abstract: The basal ganglia are four sub-cortical nuclei associated with motor control and reward learning. They are part of numerous larger mostly segregated loops where the basal ganglia receive inputs from specific regions of cortex. Converging on these inputs are dopaminergic neurons that alter their firing based on received and/or predicted rewarding outcomes of a behavior. The basal ganglia's output feeds through the thalamus back to the areas of the cortex where the loop originated. Understanding the dynamic interactions between the various parts of these loops is critical to understanding the basal ganglia's role in motor control and reward based learning. This work developed several experimental techniques that can be applied to further study basal ganglia function. The first technique used micro-volume injections of low concentration muscimol to decrease the firing rates of recorded neurons in a limited area of cortex in rats. Afterwards, an artificial cerebrospinal fluid flush was injected to rapidly eliminate the muscimol's effects. This technique was able to contain the effects of muscimol to approximately a 1 mm radius volume and limited the duration of the drug effect to less than one hour. This technique could be used to temporarily perturb a small portion of the loops involving the basal ganglia and then observe how these effects propagate in other connected regions. The second part applied self-organizing maps (SOM) to find temporal patterns in neural firing rate that are independent of behavior. The distribution of detected patterns frequency on these maps can then be used to determine if changes in neural activity are occurring over time. The final technique focused on the role of the basal ganglia in reward learning. A new conditioning technique was created to increase the occurrence of selected patterns of neural activity without utilizing any external reward or behavior. A pattern of neural activity in the cortex of rats was selected using an SOM. The pattern was then reinforced by being paired with electrical stimulation of the medial forebrain bundle triggering dopamine release in the basal ganglia. Ultimately, this technique proved unsuccessful possibly due to poor selection of the patterns being reinforced. / Dissertation/Thesis / Ph.D. Bioengineering 2014

Orientadores: Carlos Roberto de Souza Filho, Michael James Friedel / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-18T08:01:00Z (GMT). No. of bitstreams: 1 Iwashita_Fabio_D.pdf: 9120430 bytes, checksum: ed99c3753275c598eb75911e17be7646 (MD5) Previous issue date: 2011 / Resumo: Esta tese de doutorado tem como objetivo aprofundar o conhecimento sobre as relações das propriedades físico-quimicas do solo com a morfometria do relevo, buscando quantificar essas relações para a construção de modelos conceituais e preditivos. Mapas auto-organizáveis e modelos de sistemas de informação geográfica foram utilizados para investigar as relações não lineares associadas ao intemperismo químico e físico, fatores associados a fenômenos hidrológicos e à evolução dos solos. Três estudos de caso são apresentados: o intemperismo químico de solo no estado do Paraná (22 variáveis e 304 amostras), o transporte físico de sedimentos em Poços de Caldas (9 variáveis e 29 amostras), e hidroquímica de aqüíferos na Formação Serra Geral no Estado do Paraná (27 variáveis e 976 amostras). O método combinando simulação estocástica e mineração de dados permitiu explorar as relações entre relevo, granulometria e geoquímica dos solos. Regiões mais elevadas e com morfometria convexa apresentaram alta denudação de elementos móveis (e.g., Ca) e baixa de elementos pouco móveis (e.g., Al). O mesmo padrão foi observado para granulometria de solos, ou seja, alta proporção de areia em áreas altas e convexas da bacia e altos teores de argila, com baixa condutividade hidráulica, em regiões convexas próximas aos canais de drenagem. O comportamento espacial da hidroquímica das águas do aqüífero Serra Geral apontou áreas de potencial conectividade entre aqüíferos, áreas de recarga recente e de alto tempo de residência. Foram construídos modelos preditivos não tendenciosos das propriedades do solo em subsuperfície partindo da premissa de que o intemperismo e a morfometria se relacionam através de um processo duplamente dependente, onde a denudação física e química atua no delineamento do relevo e a morfometria do terreno é um fator que caracteriza as condições físico-químicas do solo / Abstract: This Doctoral thesis aims to explore the relationship between soil physical-chemical properties and relief morphometry, and quantifying these relationships to build conceptual and predictive models. Self-organizing maps and Geographic Information Systems modeling are here used to investigate nonlinear correlations associated with chemical and physical denudation; which are factors connected with hydrological phenomena and soil evolution. Three study cases are presented: soil chemical weathering within the limits of the Parana State, southern Brazil (22 variables and 304 samples), physical transport of sediments in the alkaline intrusive complex of Poços de Caldas, southeastern Brazil (9 variables and 29 samples), and hydrochemistry of Serra Geral aquifers also in the Parana State (27 variables and 976 samples). The method combining stochastic simulation and data mining allows exploring