MapReduce Based Feature Selection and Classification of Microarray Dataset

Gene expression profiling has emerged as an efficient technique for classification, diagnosis and treatment of various diseases. The data retrieved from microarray contains the gene expression values of the genes present in a tissue. The size of such data varies from some kilobytes to thousand of Gigabytes. Therefore, the analysis of microarray dataset in a very short period of time is essential. The major setback of microarray dataset is the presence of a large number of irrelevant information, which hinders the amount of useful information present in the dataset and results in a large number of computations. Therefore, selection of relevant genes is an important step in microarray data analysis. After retrieving the required number of features, classification of the dataset is done. In this project, various methods based on MapReduce are proposed to select the relevant number of feature. After feature selection, Naïve Bayes Classifier and N-Nearest Neighbor is used to classify the datasets. These algorithms are implemented on Hadoop framework. A comparative analysis is done on these methodologies using microarray data of different size

Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields

Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously. Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points. Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages

Comparing Prediction Accuracy for Machine Learning and Other Classical Approaches in Gene Expression Data

Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. Using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the unwanted noisy and redundant genes. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. In this paper, we have compared the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods are applied to datasets from four recently published cancer gene expression studies. The performance of the classification technique has been evaluated for varying number of selected features in terms of misclassification rate  using hold-out cross validation. Our study shows that KNN, RDA and SVM with linear kernel methods have lower misclassification rate than the other algorithms. Keywords: microarray, gene expression, KNN, DLDA, RDA, SV

Case-base retrieval of childhood leukaemia patients using gene expression data

University of Technology, Sydney. Faculty of Engineering and Information Technology.Acute Lymphoblastic Leukaemia (ALL) is the most common childhood malignancy. Nowadays, ALL is diagnosed by a full blood count and a bone marrow biopsy. With microarray technology, it is becoming more feasible to look at the problem from a genetic point of view and to perform assessment for each patient. This thesis proposes a case-base retrieval framework for ALL using a nearest neighbour classifier that can retrieve previously treated patients based on their gene expression data. However, the wealth of gene expression values being generated by high throughout microarray technologies leads to complex high dimensional datasets, and there is a critical need to apply data-mining and computational intelligence techniques to analyse these datasets efficiently. Gene expression datasets are typically noisy and have very high dimensionality. Moreover, gene expression microarray datasets often consist of a limited number of observations relative to the large number of gene expression values (thousands of genes). These characteristics adversely affect the analysis of microarray datasets and pose a challenge for building an efficient gene-based similarity model. Four problems are associated with calculating the similarity between cancer patients on the basis of their gene expression data: feature selection, dimensionality reduction, feature weighting and imbalanced classes. The main contributions of this thesis are: (i) a case-base retrieval framework, (ii) a Balanced Iterative Random Forest algorithm for feature selection, (iii) a Local Principal Component algorithm for dimensionality reduction and visualization and (iv) a Weight Learning Genetic algorithm for feature weighting. This thesis introduces Balanced Iterative Random Forest (BIRF) algorithm for selecting the most relevant features to the disease and discarding the non-relevant genes. Balanced iterative random forest is applied on four cancer microarray datasets: Childhood Leukaemia dataset, Golub Leukaemia dataset, Colon dataset and Lung cancer dataset. Childhood Leukaemia dataset represents the main target of this project and it is collected from The Children's Hospital at Westmead. Patients are classified based on the cancer's risk type (Medium, Standard and High risk); Colon cancer (cancer vs. normal); Golub Leukaemia dataset (acute lymphoblastic leukaemia vs. acute myeloid leukaemia) and Lung cancer (malignant pleural mesothelioma or adenocarcinoma). The results obtained by BIRF are compared to those of Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Naive Bayes (NB) classifiers. The BIRF approach results are competitive with these state-of-art methods and better in some cases. The Local Principal Component (LPC) algorithm introduced in this thesis for visualization is validated on three datasets: Childhood Leukaemia, Swiss-roll and Iris datasets. Significant results are achieved with LPC algorithm in comparison to other methods including local linear embedding and principal component analysis. This thesis introduces a Weight Learning Genetic algorithm based on genetic algorithms for feature weighting in the nearest neighbour classifier. The results show that a weighted nearest neighbour classifier with weights generated from the Weight Learning Genetic algorithm produces better results than the un-weighted nearest neighbour algorithm. This thesis also applies synthetic minority over sampling technique (SMOTE) to increase the number of points in the minority classes and reduce the effect of imbalanced classes. The results show that the minority class becomes recognised by the nearest neighbour classifier. SMOTE also reduces the effect of imbalanced classes in predicting the class of new queries especially if the query sample should be classified to the minority class

Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy

In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes.Postprint (published version