High Dimensional Gene Expression Datasets Research Articles

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.
 Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.
 Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.
 OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.
 METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.
 RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.
 CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Gene selection with Game Shapley Harris hawks optimizer for cancer classification

Cancer disease has been classified as a perilous disease for humans, being the second leading cause of death globally. Even advanced-stage diagnosis may not be effective in preventing patient mortality. Therefore, it is important to establish a sustainable framework that predicts reliable estimates for an early cancer diagnosis. In this paper, a new two-phase feature (gene) selection approach is presented. In the first phase, the kernel Shapley value (kSV) that is based on the cooperative game-theoretic feature extraction approach is utilized to extract the important feature from the high dimensional gene expression data. In the second phase, Harris hawks optimizer (HHO) algorithm is utilized to further optimize the most effective feature extracted by kSV. Next, to evaluate the effectiveness of our proposed algorithm, we conduct extensive experiments on eight benchmark high-dimensional gene expression datasets, comparing them with other state-of-the-art techniques. We employ three classifiers, namely support vector machines (SVM), Naive Bayes (NB), and K-nearest neighbors (KNN), to assess the selected genes efficacy and their impact on classification accuracy. The experimental results demonstrate that the proposed method, particularly when combined with the SVM classifier, outperforms other gene selection methods. The evaluation metrics, including accuracy, precision, recall, F1-score, ROC-AUC, box plot, and radar plot, consistently indicate the superiority of kSV-HHO across all tested datasets. Moreover, the comparative and statistical analysis reveals that our proposed method excels in identifying the most relevant features for cancer diagnosis compared to other gene selection approaches. This makes our framework a valuable tool for cancer research and clinical practice, potentially enhancing the accuracy of early cancer diagnosis using high-dimensional gene expression biomedical data.

Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio.

Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method (WSNR) are also compared with four well known feature selection methods. We found that the (WSNR) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that (WSNR) outperforms all the other methods included in the study.

An entropy-based density peak clustering for numerical gene expression datasets

In molecular biology, gene expression analysis is one of the important research areas which deals with identifying the genes having similar functionality known as co-expressed genes. Data mining techniques like clustering are frequently employed for grouping gene expressions with similar functional characteristics. Numerous such clustering techniques are available for gene expression analysis. Usually, gene expression datasets are a result of millions of measurements due to which they possess high dimensionality and noise which makes the conventional distance measures ineffective. On the other hand, entropy-based distance computation is much more efficient to capture the inhomogeneity in large dimensional data and is also quite insensitive to noise. To exploit these advantages, we propose a novel method to compute the density distribution of data points in high-dimensional and noisy gene expression datasets using the concept of entropy. After obtaining the density distribution, an existing technique known as “Extreme Clustering” is used to obtain the desired clusters present in the gene expressions dataset. The proposed technique is implemented and evaluated on diversified microarray gene expression datasets. Experiment results show that the proposed technique outperforms other popular density-based techniques in terms of cluster quality, robustness against noise, and biological significance of the genes within the clusters.

Efficient Selection of Gaussian Kernel SVM Parameters for Imbalanced Data

For medical data mining, the development of a class prediction model has been widely used to deal with various kinds of data classification problems. Classification models especially for high-dimensional gene expression datasets have attracted many researchers in order to identify marker genes for distinguishing any type of cancer cells from their corresponding normal cells. However, skewed class distributions often occur in the medical datasets in which at least one of the classes has a relatively small number of observations. A classifier induced by such an imbalanced dataset typically has a high accuracy for the majority class and poor prediction for the minority class. In this study, we focus on an SVM classifier with a Gaussian radial basis kernel for a binary classification problem. In order to take advantage of an SVM and to achieve the best generalization ability for improving the classification performance, we will address two important problems: the class imbalance and parameter selection during SVM parameter optimization. First of all, we proposed a novel adjustment method called b-SVM, for adjusting the cutoff threshold of the SVM. Second, we proposed a fast and simple approach, called the Min-max gamma selection, to optimize the model parameters of SVMs without carrying out an extensive k-fold cross validation. An extensive comparison with a standard SVM and well-known existing methods are carried out to evaluate the performance of our proposed algorithms using simulated and real datasets. The experimental results show that our proposed algorithms outperform the over-sampling techniques and existing SVM-based solutions. This study also shows that the proposed Min-max gamma selection is at least 10 times faster than the cross-validation selection based on the average running time on six real datasets.