the relationships between topography, soil texture and soil geochemistry. In the Parana State, higher regions and areas with convex morphometry shows, respectively, higher and lower denudation rates of mobile (e.g., Ca) and less mobile (e.g., Al) elements. The same pattern is observed for soil particle size. In this case, high proportion of sand is found in highlands and convex areas inside the basin, and high clay content, with low hydraulic conductivity, occurs in convex regions, near drainage channels. The spatial behavior of the Serra Geral aquifer?s hydrochemistry pointed out to areas with potential connectivity with the Guarani aquifer system, recent recharge areas, and long-standing waters. Predictive, unbiased models are built for soil properties on the premise that weathering and morphology are related through a two-way dependent process, where the physical and chemical denudation delineates the elevations of the land surface, and terrain morphometry is a factor that characterizes the physical-chemical conditions of the soil / Doutorado / Geologia e Recursos Naturais / Doutor em Ciências

Orientadores: Alexandre Campane Vidal, Emilson Pereira Leite / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-19T23:10:44Z (GMT). No. of bitstreams: 1 Kuroda_MichelleChaves_M.pdf: 6122249 bytes, checksum: a01bbebfadb01e55531df051be7c9e44 (MD5) Previous issue date: 2012 / Resumo: A caracterização de reservatórios é um trabalho complexo, que envolve muitas variáveis com informações em diferentes escalas. Para minimizar incertezas, este trabalho propõe a utilização de redes neurais artificiais, algoritmo computacional inspirado no funcionamento cerebral que mapeia, agrupa e prevê informações a partir de reconhecimento de padrões supervisionados ou não. Neste trabalho foram aplicados dois métodos: Mapas Auto-Organizáveis e Backpropagation. O objetivo da aplicação da ferramenta é o melhor entendimento dos reservatórios estudados, a partir da identificação litológica e previsão de características petrofísicas em dados de poços e a melhoria de visualização sísmica realizada a partir do estudo de multiatributos sísmicos. Através dos resultados é possível delimitar a geometria dos reservatórios possibilitando ajustes e tomadas de decisões que aperfeiçoam o processo de exploração. Com este propósito foram analisados duas áreas de estudo: a bacia de Taubaté localizada na porção leste do estado de São Paulo, que ocupa uma área aproximada de 2400 km², estendendo-se ao longo do Vale do Rio Paraíba, desde Jacareí até a cidade de Cruzeiro; e o campo de Namorado, na bacia de Campos, localizado na parte central norte na zona de acumulação de hidrocarbonetos da Bacia de Campos, a 80 km da costa. Na bacia de Taubaté foram realizados estudos de caracterização de eletrofácies em dados de perfis de poços, posteriormente utilizados no ajuste da análise sísmica das linhas 2D da mesma bacia. No campo de Namorado foram estudadas as fácies reservatório e possíveis reservatórios para identificação e classificação de qualidade, além da predição de permeabilidade nos intervalos de reservatório / Abstract: The reservoir characterization is complex, involving many variables with information on different scales. To minimize uncertainties, this paper proposes the use of artificial neural networks, computational algorithm inspired on the brain function, which maps, groups, and provides information based on supervised pattern recognition or not. In this work we applied two methods: Self-Organizing Maps, and Backpropagation. The purpose of the application of the tool is a better understanding of the reservoirs, by identifying lithologic and predicting of petrophysical characteristics on data from well logs and improving seismic preview done from the multi-attribute seismic study. Through the results it is possible to define the boundaries of reservoirs, allowing adjustments and making decisions that enhance the exploration process. For this purpose we analyzed two study areas: the Taubaté basin located in the southeastern portion of the São Paulo state, encompasses an area of approximately 2400 km², stretching along the Paraíba River Valley, from Jacareí to the Cruzeiro city; and Namorado field in the Campos Basin, located in the central zone of hydrocarbon accumulation in the Campos Basin, 80 km from the coast. In Taubaté basin the studies have been performed on well log characterization, they were used to adjust the seismic analysis of 2D lines in the same basin, through multi-attributes analysis. In Namorado field the facies of reservoir and possible reservoir were studied for identify e classify the rock types, besides to predict the permeability in reservoir intervals / Mestrado / Geologia e Recursos Naturais / Mestre em Geociências

Denna studie är ett kognitionsvetenskapligt examensarbete som syftar på att skapa en modell som skapar semantiska representationer utifrån ett mer biologiskt plausibelt tillvägagångssätt jämfört med traditionella metoder. Denna modell kan ses som ett första steg i utredningen av ansatsen som följer. Studien utreder antagandet om Self Organizing Maps kan användas för att skapa semantiska representationer ur stora mängder text utifrån ett distribuerat inspirerat tillvägagångssätt. Resultatet visar på ett potentiellt fungerande system, men som behöver utredas vidare i framtida studier för verifiering av högre grad.

