Machine Learning has a significant impact on different aspects of science and technology including that of medical researches and life sciences. Diabetes Mellitus, more commonly known as diabetes, is a chronic disease that involves abnormally high levels of glucose sugar in blood cells and the usage of insulin in the human body. This article has focused on analyzing diabetes patients as well as detection of diabetes using different Machine Learning techniques to build up a model with a few dependencies based on the PIMA dataset. The model has been tested on an unseen portion of PIMA and also on the dataset collected from Kurmitola General Hospital, Dhaka, Bangladesh. The research is conducted to demonstrate the performance of several classifiers trained on a particular country’s diabetes dataset and tested on patients from a different country. We have evaluated decision tree, K-nearest neighbor, random forest, and Naïve Bayes in this research and the results show that both random forest and Naïve Bayes classifier performed well on both datasets

Analysis of molecular interaction networks is pervasive in systems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multiway interactions among molecules, which occur very often within cells. Hypergraphs may be better representations for networks having such interactions, since hyperedges can naturally represent relationships among multiple molecules. Here, we propose using hypergraphs to capture the uncertainty inherent in reverse engineering gene-gene networks. Some subsets of nodes may induce highly varying subgraphs across an ensemble of networks inferred by a reverse engineering algorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic data sets with high precision and recall, even for moderate amount of noise. We apply our techniques to a data set of pathways inferred from genetic interaction data in S. cerevisiae related to the unfolded protein response. Our approach discovers several hyperedges that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns. We also show that these complexes are not discovered by an algorithm that computes frequent and dense subgraphs.

Motivation: Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. Results: To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone. Availability: C++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/∼murali/software/biorithm-docs. Contact: murali@cs.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Pseudo-cliques (subgraphs with almost all possible edges) have many applications. But they do not satisfy the convertible antimonotone constraint (as we prove here). So, it is hard to reduce the search space of pseudo-cliques and list them efficiently. To our knowledge, only two exact algorithms, namely, ODES and PCE, were proposed for this purpose, but both have high execution times. Here, we present an exact algorithm named Fast Pseudo-Clique Enumerator (FPCE). It employs some pruning techniques we derived to reduce the search space. Our experiment on 15 real and 16 synthetic graphs shows that (i) on real graphs, FPCE is, on average, 38.6 and 6.5 times faster than ODES and PCE, respectively, whereas (ii) on synthetic graphs, FPCE is, on average, 39.7 and 3.1 times faster than ODES and PCE, respectively. We apply FPCE and a popular heuristic method on a PPI network to identify pseudo-cliques. FPCE outputs match with more known protein complexes, are more accurate, and are biologically more significant - suggesting that the exact computation of pseudo-cliques may give better insights. For its speed, FPCE is a suitable choice in such cases.

Most basic DHT based peer-to-peer networks distribute objects among nodes in a way that tends to balance loads among the peers if the distribution of objects in the identifier space is uniform. But this doesnpsilat hold in practice. The case is further complicated by the fact that, in a typical scenario, object loads and node capacities vary enormously. Additionally, a nodepsilas load may vary greatly over time since the system can be expected to experience continuous insertions and deletions of objects, and continuous arrival and departure of nodes. This paper presents a scheme that can balance load in any DHT based P2P network even in a dynamic environment.