Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum- Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.This work was supported by Project TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness -MINECO- and the European Regional Development Fund -ERDF) and Junta de Andalucı´a (P12–TIC–2082)

Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data

BACKGROUND: Due to the large number of genes in a typical microarray dataset, feature selection looks set to play an important role in reducing noise and computational cost in gene expression-based tissue classification while improving accuracy at the same time. Surprisingly, this does not appear to be the case for all multiclass microarray datasets. The reason is that many feature selection techniques applied on microarray datasets are either rank-based and hence do not take into account correlations between genes, or are wrapper-based, which require high computational cost, and often yield difficult-to-reproduce results. In studies where correlations between genes are considered, attempts to establish the merit of the proposed techniques are hampered by evaluation procedures which are less than meticulous, resulting in overly optimistic estimates of accuracy. RESULTS: We present two realistically evaluated correlation-based feature selection techniques which incorporate, in addition to the two existing criteria involved in forming a predictor set (relevance and redundancy), a third criterion called the degree of differential prioritization (DDP). DDP functions as a parameter to strike the balance between relevance and redundancy, providing our techniques with the novel ability to differentially prioritize the optimization of relevance against redundancy (and vice versa). This ability proves useful in producing optimal classification accuracy while using reasonably small predictor set sizes for nine well-known multiclass microarray datasets. CONCLUSION: For multiclass microarray datasets, especially the GCM and NCI60 datasets, DDP enables our filter-based techniques to produce accuracies better than those reported in previous studies which employed similarly realistic evaluation procedures

Characteristics of predictor sets found using differential prioritization

<p>Abstract</p> <p>Background</p> <p>Feature selection plays an undeniably important role in classification problems involving high dimensional datasets such as microarray datasets. For filter-based feature selection, two well-known criteria used in forming predictor sets are relevance and redundancy. However, there is a third criterion which is at least as important as the other two in affecting the efficacy of the resulting predictor sets. This criterion is the degree of differential prioritization (DDP), which varies the emphases on relevance and redundancy depending on the value of the DDP. Previous empirical works on publicly available microarray datasets have confirmed the effectiveness of the DDP in molecular classification. We now propose to establish the fundamental strengths and merits of the DDP-based feature selection technique. This is to be done through a simulation study which involves vigorous analyses of the characteristics of predictor sets found using different values of the DDP from toy datasets designed to mimic real-life microarray datasets.</p> <p>Results</p> <p>A simulation study employing analytical measures such as the distance between classes before and after transformation using principal component analysis is implemented on toy datasets. From these analyses, the necessity of adjusting the differential prioritization based on the dataset of interest is established. This conclusion is supported by comparisons against both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods, which demonstrates the superiority of the DDP-based feature selection technique. Reapplying similar analyses to real-life multiclass microarray datasets provides further confirmation of our findings and of the significance of the DDP for practical applications.</p> <p>Conclusion</p> <p>The findings have been achieved based on analytical evaluations, not empirical evaluation involving classifiers, thus providing further basis for the usefulness of the DDP and validating the need for unequal priorities on relevance and redundancy during feature selection for microarray datasets, especially highly multiclass datasets.</p

Classification and feature selection algorithms for multi-class CGH data

Recurrent chromosomal alterations provide cytological and molecular positions for the diagnosis and prognosis of cancer. Comparative genomic hybridization (CGH) has been useful in understanding these alterations in cancerous cells. CGH datasets consist of samples that are represented by large dimensional arrays of intervals. Each sample consists of long runs of intervals with losses and gains

ANMM4CBR: a case-based reasoning method for gene expression data classification

<p>Abstract</p> <p>Background</p> <p>Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.</p> <p>Method</p> <p>In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.</p> <p>Results</p> <p>The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and <it>k </it>nearest neighbor (<it>k</it>NN), especially when the data contains a high level of noise.</p> <p>Availability</p> <p>The source code is attached as an additional file of this paper.</p

