Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Classification of Pulmonary Nodules by Using Hybrid Features

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity)

Evolutionary computation-based feature selection for finding a stable set of features in high-dimensional data

Evolutionary Computation (EC) algorithms have proved to work well for feature selection because they are powerful search techniques and can produce multiple good solutions. However, they suffer from some limitations for real world applications. Firstly, ECs require high computation time as they evaluate many solutions at each iteration. Secondly, a classifier is usually used as their fitness function which causes the selected subset to perform well only on the utilised classifier (e.g. classifier-bias). Lastly, ECs, as stochastic search methods, return a different final subset in different runs which poses a problem for finding a stable set of features (e.g. stability issue). To address computation time and classifier-bias limitations, this thesis proposes a new two-stage selection approach called filter/filter in which two filter feature selection algorithms are combined. In the first stage, a ranking algorithm forms a reduced dataset by selecting the most informative features from the original dataset. In the second stage, the reduced dataset is fed to a novel EC algorithm to select final feature subset. This new EC algorithm is a Tabu search hybridised with an Asexual Genetic Algorithm called TAGA. TAGA benefits from new search components and solution representation which can effectively reduce computation time. To select a classifier-unbiased final subset, a statistical criterion is used as the fitness function which evaluates the subset independent of any classifier. Experiments show that the proposed filter/filter requires an acceptable computation time and selects more classifier-unbiased features compared to the state-of-the-arts. To find a stable set of features, a novel Generalisation Power Index (GPI) is proposed to analyse the generalisation power of final subsets of an EC in several runs. Generalisation power refers to performance capability of a subset over wide range of classifiers. Computation results confirm that GPI is able to find a stable set of features which achieves near optimal accuracy when used to train various classifiers. To ex amine the suitability of the proposed methods for real-world applications, the filter/filter approach and GPI are integrated to select a stable set of features for METABRIC breast cancer subtype classification problem. Experimental results show that this integration not only can address the limitations of ECs for a real-world biomedical feature selection problem but it performs better than alternatives methods

A Hybrid Metaheuristics based technique for Mutation Based Disease Classification

Due to recent advancements in computational biology, DNA microarray technology has evolved as a useful tool in the detection of mutation among various complex diseases like cancer. The availability of thousands of microarray datasets makes this field an active area of research. Early cancer detection can reduce the mortality rate and the treatment cost. Cancer classification is a process to provide a detailed overview of the disease microenvironment for better diagnosis. However, the gene microarray datasets suffer from a curse of dimensionality problems also the classification models are prone to be overfitted due to small sample size and large feature space. To address these issues, the authors have proposed an Improved Binary Competitive Swarm Optimization Whale Optimization Algorithm (IBCSOWOA) for cancer classification, in which IBCSO has been employed to reduce the informative gene subset originated from using minimum redundancy maximum relevance (mRMR) as filter method. The IBCSOWOA technique has been tested on an artificial neural network (ANN) model and the whale optimization algorithm (WOA) is used for parameter tuning of the model. The performance of the proposed IBCSOWOA is tested on six different mutation-based microarray datasets and compared with existing disease prediction methods. The experimental results indicate the superiority of the proposed technique over the existing nature-inspired methods in terms of optimal feature subset, classification accuracy, and convergence rate. The proposed technique has illustrated above 98% accuracy in all six datasets with the highest accuracy of 99.45% in the Lung cancer dataset

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios

Background and objective: Many classification tasks in translational bioinformatics and genomics are characterized by the high dimensionality of potential features and unbalanced sample distribution among classes. This can affect classifier robustness and increase the risk of overfitting, curse of dimensionality and generalization leaks; furthermore and most importantly, this can prevent obtaining adequate patient stratification required for precision medicine in facing complex diseases, like cancer. Setting up a feature selection strategy able to extract only proper predictive features by removing irrelevant, redundant, and noisy ones is crucial to achieving valuable results on the desired task. Methods: We propose a new feature selection approach, called ReRa, based on supervised Relevance-Redundancy assessments. ReRa consists of a customized step of relevance-based filtering, to identify a reduced subset of meaningful features, followed by a supervised similarity-based procedure to minimize redundancy. This latter step innovatively uses a combination of global and class-specific similarity assessments to remove redundant features while preserving those differentiated across classes, even when these classes are strongly unbalanced. Results: We compared ReRa with several existing feature selection methods to obtain feature spaces on which performing breast cancer patient subtyping using several classifiers: we considered two use cases based on gene or transcript isoform expression. In the vast majority of the assessed scenarios, when using ReRa-selected feature spaces, the performances were significantly increased compared to simple feature filtering, LASSO regularization, or even MRmr - another Relevance-Redundancy method. The two use cases represent an insightful example of translational application, taking advantage of ReRa capabilities to investigate and enhance a clinically-relevant patient stratification task, which could be easily applied also to other cancer types and diseases. Conclusions: ReRa approach has the potential to improve the performance of machine learning models used in an unbalanced classification scenario. Compared to another Relevance-Redundancy approach like MRmr, ReRa does not require tuning the number of preserved features, ensures efficiency and scalability over huge initial dimensionalities and allows re-evaluation of all previously selected features at each iteration of the redundancy assessment, to ultimately preserve only the most relevant and class-differentiated features

Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data

The main theme of this dissertation focuses on multivariate correlation analysis on different data types and we identify and define various research gaps in the same. For the defined research gaps we develop novel techniques that address relevance of features to the target and redundancy of features amidst themselves. Our techniques aim at handling homogeneous data, i.e., only continuous or categorical features, mixed data, i.e., continuous and categorical features, and time serie