Using emergent clustering methods to analyse short time series gene expression data from childhood leukemia treated with glucocorticoids

Acute lymphoblastic leukemia (ALL) causes the highest number of deaths from cancer in children aged between one and fourteen. The most common treatment for children with ALL is chemotherapy, a cancer treatment that uses drugs to kill cancer cells or stop cell division. The drug and dosage combinations may vary for each child. Unfortunately, chemotherapy treatments may cause serious side effects. Glucocorticoids (GCs) have been used as therapeutic agents for children with ALL for more than 50 years. Common and widely drugs in this class include prednisolone and dexamethasone. Childhood leukemia now has a survival rate of 80% (Pui, Robison, & Look, 2008). The key clinical question is identifying those children who will not respond well to established therapy strategies.GCs regulate diverse biological processes, for example, metabolism, development, differentiation, cell survival and immunity. GCs induce apoptosis and G1 cell cycle arrest in lymphoid cells. In fact, not much is known about the molecular mechanism of GCs sensitivity and resistance, and GCs-induced apoptotic signal transduction pathways and there are many controversial hypotheses about both genes regulated by GCs and potential molecular mechanism of GCs-induced apoptosis. Therefore, understanding the mechanism of this drug should lead to better prognostic factors (treatment response), more targeted therapies and prevention of side effects. GCs induced apoptosis have been studied by using microarray technology in vivo and in vitro on samples consisting of GCs treated ALL cell lines, mouse thymocytes and/or ALL patients. However, time series GCs treated childhood ALL datasets are currently extremely limited. DNA microarrays are essential tools for analysis of expression of many genes simultaneously. Gene expression data show the level of activity of several genes under experimental conditions. Genes with similar expression patterns could belong to the same pathway or have similar function. DNA microarray data analysis has been carried out using statistical analysis as well as machine learning and data mining approaches. There are many microarray analysis tools; this study aims to combine emergent clustering methods to get meaningful biological insights into mechanisms underlying GCs induced apoptosis. In this study, microarray data originated from prednisolone (glucocorticoids) treated childhood ALL samples (Schmidt et al., 2006) (B-linage and T-linage) and collected at 6 and 24 hours after treatment are analysed using four methods: Selforganizing maps (SOMs), Emergent self-organizing maps (ESOM) (Ultsch & Morchen, 2005), the Short Time series Expression Miner (STEM) (Ernst & Bar-Joseph, 2006) and Fuzzy clustering by Local Approximation of MEmbership (FLAME) (Fu & Medico, 2007). The results revealed intrinsic biological patterns underlying the GCs time series data: there are at least five different gene activities happening during the three time points; GCs-induced apoptotic genes were identified; and genes active at both time points or only at 6 hours or 24 hours were determined. Also, interesting gene clusters with membership in already known pathways were found thereby providing promising candidate gens for further inferring GCs induced apoptotic gene regulatory networks

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research

8 pages, 5 figures, 3 additional files.-- Software.[Background] Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine.[Results] In addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques.[Conclusion] geneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org (webcite).This work is supported in part by the projects Research on Translational Bioinformatics (ref. 08VIB6) from University of Vigo and Development of computational tools for the classification and clustering of gene expression data in order to discover meaningful biological information in cancer diagnosis (ref. VA100A08) from JCyL (Spain). The work of D. Glez-Peña is supported by a "María Barbeito" contract from Xunta de Galicia.Peer reviewe

Applying GCS Networks to Fuzzy Discretized Microarray Data for Tumour Diagnosis

Gene expression profiles belonging to DNA microarrays are composed of thousands of genes at the same time, representing the complex relationships between them. In this context, the ability of designing methods capable of overcoming current limitations is crucial to reduce the generalization error of state-of-the-art algorithms. This paper presents the application of a self-organised growing cell structures network in an attempt to cluster biological homogeneous patients. This technique makes use of a previous successful supervised fuzzy pattern algorithm capable of performing DNA microarray data reduction. The proposed model has been tested with microarray data belonging to bone marrow samples from 43 adult patients with cancer plus a group of six cases corresponding to healthy persons. The results of this work demonstrate that classical artificial intelligence techniques can be effectively used for tumour diagnosis working with high-dimensional microarray data

gene-CBR: a case-based reasong tool for cancer diagnosis using microarray data sets

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. This paper presentsgene-CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case-based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty-three adult patients with cancer plus a group of six cases corresponding to healthy persons