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Simple but Not Simplistic: Reducing the Complexity of Machine Learning Methods

Programa Oficial de Doutoramento en Computación . 5009V01[Resumo] A chegada do Big Data e a explosión do Internet das cousas supuxeron un gran reto para os investigadores en Aprendizaxe Automática, facendo que o proceso de aprendizaxe sexa mesmo roáis complexo. No mundo real, os problemas da aprendizaxe automática xeralmente teñen complexidades inherentes, como poden ser as características intrínsecas dos datos, o gran número de mostras, a alta dimensión dos datos de entrada, os cambios na distribución entre o conxunto de adestramento e test, etc. Todos estes aspectos son importantes, e requiren novoS modelos que poi dan facer fronte a estas situacións. Nesta tese, abordáronse todos estes problemas, tratando de simplificar o proceso de aprendizaxe automática no escenario actual. En primeiro lugar, realízase unha análise de complexidade para observar como inflúe esta na tarefa de clasificación, e se é posible que a aplicación dun proceso previo de selección de características reduza esta complexidade. Logo, abórdase o proceso de simplificación da fase de aprendizaxe automática mediante a filosofía divide e vencerás, usando un enfoque distribuído. Seguidamente, aplicamos esa mesma filosofía sobre o proceso de selección de características. Finalmente, optamos por un enfoque diferente seguindo a filosofía do Edge Computing, a cal permite que os datos producidos polos dispositivos do Internet das cousas se procesen máis preto de onde se crearon. Os enfoques propostos demostraron a súa capacidade para reducir a complexidade dos métodos de aprendizaxe automática tradicionais e, polo tanto, espérase que a contribución desta tese abra as portas ao desenvolvemento de novos métodos de aprendizaxe máquina máis simples, máis robustos, e máis eficientes computacionalmente.[Resumen] La llegada del Big Data y la explosión del Internet de las cosas han supuesto un gran reto para los investigadores en Aprendizaje Automático, haciendo que el proceso de aprendizaje sea incluso más complejo. En el mundo real, los problemas de aprendizaje automático generalmente tienen complejidades inherentes) como pueden ser las características intrínsecas de los datos, el gran número de muestras, la alta dimensión de los datos de entrada, los cambios en la distribución entre el conjunto de entrenamiento y test, etc. Todos estos aspectos son importantes, y requieren nuevos modelos que puedan hacer frente a estas situaciones. En esta tesis, se han abordado todos estos problemas, tratando de simplificar el proceso de aprendizaje automático en el escenario actual. En primer lugar, se realiza un análisis de complejidad para observar cómo influye ésta en la tarea de clasificación1 y si es posible que la aplicación de un proceso previo de selección de características reduzca esta complejidad. Luego, se aborda el proceso de simplificación de la fase de aprendizaje automático mediante la filosofía divide y vencerás, usando un enfoque distribuido. A continuación, aplicamos esa misma filosofía sobre el proceso de selección de características. Finalmente, optamos por un enfoque diferente siguiendo la filosofía del Edge Computing, la cual permite que los datos producidos por los dispositivos del Internet de las cosas se procesen más cerca de donde se crearon. Los enfoques propuestos han demostrado su capacidad para reducir la complejidad de los métodos de aprendizaje automático tnidicionales y, por lo tanto, se espera que la contribución de esta tesis abra las puertas al desarrollo de nuevos métodos de aprendizaje máquina más simples, más robustos, y más eficientes computacionalmente.[Abstract] The advent of Big Data and the explosion of the Internet of Things, has brought unprecedented challenges to Machine Learning researchers, making the learning task more complexo Real-world machine learning problems usually have inherent complexities, such as the intrinsic characteristics of the data, large number of instauces, high input dimensionality, dataset shift, etc. AH these aspects matter, and can fOI new models that can confront these situations. Thus, in this thesis, we have addressed aH these issues) simplifying the machine learning process in the current scenario. First, we carry out a complexity analysis to see how it inftuences the classification models, and if it is possible that feature selection might result in a deerease of that eomplexity. Then, we address the proeess of simplifying learning with the divide-and-conquer philosophy of the distributed approaeh. Later, we aim to reduce the complexity of the feature seleetion preprocessing through the same philosophy. FinallYl we opt for a different approaeh following the eurrent philosophy Edge eomputing, whieh allows the data produeed by Internet of Things deviees to be proeessed closer to where they were ereated. The proposed approaehes have demonstrated their eapability to reduce the complexity of traditional maehine learning algorithms, and thus it is expeeted that the eontribution of this thesis will open the doors to the development of new maehine learning methods that are simpler, more robust, and more eomputationally efficient

