, rather than only being risk factors for AD. The first autosomal dominant mutation in the amyloid precursor protein (APP) gene was described in 1991. Later, AD was also associated with mutated early-onset (presenilin 1/2, PSEN1/2 and APP) and late-onset (apolipoprotein E, ApoE) genes. Genome-wide association and linkage analysis studies with identified multiple genomic areas have implications for the treatment of AD. We conclude this review with future directions and clinical implications of genetic research in AD.

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme.

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.