Using Fuzzy Patterns for Gene Selection and Data Reduction on Microarray Data

The advent of DNA microarray technology has supplied a large volume of data to many fields like machine learning and data mining. Intelligent support is essential for managing and interpreting this great amount of information. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. In this paper we demonstrate how a supervised fuzzy pattern algorithm can be used to perform DNA microarray data reduction over real data. The benefits of our method can be employed to find biologically significant insights relating to meaningful genes in order to improve previous successful techniques. Experimental results on acute myeloid leukemia diagnosis show the effectiveness of the proposed approach

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM)clusteringwere also used to developmodels for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although themajority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the othermodels. The ANFISmodels have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season

Development and Evolution of the Mammalian Cerebellum at Single Cell Resolution

Originally thought to only take part in motor control, the cerebellum emerged over the last decades as an important organ in various higher cognitive functions, such as learning and speech. Besides this, the cerebellum is associated to various diseases, such as spinocerebellar ataxia, autism spectrum disorder, and medulloblastoma. The basic structure and connective properties of it are well understood, but single-cell-technologies made it possible to study the cerebellum at higher resolution. Many questions about molecular details of its development and evolution are still not answered. Cerebella are present in all jawed vertebrates, though structural diversity is macroscopic and microscopic detectable, such as the number of deep nuclei, the presence of the vermis, or the mode of production of one of the most important cell types in the cerebellum - granule cells. Using single-nucleus RNA-sequencing (snRNA-seq) and bioinformatic approaches, I studied cerebellum data of human, mouse (Mus musculus) and opossum (Monodelphis domestica). The dataset contained samples spanning the organs development at high temporal resolution. It was possible to track the differentiation of the major cerebellar neuronal and glial cell types, as well as identify states and subtypes. This generated a comprehensive map of cellular complexity through eutherian (human and mouse) and marsupial (opossum) development. Leveraging the evolutionary distance of approximately 160 million years between the eutherian and marsupial lineage, conserved and diverged cell type marker genes could be identified which might be promising candidates for understanding the basic blueprint of cerebellar cell type identity. Stage correspondence mapping aligned the vastly different developmental time frames of the three studied species and allowed the identification of a two-fold increase in Purkinje cell progenitors in the human lineage, which might be connected to a recently identified human-specific secondary ventricular zone progenitor pool. It was possible to model the differentiation path of granule and Purkinje cells from early progenitors to mature neurons. Conserved and diverged gene expression trajectories were discovered. Using in vitro and in vivo intollerance scores, I could show that genes which are dynamically expressed during differentiation show higher functional constraint as non-dynamic genes, fitting to previous bulk-RNA-seq studies, showing similar results across the development of the full organ. Some orthologs with diverging patterns were disease-associated genes, which could have implications on clinical research on conditions like autism spectrum disorders and medulloblastoma. Furthermore, fundamental changes of gene expressions, established as gain or loss of expression within a cell type and species, were detected. Affected genes showed decreased functional constraint, verifying evolutionary principles on single cell scale. Taken together, this study shows the strength of state of the art methodology combined with high resolution developmental sampling in an evolution biological context to discover fundamental principles of organ development at single-cell scale

Case-based reasoning as a decision support system for cancer diagnosis: A case study

Microarray technology can measure the expression levels of thousands of genes in an experiment. This fact makes the use of computational methods in cancer research absolutely essential. One of the possible applications is in the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays, but there is a growing need for new and effective solutions. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow filtering, classification, and knowledge extraction. The system has been tested and the results obtained are presented in this paper

Computational Intelligence Techniques for Classification in Microarray Analysis

During the last few years there has been a growing need for using computational intelligence techniques to analyze microarray data. The aim of the system presented in this study is to provide innovative decision support techniques for classifying data from microarrays and for extracting knowledge about the classification process. The computational intelligence techniques used in this chapter follow the case-based reasoning paradigm to emulate the steps followed in expression analysis. This work presents a novel filtering technique based on statistical methods, a new clustering technique that uses ESOINN (Enhanced Self-Organizing Incremental Neuronal Network), and a knowledge extraction technique based on the RIPPER algorithm. The system presented within this chapter has been applied to classify CLL patients and extract knowledge about the classification process. The results obtained permit us to conclude that the system provides a notable reduction of the dimensionality of the data obtained from microarrays. Moreover, the classification process takes the detection of relevant and irrelevant probes into account, which is fundamental for subsequent classification and an extraction of knowledge tool with a graphical interface to explain the classification process, and has been much appreciated by the human experts. Finally, the philosophy of the CBR systems facilitates the resolution of new problems using past experiences, which is very appropriate regarding the classification of leukemia