Instance-based concept learning from multiclass DNA microarray data

BACKGROUND: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance. RESULTS: We investigated the performance of instance-based classifiers, including a NN classifier able to assign a degree of class membership to each sample. This model alleviates a major problem of conventional instance-based learners, namely the lack of confidence values for predictions. The model translates the distances to the nearest neighbors into 'confidence scores'; the higher the confidence score, the closer is the considered instance to a pre-defined class. We applied the models to three real gene expression data sets and compared them with state-of-the-art methods for classifying microarray data of multiple classes, assessing performance using a statistical significance test that took into account the data resampling strategy. Simple NN classifiers performed as well as, or significantly better than, their more intricate competitors. CONCLUSION: Given its highly intuitive underlying principles – simplicity, ease-of-use, and robustness – the k-NN classifier complemented by a suitable distance-weighting regime constitutes an excellent alternative to more complex models for multiclass microarray data sets. Instance-based classifiers using weighted distances are not limited to microarray data sets, but are likely to perform competitively in classifications of high-dimensional biological data sets such as those generated by high-throughput mass spectrometry

Bioinformatics applied to human genomics and proteomics: development of algorithms and methods for the discovery of molecular signatures derived from omic data and for the construction of co-expression and interaction networks

[EN] The present PhD dissertation develops and applies Bioinformatic methods and tools to address key current problems in the analysis of human omic data. This PhD has been organised by main objectives into four different chapters focused on: (i) development of an algorithm for the analysis of changes and heterogeneity in large-scale omic data; (ii) development of a method for non-parametric feature selection; (iii) integration and analysis of human protein-protein interaction networks and (iv) integration and analysis of human co-expression networks derived from tissue expression data and evolutionary profiles of proteins. In the first chapter, we developed and tested a new robust algorithm in R, called DECO, for the discovery of subgroups of features and samples within large-scale omic datasets, exploring all feature differences possible heterogeneity, through the integration of both data dispersion and predictor-response information in a new statistic parameter called h (heterogeneity score). In the second chapter, we present a simple non-parametric statistic to measure the cohesiveness of categorical variables along any quantitative variable, applicable to feature selection in all types of big data sets. In the third chapter, we describe an analysis of the human interactome integrating two global datasets from high-quality proteomics technologies: HuRI (a human protein-protein interaction network generated by a systematic experimental screening based on Yeast-Two-Hybrid technology) and Cell-Atlas (a comprehensive map of subcellular localization of human proteins generated by antibody imaging). This analysis aims to create a framework for the subcellular localization characterization supported by the human protein-protein interactome. In the fourth chapter, we developed a full integration of three high-quality proteome-wide resources (Human Protein Atlas, OMA and TimeTree) to generate a robust human co-expression network across tissues assigning each human protein along the evolutionary timeline. In this way, we investigate how old in evolution and how correlated are the different human proteins, and we place all them in a common interaction network. As main general comment, all the work presented in this PhD uses and develops a wide variety of bioinformatic and statistical tools for the analysis, integration and enlighten of molecular signatures and biological networks using human omic data. Most of this data corresponds to sample cohorts generated in recent biomedical studies on specific human diseases