Modeling Drivers of Political Risk in Offshore Outsourcing

Offshore outsourcing presents many opportunities that are not available domestically. Lower labor costs are the primary driver, but companies also want to focus on their core businesses and create value for their shareholders. Recently, companies even move beyond non-strategic functions into important operational and strategic functions. Smart companies have gained strategic advantage by offshoring processes. Many risks involve in offshore outsourcing of professional services because on behalf of client organization service provider provides services. Political instability in offshore destinations is one of the risks related to offshore outsourcing. Political risks are very volatile and also often more difficult to observe, so they may go unnoticed. Terrorism, Fiscal & Monetary policies, and Corruption are obvious problems that complicate offshore process management. The main objective of this paper is to identify and understand the mutual interaction among various drivers of political risk which affects the performance of offshore outsourcing.  To this effect, authors have identified various drivers through extant review of literature.  From this information, an integrated model using interpretive structural modeling (ISM) for drivers of political risk in offshore outsourcing is developed and the structural relationships between these drivers are modeled.  Further, MICMAC analysis is done to analyze the independent power and dependency of drivers which shall be helpful to managers to identify and classify important criterions and to reveal the direct and indirect effects of each criterion of political environment on offshore outsourcing. Results show that Domestic policies of host country, Civil war, Terrorism and Human resource availability are act as independent drivers Keywords: Political risk, offshore outsourcing, interpretive structural modeling, MICMAC analysi

Hypothesis Protein interaction network for Alzheimer's disease using computational approach

Abstract: Alzheimer's disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer's disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future

Protein-Protein Interaction Detection: Methods and Analysis

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases