Evolutionary approaches for feature selection in biological data

Data mining techniques have been used widely in many areas such as business, science, engineering and medicine. The techniques allow a vast amount of data to be explored in order to extract useful information from the data. One of the foci in the health area is finding interesting biomarkers from biomedical data. Mass throughput data generated from microarrays and mass spectrometry from biological samples are high dimensional and is small in sample size. Examples include DNA microarray datasets with up to 500,000 genes and mass spectrometry data with 300,000 m/z values. While the availability of such datasets can aid in the development of techniques/drugs to improve diagnosis and treatment of diseases, a major challenge involves its analysis to extract useful and meaningful information. The aims of this project are: 1) to investigate and develop feature selection algorithms that incorporate various evolutionary strategies, 2) using the developed algorithms to find the “most relevant” biomarkers contained in biological datasets and 3) and evaluate the goodness of extracted feature subsets for relevance (examined in terms of existing biomedical domain knowledge and from classification accuracy obtained using different classifiers). The project aims to generate good predictive models for classifying diseased samples from control

Pareto optimal-based feature selection framework for biomarker identification

Numerous computational techniques have been applied to identify the vital features of gene expression datasets in aiming to increase the efficiency of biomedical applications. The classification of microarray data samples is an important task to correctly recognise diseases by identifying small but clinically meaningful genes. However, identification of disease representative genes or biomarkers in high dimensional microarray gene-expression datasets remains a challenging task. This thesis investigates the viability of Pareto optimisation in identifying relevant subsets of biomarkers in high-dimensional microarray datasets. A robust Pareto Optimal based feature selection framework for biomarker discovery is then proposed. First, a two-stage feature selection approach using ensemble filter methods and Pareto Optimality is proposed. The integration of the multi-objective approach employing Pareto Optimality starts with well-known filter methods applied to various microarray gene-expression datasets. Although filter methods provide ranked lists of features, they do not give information about optimum subsets of features, which are namely genes in this study. To address this limitation, the Pareto Optimality is incorporated along with filter methods. The robustness of the proposed framework is successfully demonstrated on several well-known microarray gene expression datasets and it is shown to achieve comparable or up to 100% predictive accuracy with comparatively fewer features. Better performance results are obtained in comparison with other approaches, which are single-objective approaches. Furthermore, cross-validation and k-fold approaches are integrated into the framework, which can enhance the over-fitting problem and the gene selection process is subsequently more accurate under various conditions. Then the proposed framework is developed in several phases. The Sequential Forward Selection method (SFS) is first used to represent wrapper techniques, and the developed Pareto Optimality based framework is applied multiple times and tested on different data types. Given the nature of most real-life data, imbalanced classes are examined using the proposed framework. The classifier achieves high performance at a similar level of different cases using the proposed Pareto Optimal based feature selection framework, which has a novel structure for imbalanced classes. Comparable or better gene subset sizes are obtained using the proposed framework. Finally, handling missing data within the proposed framework is investigated and it is demonstrated that different data imputation methods can also help in the effective integration of various feature selection methods

A data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case study

[EN] Gene selection (or feature selection) from DNA-microarray data can be focused on different techniques, which generally involve statistical tests, data mining and machine learning. In recent years there has been an increasing interest in using hybrid-technique sets to face the problem of meaningful gene selection; nevertheless, this issue remains a challenge. In an effort to address the situation, this paper proposes a novel hybrid framework based on data mining techniques and tuned to select gene subsets, which are meaningfully related to the target disease conducted in DNA-microarray experiments. For this purpose, the framework above deals with approaches such as statistical significance tests, cluster analysis, evolutionary computation, visual analytics and boundary points. The latter is the core technique of our proposal, allowing the framework to define two methods of gene selection. Another novelty of this work is the inclusion of the age of patients as an additional factor in our analysis, which can leading to gaining more insight into the disease. In fact, the results reached in this research have been very promising and have shown their biological validity. Hence, our proposal has resulted in a methodology that can be followed in the gene selection process from DNA-microarray data

Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics

Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.

Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection

BACKGROUND: Acupuncture has been practiced in China for thousands of years as part of the Traditional Chinese Medicine (TCM) and has gradually accepted in western countries as an alternative or complementary treatment. However, the underlying mechanism of acupuncture, especially whether there exists any difference between varies acupoints, remains largely unknown, which hinders its widespread use. RESULTS: In this study, we develop a novel Linear Programming based Feature Selection method (LPFS) to understand the mechanism of acupuncture effect, at molecular level, by revealing the metabolite biomarkers for acupuncture treatment. Specifically, we generate and investigate the high-throughput metabolic profiles of acupuncture treatment at several acupoints in human. To select the subsets of metabolites that best characterize the acupuncture effect for each meridian point, an optimization model is proposed to identify biomarkers from high-dimensional metabolic data from case and control samples. Importantly, we use nearest centroid as the prototype to simultaneously minimize the number of selected features and the leave-one-out cross validation error of classifier. We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR). We find that our LPFS method tends to reveal a small set of metabolites with small standard deviation and large shifts, which exactly serves our requirement for good biomarker. Biologically, several metabolite biomarkers for acupuncture treatment are revealed and serve as the candidates for further mechanism investigation. Also biomakers derived from five meridian points, Zusanli (ST36), Liangmen (ST21), Juliao (ST3), Yanglingquan (GB34), and Weizhong (BL40), are compared for their similarity and difference, which provide evidence for the specificity of acupoints. CONCLUSIONS: Our result demonstrates that metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture. Comparing with other existing methods, LPFS shows better performance to select a small set of key molecules. In addition, LPFS is a general methodology and can be applied to other high-dimensional data analysis, for example cancer genomics

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

Multi-objective Feature Selection in Remote Health Monitoring Applications

Radio frequency (RF) signals have facilitated the development of non-contact human monitoring tasks, such as vital signs measurement, activity recognition, and user identification. In some specific scenarios, an RF signal analysis framework may prioritize the performance of one task over that of others. In response to this requirement, we employ a multi-objective optimization approach inspired by biological principles to select discriminative features that enhance the accuracy of breathing patterns recognition while simultaneously impeding the identification of individual users. This approach is validated using a novel vital signs dataset consisting of 50 subjects engaged in four distinct breathing patterns. Our findings indicate a remarkable result: a substantial divergence in accuracy between breathing recognition and user identification. As a complementary viewpoint, we present a contrariwise result to maximize user identification accuracy and minimize the system's capacity for breathing activity recognition.Comment: Under revie