A multi-objective evolutionary algorithm with decomposition and the information feedback for high-dimensional medical data

High-dimensional medical data often leads to a phenomenon known as the ”curse of dimensionality,” which causes additional memory and high training costs, as well as degrading the generalization capacity of learning algorithms. To address this issue, a multi-objective evolutionary algorithm that integrates decomposition and the information feedback model (IFMMOEAD) is proposed for high-dimensional medical data. This algorithm not only considers the number of selected features, but also classification accuracy and correlation measures of features when feature dimensionality reduction is executed. The property of IFMMOEAD is first verified by standard benchmarks DTLZ1–DTLZ7. Then, it is used to develop machine learning algorithms for thirty-five high-dimensional cancer gene expression data sets, showing excellent potential for high-dimensional medical machine learning. Finally, the IFMMOEAD is applied to empirical clinical data of multiple myeloma, significantly outperforming existing algorithms in terms of normalized mutual information and adjusted rand index metrics. We suggest that this algorithm could be implemented in medical information systems as a promising technique for high-dimensional medical problems.

A high-dimensional feature selection method based on modified Gray Wolf Optimization

For data mining tasks on high-dimensional data, feature selection is a necessary pre-processing stage that plays an important role in removing redundant or irrelevant features and improving classifier performance. The Gray Wolf optimization algorithm is a global search mechanism with promising applications in feature selection, but tends to stagnate in high-dimensional problems with locally optimal solutions. In this paper, a modified gray wolf optimization algorithm is proposed for feature selection of high-dimensional data. The algorithm introduces ReliefF algorithm and Coupla entropy in the initialization process, which effectively improves the quality of the initial population. In addition, modified gray wolf optimization includes two new search strategies: first, a competitive guidance strategy is proposed to update individual positions, which make the algorithm’s search more flexible; second, a differential evolution-based leader wolf enhancement strategy is proposed to find a better position where the leader wolf may exist and replace it, which can prevent the algorithm from falling into local optimum. The results on 10 high-dimensional small-sample gene expression datasets demonstrate that the proposed algorithm selects less than 0.67% of the features, improves the classification accuracy while further reducing the number of features, and obtains very competitive results compared with some advanced feature selection methods. The comprehensive study analysis shows that proposed algorithm better balances the exploration and exploration balance, and the two search strategies are conducive to the improvement of gray wolf optimization search capability.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets. Results: The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses. Conclusion: The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

HighMLR: An open-source package for R with machine learning for feature selection in high dimensional cancer clinical genome time to event data

Machine learning techniques, popularly used as a tool for dimensionality reduction and pattern recognition of features, have been utilized extensively in data mining. In survival analysis, where the primary outcome is the time until a specific event occurs, identifying relevant features for building an efficient prediction model is essential. This is where machine learning can be a suitable option. However, there is an existing gap in utilizing machine learning techniques in high-dimensional survival data due to the non-availability of convenient programming functions and packages. In this article, we have developed an efficient machine learning procedure for analyzing survival data associated with high-dimensional gene expressions. Though there are several R libraries available for performing machine learning, no package support is available to implement machine learning with classification on high-dimensional survival data. highMLR, our developed R package, is capable of implementing machine learning methods on high dimensional survival data and provides a way of feature selection based on the logarithmic loss function. Several statistical methods for survival analysis have been incorporated into this machine learning algorithm. A high-dimensional gene expression dataset has been analyzed using the proposed R library to show its efficacy in feature selection.

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.

BackgroundMachine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis.MethodsIn this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets.ResultsTo assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.

Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis

BackgroundLongitudinal gene expression analysis and survival modeling have been proved to add valuable biological and clinical knowledge. This study proposes a novel framework to discover gene signatures and patterns in a high-dimensional time series transcriptomics data and to assess their association with hospital length of stay.MethodsWe investigated a longitudinal and high-dimensional gene expression dataset from 168 blunt-force trauma patients followed during the first 28 days after injury. To model the length of stay, an initial dimensionality reduction step was performed by applying Cox regression with elastic net regularization using gene expression data from the first hospitalization days. Also, a novel methodology to impute missing values to the genes selected previously was proposed. We then applied multivariate time series (MTS) clustering to analyse gene expression over time and to stratify patients with similar trajectories. The validation of the patients’ partitions obtained by MTS clustering was performed using Kaplan-Meier curves and log-rank tests.ResultsWe were able to unravel 22 genes strongly associated with hospital’s discharge. Their expression values in the first days after trauma showed to be good predictors of the length of stay. The proposed mixed imputation method allowed to achieve a complete dataset of short time series with a minimum loss of information for the 28 days of follow-up. MTS clustering enabled to group patients with similar genes trajectories and, notably, with similar discharge days from the hospital. Patients within each cluster have comparable genes’ trajectories and may have an analogous response to injury.ConclusionThe proposed framework was able to tackle the joint analysis of time-to-event information with longitudinal multivariate high-dimensional data. The application to length of stay and transcriptomics data revealed a strong relationship between gene expression trajectory and patients’ recovery, which may improve trauma patient’s management by healthcare systems. The proposed methodology can be easily adapted to other medical data, towards more effective clinical decision support systems for health applications.