The Time Dependent Kernel Density Estimation (TDKDE) developed by Harvey & Oryshchenko (2012) is a kernel density estimation adjusted by the Exponentially Weighted Moving Average (EWMA) weighting scheme. The Maximum Likelihood Estimation (MLE) procedure for estimating the parameters proposed by Harvey & Oryshchenko (2012) is easy to apply but has two inherent problems. In this study, we evaluate the performances of the probability density estimation in terms of the uniformity of Probability Integral Transforms (PITs) on various kernel functions combined with different preset numbers. Furthermore, we develop a new estimation algorithm which can be conducted using Artificial Neural Networks to eliminate the inherent problems with the MLE method and to improve the estimation performance as well. Based on the new estimation algorithm, we develop the TDKDE-based Random Forests time series classification algorithm which is significantly superior to the commonly used statistical feature-based Random Forests method as well as the Ker- nel Density Estimation (KDE)-based Random Forests approach. Furthermore, the proposed TDKDE-based Self-organizing Map (SOM) clustering algorithm is demonstrated to be superior to the widely used Discrete-Wavelet- Transform (DWT)-based SOM method in terms of the Adjusted Rand Index (ARI).

No description available.

Condition monitoring of systems and detection of changes in the systems are of significant importance for an automated system, whether it is for production, transport, amusement, or any other application. Although condition monitoring is already widely used in machinery, the need for it is growing, especially as systems become increasingly autonomous and self-contained. One of the toughest tasks concerning embedded condition monitoring is to extract the useful information and conclusions from the often large amount of measured data. The use of self-organising maps, SOMs, for embedded condition monitoring is of interest for the component manufacturer who lacks information about how the component is to be used by the system integrator, or in what applications and load cases. At the same time, there is also a potential interest on the part of the system builders. Although they know how the system is designed and will be used, it is still hard to identify all possible failure modes. A component does not break at all locations or in all functions simultaneously, but rather in one, more stressed, location. Where is this location? Here, the collection of as much data as possible from the system and then processing it with the aid of SOMs allows the system integrators to create a map of the load on the system in its operating conditions. This gives the system integrators a better chance to decide where to improve the system. Automating monitoring and analysis means not only being able to collect prodigious amounts of measured data, but also being able to interpret the data and transform it into useful information, e.g. conclusions about the state of the system. However, as will be argued in this thesis, drawing the conclusions is one thing, being able to interpret the conclusions is another, not least concerning the credibility of the conclusions drawn. This has proven to be particularly true for simple mechanical systems like pneumatics in the manufacturing industry.

