Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data

Mass spectrometry (MS) is currently the most commonly used technology in biochemical research for proteomic analysis. The primary goal of proteomic profiling using mass spectrometry is the classification of samples from different experimental states. To classify the MS samples, the identification of protein or peptides (biomarker detection) that are expressed differently between the classes, is required. However, due to the high dimensionality of the data and the small number of samples, classification of MS data is extremely challenging. Another important aspect of biomarker detection is the verification of the detected biomarker that acts as an intermediate step before passing these biomarkers to the experimental validation stage. Biomarker detection aims at altering the input space of the learning algorithm for improving classification of proteomic or metabolomic data. This task is performed through feature manipulation. Feature manipulation consists of three aspects: feature ranking, feature selection, and feature construction. Genetic programming (GP) is an evolutionary computation algorithm that has the intrinsic capability for the three aspects of feature manipulation. The ability of GP for feature manipulation in proteomic biomarker discovery has not been fully investigated. This thesis, therefore, proposes an embedded methodology for these three aspects of feature manipulation in high dimensional MS data using GP. The thesis also presents a method for biomarker verification, using GP. The thesis investigates the use of GP for both single-objective and multi-objective feature selection and construction. In feature ranking, the thesis proposes a GP-based method for ranking subsets of features by using GP as an ensemble approach. The proposed algorithm uses GP capability to combine the advantages of different feature ranking metrics and evolve a new ranking scheme for the subset of the features selected from the top ranked features. The capability of GP as a classifier is also investigated by this method. The results show that GP can select a smaller number of features and provide a better ranking of the selected features, which can improve the classification performance of five classifiers. In feature construction, this thesis proposes a novel multiple feature construction method, which uses a single GP tree to generate a new set of high-level features from the original set of selected features. The results show that the proposed new algorithm outperforms two feature selection algorithms. In feature selection, the thesis introduces the first GP multi-objective method for biomarker detection, which simultaneously increase the classification accuracy and reduce the number of detected features. The proposed multi-objective method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. This thesis also develops the first multi-objective multiple feature construction algorithm for MS data. The proposed method aims at both maximising the classification performance and minimizing the cardinality of the constructed new high-level features. The results show that GP can dis- cover the complex relationships between the features and can significantly improve classification performance and reduce the cardinality. For biomarker verification, the thesis proposes the first GP biomarker verification method through measuring the peptide detectability. The method solves the imbalance problem in the data and shows improvement over the benchmark algorithms. Also, the algorithm outperforms a well-known peptide detection method. The thesis also introduces a new GP method for alignment of MS data as a preprocessing stage, which will further help in improving the biomarker detection process

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.

Gene selection and classification in autism gene expression data

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are currently diagnosed on the basis of abnormal stereotyped behaviour as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been attributed to the disorder, no single gene is applicable to more than 1–2% of the general ASD population. Despite extensive efforts, definitive genes that contribute to autism susceptibility have yet to be identified. The major problems in dealing with the gene expression dataset of autism include the presence of limited number of samples and large noises due to errors of experimental measurements and natural variation. In this study, a systematic combination of three important filters, namely t-test (TT), Wilcoxon Rank Sum (WRS) and Feature Correlation (COR) are applied along with efficient wrapper algorithm based on geometric binary particle swarm optimization-support vector machine (GBPSO-SVM), aiming at selecting and classifying the most attributed genes of autism. A new approach based on the criterion of median ratio, mean ratio and variance deviations is also applied to reduce the initial dataset prior to its involvement. Results showed that the most discriminative genes that were identified in the first and last selection steps concluded the presence of a repetitive gene (CAPS2), which was assigned as the most ASD risk gene. The fused result of genes subset that were selected by the GBPSO-SVM algorithm increased the classification accuracy to about 92.10%, which is higher than those reported in literature for the same autism dataset. Noticeably, the application of ensemble using random forest (RF) showed better performance compared to that of previous studies. However, the ensemble approach based on the employment of SVM as an integrator of the fused genes from the output branches of GBPSO-SVM outperformed the RF integrator. The overall improvement was ascribed to the selection strategies that were taken to reduce the dataset and the utilization of efficient wrapper based GBPSO-SVM algorithm

Layered genetic programming for feature extraction in classification problems

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsGenetic programming has been proven to be a successful technique for feature extraction in various applications. In this thesis, we present a Layered Genetic Programming system which implements genetic programming-based feature extraction mechanism. The proposed system uses a layered structure where instead of evolving just one population of individuals, several populations are evolved sequentially. Each such population transforms the input data received from the previous population into a lower dimensional space with the aim of improving classification performance. The performance of the proposed system was experimentally tested on 5 real-world problems using different dimensionality reduction step sizes and different classifiers. The proposed method was able to outperform a simple classifier applied directly on the original data on two problems. On the remaining problems, the classifier performed better using the original data. The best solutions were often obtained in the first few layers which implied that increasing the size of the system, i.e. adding more layers was not useful. However, the layered structure allowed control of the size of individuals

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Improving land cover classification using genetic programming for feature construction

Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.publishersversionpublishe

Optimization Based Tumor Classification from Microarray Gene Expression Data

An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers

Microarray Data Mining and Gene Regulatory Network Analysis

The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer from low accuracy or computational complexity. To overcome such limitations, the following strategies were proposed to integrate bioinformatics data mining techniques with existing GRN inference algorithms, which enables the discovery of novel biological knowledge. An integrated statistical and machine learning (ISML) pipeline was developed for feature selection and classifier genes identification to solve the challenges of the curse of dimensionality problem as well as the huge search space. Using the selected classifier genes as seeds, a scale-up technique is applied to search through major databases of genetic interaction networks, metabolic pathways, etc. By curating relevant genes and blasting genomic sequences of non-model organisms against well-studied genetic model organisms, a reference gene regulatory network for less-studied organisms was built and used both as prior knowledge and model validation for GRN reconstructions. Networks of gene interactions were inferred using a Dynamic Bayesian Network (DBN) approach and were analyzed for elucidating the dynamics caused by perturbations. Our proposed pipelines were applied to investigate molecular mechanisms for chemical-induced reversible neurotoxicity