Designing Energy-Efficient Topologies for Wireless Sensor Network: Neural Approach

Preserving energy or battery power of wireless sensor network is of major concern. As such type of network, the sensors are deployed in an ad hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by neural network which proves to be energy efficient as compared with unmodified topology. Neural network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural network's Self-Organizing Feature Map (SOFM) is used

Single Nucleotide Polymorphism Network: A Combinatorial Paradigm for Risk Prediction

<div><p>Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes <i>P2RY1</i> and <i>P2RY12</i>. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.</p> </div

Network representation of segregation of combined genotypic data of three population (oral cancer, leukoplakia and control).

<div><p>Five SNPs (studied with leukoplakia, cancer and control samples) of each individual are combined to form super-set genotypes. One hundred fourty three unique genotype-sets are observed of which 18 are specific to each control, leukoplakia and cancer individuals, 12 genotype-sets are present in both control and leukoplakia individuals, 18 genotype-sets are present in both control and cancer individuals, only 6 genotype-sets are common between leukoplakia and cancer individuals and as many as 53 genotype-sets are common to case, control and leukoplakia. The number of occurrences of each particular genotype-set is illustrated through its corresponding edge-width.</p> <p>The circles in i) red ii) violet iii) yellow iv) prussian blue v) grey vi) orange and vii) blue respectively represents the following groups i) cancer only ii) cancer and control iii) leukoplakia only iv) control only v) control and leukoplakia vi) cancer and leukoplakia and vii) cancer, leukoplakia and control.</p></div

The strategy of our work is described by the illustration.

<p>The chart shows the strategy employed in the present analysis. The way of doing the whole analyses is described sequentially through the chart. The methods involved in the network based analysis and further the consistency of the outcome of Network based approach and the conventional statistical methods are also described.</p

Single nucleotide polymorphic positions at <i>P2RY1</i> and <i>P2RY12.</i>

<p>Polymorphic and corresponding restriction enzyme cutting sites at <i>P2RY1</i> and P2RY12 (<i>Distance between two sites is not in proper scale</i>).</p

A network through which we represent the segregated pattern of combined genotypic data of case (acute coronary syndrome) and control population (healthy).

<p>The 5 SNPs of each individual are combined to form super-set genotypes in both case and control. Thirty five unique genotype combinations are observed of which 14 combinations are specific to cases (marked as red), 7 combinations are specific to controls (marked as green) and 14 combinations are present in both case and control (marked as brown). The number of occurrences of each particular genotype combinations is illustrated through its corresponding edge-width.</p

Case and control specific genotypic fractions after single locus omission.

<p>One SNP at a time is removed from all the genotypic set in the population to predict the probable risk genotype. The removed locus is denoted by * in the genotype supersets formed taking 4 loci at a time. The effect is studied in terms of the redistribution of number of unique genotypes (nodes) remaining after each SNP deletion in Case, Control and Common populations. The total number of genotype differs with different SNP combinations since once a particular SNP is removed; two genotypes may lose their variation and get collapsed to a single genotype.</p

Agarose gel photograph and a chromatogram view of the 5 loci.

<p>(a) Agarose gel photograph of RFLP using Bcl1 enzyme on <i>P2RY1</i> gene (<i>1622A>G</i>). (b) Chromatogram view of sequencing of <i>P2RY12</i> (<i>1622T>C</i>).</p

Toy network to understand the whole network architecture strategy.

<p>The toy network contains 2 supernodes (Condition1, Condition2), 8 nodes (from A-H) and w1-w12 edges through which nodes are linked to supernodes. In this network, Fraction of Condition2 specific genotypes =4/9 and Fraction of Condition2 specific population = (W1+W2+W5+W6)/∑ W.</p