The meridional temperature gradient between middle and high latitudes is decreasing due to Arctic amplification, which enhances the warming in the Arctic region. This change in temperature is also influencing the circulation and the horizontal energy fluxes between the mid latitudes and the Arctic, which itself might influence the Arctic additionally. The horizontal energy flux, to our best knowledge, has never been analyzed using the up-to-date method called self-organizing map (SOM). The SOM is a simple unsupervised neural network that is used to extract patterns of high-dimensional data and presents the patterns in a two dimensional lattice, where similar (more different) patterns are closer together (farther apart) within the lattice. An advantage of using the SOM is that there are no underlying linear assumptions like in other methods that characterize the circulation, such as the Arctic Oscillation or the North Atlantic Oscillation index. The SOM has been used in this work to extract and analyze horizontal heat flux patterns from reanalysis data and climate model data. Using the SOM method, it was possible to find distinct horizontal heat flux patterns into the Arctic, that have been combined into heat flux pathways. The SOM made it possible to characterize the pathways' change in occurrence frequency throughout the last thirty years and the change between present-day climate model simulations and climate projections with increased greenhouse gas concentrations. Using reanalysis data, three distinct patterns have been extracted, which all show different features. They are named according to the main pathway the horizontal heat flux takes to reach the Arctic: the Atlantic pathway, the Pacific pathway, and the continental pathway. For the reanalysis data, it is shown that the Atlantic pathway, which is connected with positive temperature anomalies in the central Arctic, has become more frequent during the last three decades, while the Pacific pathway, that is connected to negative temperature anomalies around Svalbard, has become less frequent. This suggests that the circulation, which is connected to the temperature in the Arctic, is changing. The trends for the occurrence frequencies of the SOM horizontal heat flux pathways have, to our best knowledge, never been analyzed prior to this work. With respect to climate model results, the three distinct patterns were also identified in climate simulations of the second half of the twentieth century and climate projections of the second half of the twenty-first century from eight models. This demonstrates that these three pathways are an inherent part of the atmosphere. In comparison with the reanalysis data, the climate models show much stronger occurrence frequencies for the continental pathway. The reanalysis data of the continental pathway does not show such high occurrence frequencies. However, the multi model mean shows a clear decrease in these occurrence frequencies of the continental pathway between the present-day climate simulation and the climate projection with increased greenhouse gas concentrations. The continental pathway is mostly connected to strong zonal fluxes while there are only small meridional transports over Siberia or North America. This suggests that the fluxes become more meridional with an enhanced warming and thus increase the heat flux into the Arctic, which might influence the surface air temperature.:Bibliographische Beschreibung Bibliographic Description Acronyms 1. Introduction: Arctic Amplification, Circulation and Transport 1.1. Arctic Amplification 1.2. The (AC)3 project 1.3. Overview of General Circulation in Mid and High Latitudes 1.3.1. Drivers of the general circulation 1.3.2. Circulation impacts on high and mid latitudes 1.3.3. Atmospheric energy transport into the Arctic 1.4. Overview of the Thesis 2. The Self-organizing Map 2.1. Mathematical Description 2.2. SOM Parameters and their Effect on Clustering Meteorological Data 2.2.1. Map size 2.2.2. Neighborhood function 2.2.3. Iterations 2.2.4. Learning rate 2.2.5. Summary of the effect of learning parameters 2.3. Limits of SOM 2.4. Application of SOM in Atmospheric Sciences 2.5. Comparison with the K-Means Clustering Algorithm 2.6. A Practical Guide to SOM 3. Clustering of Atmospheric Energy Transport within ERA-Interim 3.1. Data and Method 3.1.1. ERA-Interim data 3.1.2. Analysis method 3.2. Results 3.2.1. Heat transport SOM 3.2.2. Temperature anomaly composites related to transport pathways 3.2.3. Mean meridional heat transport 3.2.4. Trend of transport pathways 3.2.5. Two-meter temperature trends 3.3. Discussion 3.4. Summary of ERA-Interim Analysis 4. Comparison of Flux Pathways in CMIP5 Model Analysis 4.1. Methods and Data 4.1.1. CMIP5 model data 4.1.2. Analysis