A collateral missing value estimation algorithm for DNA microarrays

Genetic microarray expression data often contains multiple missing values that can significantly affect the performance of statistical and machine learning algorithms. This paper presents an innovative missing value estimation technique, called collateral missing value estimation (CMVE) which has demonstrated superior estimation performance compared with the K-nearest neighbour (KNN) imputation algorithm, the least square impute (LSImpute) and Bayesian principal component analysis (BPCA) techniques. Experimental results confirm that CMVE provides an improvement of 89%, 12% and 10% for the BRCA1, BRCA2 and sporadic ovarian cancer mutations, respectively, compared to the average error rate of KNN, LSImpute and BPCA imputation methods, over a range of randomly selected missing values. The underlying theory behind CMVE also means that it is not restricted to bioinformatics data, but can be successfully applied to any correlated data set

Genomic data analysis using grid-based computing

Microarray experiments generate a plethora of genomic data; therefore we need techniques and architectures to analyze this data more quickly. This thesis presents a solution for reducing the computation time of a highly computationally intensive data analysis part of a genomic application. The application used is the Stanford Microarray Database (SMD). SMD\u27s implementation, working, and analysis features are described. The reasons for choosing the computationally intensive problems of the SMD, and the background importance of these problems are presented. This thesis presents an effective parallel solution to the computational problem, including the difficulties faced with the parallelization of the problem and the results achieved. Finally, future research directions for achieving even greater speedups are presented

Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits

A Review of Missing Data Handling Techniques for Machine Learning

Real-world data are commonly known to contain missing values, and consequently affect the performance of most machine learning algorithms adversely when employed on such datasets. Precisely, missing values are among the various challenges occurring in real-world data. Since the accuracy and efficiency of machine learning models depend on the quality of the data used, there is a need for data analysts and researchers working with data, to seek out some relevant techniques that can be used to handle these inescapable missing values. This paper reviews some state-of-art practices obtained in the literature for handling missing data problems for machine learning. It lists some evaluation metrics used in measuring the performance of these techniques. This study tries to put these techniques and evaluation metrics in clear terms, followed by some mathematical equations. Furthermore, some recommendations to consider when dealing with missing data handling techniques were provided

Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans

Background Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition. Results We propose an imputation method based on multivariate mixed models using pedigree information. Our methods comparison indicate that heritability of traits can be affected by the imputation method. Genotypes with missing values imputed with methods that make use of genealogic information can favor genetic analysis of highly polygenic traits, but not genome-wide prediction accuracy. The genotypic matrix captured the highest amount of genetic variance when missing loci were imputed by the method proposed in this paper. Conclusions We concluded that hidden Markov models and random forest imputation are more suitable to studies that aim analyses of highly heritable traits while pedigree-based methods can be used to best analyze traits with low heritability. Despite the notable contribution to heritability, advantages in genomic prediction were not observed by changing the imputation method. We identified significant differences across imputation methods in a dataset missing 20 % of the genotypic values. It means that genotypic data from genotyping technologies that provide a high proportion of missing values, such as GBS, should be handled carefully because the imputation method will impact downstream analysis

Automatic classification of respiratory patterns involving missing data imputation techniques

[Abstract] A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that in general, the Self-Organising Map imputation method allows non-tree based classifiers to achieve improvements over the rest of the imputation methods in terms of the classification accuracy, and that the Feedforward neural network and the Random Forest classifiers offer the best performance regardless of the imputation method used. The improvements in terms of accuracy over the previous work of the authors are limited but the Feed Forward neural network model achieves promising results.Ministerio de Economía y Competitividad; TIN 2013-40686-PXunta de Galicia; GRC2014/35

A comparative study of tree-based models for churn prediction : a case study in the telecommunication sector

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMIn the recent years the topic of customer churn gains an increasing importance, which is the phenomena of the customers abandoning the company to another in the future. Customer churn plays an important role especially in the more saturated industries like telecommunication industry. Since the existing customers are very valuable and the acquisition cost of new customers is very high nowadays. The companies want to know which of their customers and when are they going to churn to another provider, so that measures can be taken to retain the customers who are at risk of churning. Such measures could be in the form of incentives to the churners, but the downside is the wrong classification of a churners will cost the company a lot, especially when incentives are given to some non-churner customers. The common challenge to predict customer churn will be how to pre-process the data and which algorithm to choose, especially when the dataset is heterogeneous which is very common for telecommunication companies’ datasets. The presented thesis aims at predicting customer churn for telecommunication sector using different decision tree algorithms and its ensemble models

Optimizing Gene Expression Prediction and Omics Integration in Populations of African Ancestry

Popular transcriptome imputation methods such as PrediXcan and FUSIon use parametric linear assumptions, and thus are unable to flexibly model the complex genetic architecture of the transcriptome. Although non-linear modeling has been shown to improve imputation performance, replicability and potential cross-population differences have not been adequately studied. Therefore, to optimize imputation performance across global populations, we used the non-linear machine learning (ML) models random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN) to build transcriptome imputation models, and evaluated their performance in comparison to elastic net (EN). We trained gene expression prediction models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed RF, SVR, and KNN, we found that RF outperforms EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan reveals potential gene associations missed by EN models. Therefore, by integrating non-linear modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits. We did not find any significant associations when the prediction models were applied to obesity status and microbiome diversity

Imputation Aided Methylation Analysis

Genome-wide DNA methylation analysis is of broad interest to medical research because of its central role in human development and disease. However, generating high-quality methylomes on a large scale is particularly expensive due to technical issues inherent to DNA treatment with bisulfite, requiring deeper than usual sequencing. In silico methodologies, such as imputation, can be used to address this limitation and improve the coverage and quality of data produced in these experiments. Imputation is a statistical technique where missing values are substituted with computed values. The process involves leveraging information from reference data to calculate probable values for missing data points. In this thesis, imputation is explored for its potential to increase the value of methylation datasets sequenced at different depths: 1. First, a new R package, Methylation Analysis ToolkiT (MATT), was developed to deal with large numbers of WGBS datasets in a computationally- and memory-efficient manner. 2. Second, the performance of DNA methylation-specific and generic imputation tools were assessed by down-sampling high-quality (100x) WGBS datasets to determine the extent to which missing data can be recovered and the accuracy of imputed values. 3. Third, to overcome shortfalls within existing tools, a novel imputation tool was developed, termed Global IMputation of cpg MEthylation (GIMMEcpg). GIMMEcpg default implementation is based on Model Stacking and outperforms existing tools in accuracy and speed. 4. Lastly, to demonstrate its potential, GIMMEcpg was used to impute ten shallow (17x) WGBS datasets from healthy volunteers of the Personal Genome Project UK with high accuracy. Moreover, the extent of missing and low-quality data, as well as the reproducibility and accuracy of methylation datasets, were explored for different data types (Microarrays, Reduced Representation Bisulfite Sequencing (RRBS), Whole Genome Bisulfite Sequencing (WGBS), EM-Seq and Nanopore sequencing)

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model