Innovative Drug and Disease Prediction with Dimensionality Reduction and Intelligence Based Random Walk Methods

Abstract

Medicine is a chemical, which processes, cures, avoids or diagnoses illnesses. There are three phases in drug design: phase of discovery, preclinical phase, and phase of clinical development [1]. Associations of drug-disease reveal and have received excellent attention to the close relationship between drugs and illnesses. Computing techniques may monitor prospective sedate-illness Linkages and additional or expensive moist experiments are performed.A variety of computational methods for predicting sedate disease associations have been suggested in the latest years. Many medicine-sickness confederacies have been identified and saved in databases. Many organizations are however unaccounted for and need to be found. However, there have been few algorithms dedicated to medicines for a distinct illness. This is also one reason why many findings are not sufficiently predictive. The most prevalent form of hepatocellular carcinoma (HCC) in drug discovery. The second most common hepatobiliary disease following HCC is the Cholangiocarcinoma (CCA), representing 10 to 15 half of all liver carcinomas. .

Evaluation of Risk Factor for Cancer Insurance using R Analytics

“…Recently, there have been an increasing use of data analytic techniques in the healthcare studies to estimate a particular disease rate [15], [16], [17]. In this study, we observe the cancer incidence rates pattern among hospital patients according to their age, gender and family history record.…”

Machine Learning Approach towards Tomato Leaf Disease Classification

“…It can be viewed as the probability that a relevant document is retrieved by the query. To achieve recall of 100%, all documents returns in response to any query [12], [14]. Recall is defined as Table 6 shows the performance measure of different classifiers using Gabor features.…”

Level Set Based Whole Heart Segmentation in Non-Contrast Enhanced CT Images

“…Several methods for whole heart segmentation in CT images have been proposed ( [1], [13]- [16]) that mostly utilized atlas or registration-based segmentation methods that are time consuming. In latest, Bui et al, [17] proposed random walk [18] mode segmentation method to segment the heart in contrast enhanced CT images where the heart and its substructures are clearly shown. This is different to the problem that is presented in this paper.…”