Abstract
This paper offers a bird’s eye perception of how bipartite graph modeling could help to comprehend the progression of Alzheimer Disease (AD). We will also discuss the role of the various software tools available in the literature to identify the bipartite structure in AD affected patient brain networks and a general procedure to generate a graph from the AD brain network. Further, as AD is a minacious disorder that leads to the progressive decline of memory and physical ability we resort to Computer-Aided Diagnosis. It has a vital part in the preliminary estimation and finding of AD. We propose an approach to become aware of AD particularly in its beginning phase by analyzing the measurable variations in the hippocampus, grey matter, cerebrospinal fluid and white matter of the brain from Magnetic resonance images. Hence an appropriate segmentation and categorization methods are projected to detect the presence of AD. The trials were carried out on Magnetic resonance images to distinguish from the section of interest. The effectiveness of the CAD system was experimentally evaluated from the images considered from publicly available databases. Obtained findings recommend that the established CAD system has boundless prospective and great guarantee for the prognosis of AD.
Highlights
In this big data era, it is crystal clear that the growth rate of information is exponential in the biosphere
We propose to probe the validity of causative factors like food, smoking habit, drinking habit, life style, hereditary history etc and find out the genes that gets eroded due to these factors and model the phenomena using bipartite graphs and exploit the attributes of such graphs to find out a strategy to prolong the early onset of Alzheimer Disease (AD)
Images were considered from normal individuals (n=146 aged 75.19 ± 4.48) who had been tracked for three years and individuals (n=98 aged 74.1 ± 6.05) with Mild Cognitive Impairment (MCI) who had transmuted to AD within three years
Summary
In this big data era, it is crystal clear that the growth rate of information is exponential in the biosphere. A pertinent property of the bipartite networks referring to biomedical in particular, is to throw back the entities they contain that are abstract and use data integration techniques and incorporate data from various sources like clinical symptoms, diseases, pharmaceutical drugs, in contrast to other networks [1,2]. This imposes the necessity for the spawn of biological databases accessible to the public and which comprise data of appreciable quality. Computing power of the present era permits scrutiny of giant networks, but the task of visualization and scalability continues to be very hard [6]
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