using the SOM method 4.2. Results 4.2.1. Historical patterns 4.2.2. RCP8.5 patterns 4.2.3. Mean pathway occurrence frequencies 4.2.4. Pathway occurrence frequency trends during the historical and future time intervals 4.3. Discussion of CMIP5 Analysis 5. Summary and Conclusion of the Horizontal Energy Flux SOM Analysis References A. Appendix: ERA-Interim Self-Organizing Map Analysis B. Appendix: CMIP5 Self-Organizing Map Results Acknowledgments Curriculum Vitae Affirmation / Der meridionale Temperaturgradient zwischen mittleren und hohen Breiten nimmt aufgrund der arktischen Verstärkung ab. Diese Temperaturänderung beeinflusst auch die Zirkulation und die horizontalen Energieflüsse zwischen den mittleren Breiten und der Arktis, was die Arktis selbst zusätzlich beeinflussen könnte. Der horizontale Energietransport wurde, unserem bestem Wissen nach, nie mit der aktuellen Methode namens Self-Organizing Map (SOM) analysiert. Die SOM ist ein einfaches unüberwachtes neuronales Netzwerk, das zum Extrahieren von Mustern hoch dimensionaler Daten verwendet wird und die Muster in einem zweidimensionalen Gitter darstellt, in dem ähnliche (unterschiedliche) Muster innerhalb des Gitters näher beieinander (weiter voneinander entfernt) liegen. Ein Vorteil der SOM besteht darin, dass keine linearen Annahmen wie bei anderen Methoden vorliegen, die die Zirkulation charakterisieren, wie z. B. die Arktische Oszillation oder der Nordatlantische Oszillationsindex. Die SOM wurde im Rahmen dieser Arbeit verwendet, um horizontale Wärmetransportmuster aus Reanalysedaten und Klimamodelldaten zu extrahieren und zu analysieren. Mit der SOM-Methode konnten unterschiedliche horizontale Muster des Wärmetransports in die Arktis identifiziert werden, welche wiederum zu Pfaden zusammengefasst wurden. Die SOM ermöglichte es, die Veränderung der Auftrittshäufigkeit der Pfade in den letzten dreißig Jahren und die Veränderung der Muster zwischen einer Simulation des heutigen Zustandes und einer Klimaprojektion mit erhöhten Treibhausgaskonzentrationen zu charakterisieren. Unter Verwendung von Reanalysedaten konnten drei unterschiedliche Pfade extrahiert werden, die alle unterschiedliche Merkmale aufweisen. Sie wurden nach dem jeweiligen Hauptpfad benannt, den der horizontale Wärmetransport vollzieht, um in die Arktis zu gelangen: der Atlantikpfad, der Pazifikpfad und der Kontinentalpfad. Für die Reanalysedaten konnte gezeigt werden, dass die Auftretenshäufigkeit des Atlantikpfads, der mit positiven Temperaturanomalien in der Zentralarktis verbunden ist, in den letzten drei Jahrzehnten gestiegen ist. Demgegenüber ist die Auftretenshäufigkeit des pazifischen Pfads, der mit negativen Temperaturanomalien um Spitzbergen verbunden ist, in den letzten drei Jahrzehnten gesunken. Dies deutet darauf hin, dass sich die Zirkulation, die mit der Temperatur in der Arktis verbunden ist, ändert. Die Trends für die Auftrittshäufigkeiten der horizontalen SOM-Wärmetransportpfade wurden, nach bestem Wissen, vor dieser Arbeit noch nie analysiert. Auswertungen basierend auf acht Klimamodellen haben die drei unterschiedlichen Muster sowohl in Klimasimulationen für die zweite Hälfte des zwanzigsten Jahrhunderts, als auch in Klimaprojektionen der zweiten Hälfte des einundzwanzigsten Jahrhunderts gefunden. Dies zeigt, dass diese drei Pfade der Atmosphäre inhärent sind. Im Vergleich zu den Reanalysedaten zeigen die Klimamodelle viel stärkere Auftrittshäufigkeiten für den Kontinentalpfad. Die Reanalysedaten des Kontinentalpfads weisen keine hohen Auftrittshäufigkeiten auf. Der Multi-Modell-Mittelwert zeigt jedoch eine deutliche Abnahme dieser Auftrittshäufigkeiten des Kontinentalpfads zwischen der Simulation des heutigen Zustands und der Projektion mit erhöhten Treibhausgaskonzentrationen. Der Kontinentalpfad ist meist mit starken zonalen Transporten verbunden, während nur kleine meridionale Transporte über Sibirien oder Nordamerika erfolgen. Dies deutet darauf hin, dass mit zunehmender Erwärmung die Flüsse meridionaler werden sowie den Wärmetransport in die Arktis erhöhen und somit die Lufttemperatur in Bodennähe beeinflussen können.:Bibliographische Beschreibung Bibliographic Description Acronyms 1. Introduction: Arctic Amplification, Circulation and Transport 1.1. Arctic Amplification 1.2. The (AC)3 project 1.3. Overview of General Circulation in Mid and High Latitudes 1.3.1. Drivers of the general circulation 1.3.2. Circulation impacts on high and mid latitudes 1.3.3. Atmospheric energy transport into the Arctic 1.4. Overview of the Thesis 2. The Self-organizing Map 2.1. Mathematical Description 2.2. SOM Parameters and their Effect on Clustering Meteorological Data 2.2.1. Map size 2.2.2. Neighborhood function 2.2.3. Iterations 2.2.4. Learning rate 2.2.5. Summary of the effect