We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1-20% missing values. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We present a comparative study of several methods for the estimation of missing values in gene microarray data. In this report, we investigate automated methods for estimating missing data. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. Gene expression microarray experiments can generate data sets with multiple missing expression values. Testing data are classified into randomly selected four groups of data. The 677 micro-array data were collected from a cancer microarray database and data-mining platform called Oncomine. The number of inputs is fixed at two and the number of output is one. The supervised learning algoritm is used during the training and testing stages. Methods: The Back-Propagation Multi Layer perceptron (BPMLP) for the prediction of score value. Purpose of the Study: The aim of this research was to develop a nonlinear empirical model to predict a score value for specific microRNAs responsible from cancer pathogenesis by using micro-array data. This study developed a neural network with back-propagation learning algorithm for the prediction of microRNAs responsible from cancer pathogenesis at earlier stages. The effectiveness in treatment and curing cancer is directly dependent on the ability to detect cancers at their earlier stages. Evidence is emerging that particular microRNAs may play a role in human cancer pathogenesis they exhibit important regulatory roles in development, cell proliferation, cell survival and apoptosis. Problem Statement: Cancer is a complex genetic disease. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. Here, we develop and validate genome-wide polygenic scores for five common diseases. Although most disease risk is polygenic in nature2-5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies.
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