MMCo-Clus – An Evolutionary Co-clustering Algorithm for Gene Selection

In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Curse of dimensionality</i> . To overcome this problem, the dimensionality reduction through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature selection</i> becomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> ulti-objective optimization based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> ulti-view <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>-Clus</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> tering algorithm (named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MMCo-</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Clus</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> ). A popular evolutionary technique – Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clus</i> algorithm considering the constructed views to identify a set of “good” co-clustering solutions. Finally, based on a concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consensus operation</i> on the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed method's potency.

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.

Weighted L1-norm Logistic Regression for Gene Selection of Microarray Gene Expression Classification

The classification of cancer is a significant application of the DNA microarray data. Gene selection methods are ordinarily used handle the issue of high-dimensionality of microarray data to enable experts to diagnose and classify cancer with high accuracy. The penalized logistic regression (PLR) technique is usually used in the dimensionality reduction of the high-dimensional gene expression data sets to remove irrelevant and redundant predictors from the binary logistic regression model. One of the regularization techniques used to achieve this goal is the least absolute shrinkage and selection operator (Lasso). However, this technique has been criticized for being biased in the selection of genes. The adaptive Lasso was usually proposed by assigning an initial weight to each gene to address the selection bias. This paper is concerned with adapting PLR to improve its capability in classification and gene selection, in the sense of accuracy, by introducing the one-dimensional weighted Mahalanobis distance (1-DWM) for each gene as an initial weight inside L1-norm. By experiments, this proposed method, denoted by adaptive penalized logistic regression (APLR), gives more accurate results compared with other famous methods in this regard. The proposed method is applied to some real high-dimensional gene expression data sets in order to demonstrate its efficiency in terms of classification accuracy and selection of gene. Therefore, the proposed method could be utilized in other studies implementing gene selection in the area of classification of high dimensional cancer data sets.

Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches.

Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.

Sparse feature selection: Relevance, redundancy and locality structure preserving guided by pairwise constraints

Selection of features as a pre-processing stage is an essential issue in many machine learning tasks (such as classification) to reduce data dimensionality as there are many irrelevant and redundant features that can mislead the learning process. Graph-based sparse feature selection is developed to overcome this issue. In this paper, a novel graph-based sparse feature selection method is proposed that take into account both issues: relevancy and redundancy analysis. An empirical loss function joining with ℓ1-norm regularization term is proposed to overcome the relevancy issue and the redundancy issue is overcome by introducing a regularization term that prefers uncorrelated features. Furthermore, the proposed learning procedure is guided by two different sets of supervision information as pairs of must-linked (positive) and cannot-linked (negative) constraint sets to select a discriminative feature subset. These guiding information besides the whole data points are encoded in the graph Laplacian matrix that preserves the locality structure of the original data. The graph Laplacian matrix is constructed by two different approaches. Our first approach tries to preserve the structure of the original data guided just by the positive data points (unique samples in the must-linked constraints), and our second approach applies a normalized adapted affinity matrix to embed the pairwise must-linked and cannot-linked constraints as well as the neighborhood relationships information, all together. The experimental results on a number of several datasets from the University of California-Irvine machine learning repository, in addition to several high dimensional gene expression datasets show the efficacy of the proposed methods in the classification tasks compared to several powerful feature selection methods.

Pathways Enrichment Analysis of Gene Expression Data in Type 2 Diabetes.