of learning parameters 2.3. Limits of SOM 2.4. Application of SOM in Atmospheric Sciences 2.5. Comparison with the K-Means Clustering Algorithm 2.6. A Practical Guide to SOM 3. Clustering of Atmospheric Energy Transport within ERA-Interim 3.1. Data and Method 3.1.1. ERA-Interim data 3.1.2. Analysis method 3.2. Results 3.2.1. Heat transport SOM 3.2.2. Temperature anomaly composites related to transport pathways 3.2.3. Mean meridional heat transport 3.2.4. Trend of transport pathways 3.2.5. Two-meter temperature trends 3.3. Discussion 3.4. Summary of ERA-Interim Analysis 4. Comparison of Flux Pathways in CMIP5 Model Analysis 4.1. Methods and Data 4.1.1. CMIP5 model data 4.1.2. Analysis using the SOM method 4.2. Results 4.2.1. Historical patterns 4.2.2. RCP8.5 patterns 4.2.3. Mean pathway occurrence frequencies 4.2.4. Pathway occurrence frequency trends during the historical and future time intervals 4.3. Discussion of CMIP5 Analysis 5. Summary and Conclusion of the Horizontal Energy Flux SOM Analysis References A. Appendix: ERA-Interim Self-Organizing Map Analysis B. Appendix: CMIP5 Self-Organizing Map Results Acknowledgments Curriculum Vitae Affirmation

Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectrometry provide huge amounts of data per measurement and challenge traditional analyses. New strategies of data processing, visualization and functional analysis are inevitable. This thesis presents an approach which applies a machine learning technique known as self organizing maps (SOMs). SOMs enable the parallel sample- and feature-centered view of molecular phenotypes combined with strong visualization and second-level analysis capabilities. We developed a comprehensive analysis and visualization pipeline based on SOMs. The unsupervised SOM mapping projects the initially high number of features, such as gene expression profiles, to meta-feature clusters of similar and hence potentially co-regulated single features. This reduction of dimension is attained by the re-weighting of primary information and does not entail a loss of primary information in contrast to simple filtering approaches. The meta-data provided by the SOM algorithm is visualized in terms of intuitive mosaic portraits. Sample-specific and common properties shared between samples emerge as a handful of localized spots in the portraits collecting groups of co-regulated and co-expressed meta-features. This characteristic color patterns reflect the data landscape of each sample and promote immediate identification of (meta-)features of interest. It will be demonstrated that SOM portraits transform large and heterogeneous sets of molecular biological data into an atlas of sample-specific texture maps which can be directly compared in terms of similarities and dissimilarities. Spot-clusters of correlated meta-features can be extracted from the SOM portraits in a subsequent step of aggregation. This spot-clustering effectively enables reduction of the dimensionality of the data in two subsequent steps towards a handful of signature modules in an unsupervised fashion. Furthermore we demonstrate that analysis techniques provide enhanced resolution if applied to the meta-features. The improved discrimination power of meta-features in downstream analyses such as hierarchical clustering, independent component analysis or pairwise correlation analysis is ascribed to essentially two facts: Firstly, the set of meta-features better represents the diversity of patterns and modes inherent in the data and secondly, it also possesses the better signal-to-noise characteristics as a comparable collection of single features. Additionally to the pattern-driven feature selection in the SOM portraits, we apply statistical measures to detect significantly differential features between sample classes. Implementation of scoring measurements supplements the basal SOM algorithm. Further, two variants of functional enrichment analyses are introduced which link sample specific patterns of the meta-feature landscape with biological knowledge and support functional interpretation of the data based on the ‘guilt by association’ principle. Finally, case studies selected from different ‘OMIC’ realms are presented in this thesis. In particular, molecular phenotype data derived from expression microarrays (mRNA, miRNA), sequencing (DNA methylation, histone modification patterns) or mass spectrometry (proteome), and also genotype data (SNP-microarrays) is analyzed. It is shown that the SOM analysis pipeline implies strong application capabilities and covers a broad range of potential purposes ranging from time series and treatment-vs.-control experiments to discrimination of samples according to genotypic, phenotypic or taxonomic classifications.

No description available.