Profiling genome-wide transcriptional changes with advanced high-throughput transcriptional profiling techniques has led to a revolution in biomedical science. It has been challenging to handle the massive data generated by these techniques and draw meaningful conclusions from it. Therefore, computational biologists have developed a number of innovative methods of varying complexity and effectiveness to analyze such complex data. Over the past decade, rich information in pathway repositories has attracted and motivated researchers to incorporate such existing biological knowledge into computational analysis tools to develop what is known as pathway enrichment analysis tools. This chapter describes a new sophisticated pathway enrichment tool that exploits topology of pathway as well as expression of significantly changed genes to identify biologically significant pathways for high-dimensional gene expression datasets. Also, we demonstrate the use of this tool to analyze gene expression data from a type 2 diabetes dataset to identify a list of significantly enriched metabolic pathways.

Gaussian mixture copulas for high-dimensional clustering and dependency-based subtyping

The identification of sub-populations of patients with similar characteristics, called patient subtyping, is important for realizing the goals of precision medicine. Accurate subtyping is crucial for tailoring therapeutic strategies that can potentially lead to reduced mortality and morbidity. Model-based clustering, such as Gaussian mixture models, provides a principled and interpretable methodology that is widely used to identify subtypes. However, they impose identical marginal distributions on each variable; such assumptions restrict their modeling flexibility and deteriorates clustering performance. In this paper, we use the statistical framework of copulas to decouple the modeling of marginals from the dependencies between them. Current copula-based methods cannot scale to high dimensions due to challenges in parameter inference. We develop HD-GMCM, that addresses these challenges and, to our knowledge, is the first copula-based clustering method that can fit high-dimensional data. Our experiments on real high-dimensional gene-expression and clinical datasets show that HD-GMCM outperforms state-of-the-art model-based clustering methods, by virtue of modeling non-Gaussian data and being robust to outliers through the use of Gaussian mixture copulas. We present a case study on lung cancer data from TCGA. Clusters obtained from HD-GMCM can be interpreted based on the dependencies they model, that offers a new way of characterizing subtypes. Empirically, such modeling not only uncovers latent structure that leads to better clustering but also meaningful clinical subtypes in terms of survival rates of patients. An implementation of HD-GMCM in R is available at: https://bitbucket.org/cdal/hdgmcm/. Supplementary data are available at Bioinformatics online.

Visualisation and Modelling of High-Dimensional Cancerous Gene Expression Dataset

The increase in the number of dimensions of cancerous gene expression dataset causes an increase in complexity, misinterpretation and decrease in the visualisation of the particular dataset for further analysis. Therefore, dimensionality reduction, visualisation and modelling tasks of these dataset become challenging. In this paper, a framework is developed which helps to understand, visualise and model high-dimensional cancerous gene expression dataset into lower dimensions which may be helpful in revealing cancer mechanism and diagnosis. Initially, cancerous gene expression datasets are preprocessed to make them complete, precise and efficient; and principal component analysis is applied for dimensionality reduction and visualisation purpose. The regression is used to model the cancerous gene expression dataset so that type of association (linear or nonlinear) and directions between gene profiles may be estimated. To assess the performance of the developed framework, three different types of cancerous gene expression datasets are taken namely: breast (GEO Acc. No. GDS5076), lung (GEO Acc. No. GDS5040) and prostate (GEO Acc. No. GDS5072) which are publicly available. To validate the results of the regression the cross-validation method is used. The results revealed that a linear approach is to be used for prostate cancer dataset and nonlinear approach for breast and lung cancer datasets in finding an association between gene pairs.

Fuzzy magnetic optimization clustering algorithm with its application to health care

Clustering is an important tool for data mining and knowledge discovery that helps in revealing hidden structures and “clusters” found in large data sets. Fuzzy C-means (FCM) is considered to be popular data clustering method due to its capability of clustering the datasets that are uncertain, vague and/or are otherwise difficult to cluster. Although, noted both for its simplicity of implementation and its output validity, performance of FCM usually gets affected in case of poor initialization resulting in the algorithm getting trapped into a local optimum. To overcome this shortcoming, the present study proposes a novel clustering algorithm called fuzzy magnetic optimization clustering (Fuzzy-MOC) which embeds the concept of fuzzy clustering into magnetic optimization algorithm. In Fuzzy-MOC, the data points apply force directly to the magnetic particles due to which the particles change their positions in the feature space. Magnetic particles are attracted by their neighbours assumed to be in a lattice like structure. The proposed algorithm is evaluated on a set of 16 benchmark datasets taken from the UCI Machine Learning Repository including high dimensional gene expression dataset. Experimental results demonstrate that Fuzzy-MOC outperforms the other state-of-the-art algorithms in terms of different performance metrics like F1, accuracy, purity and RI measure.