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21 Publications visible to you, out of a total of 21
Personalized disease phenotypes from massive omics data.
LHA - Leipzig Health Atlas

Abstract (Expand)

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual … on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such "big data." Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Authors: Hans Binder, Lydia Hopp, K. Lembcke, Henry Löffler-Wirth

Date Published: 2017

Publication Type: Not specified

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases.
LHA - Leipzig Health Atlas

Abstract (Expand)

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective … treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent.

Authors: A. Arakelyan, L. Nersisyan, M. Petrek, H. Loffler-Wirth, H. Binder

Date Published: 21st May 2016

Publication Type: Not specified

Human Diseases: lung disease

Analysis of Large-Scale OMIC Data Using Self Organizing Maps
LHA - Leipzig Health Atlas

Abstract (Expand)

The data produced by high-throughput bioanalytics is usually given as a feature matrix of dimension N x M (see Figure 1) where N is the number of features measured per sample and M is the number of … samples referring, e.g., to different treatments, time points or individuals. As a convention, each row of the matrix will be termed profile of the respective feature. The columns on the other hand will be termed states referring to each of the conditions studied. In general, the number of features can range from several thousands to millions, depending on the experimental screening technique used. Typically, this number largely exceeds the number of states studied, i.e. N>>M. SOM machine learning aims at reducing the number of relevant features by grouping the input data into clusters of appropriate size, and thus to transform the matrix of input data into a matrix of so-called meta-data with a reduced number of meta-features, K<<N (Figure 1a and b). In other words, SOM aims at mapping the space of the high-dimensional input data onto meta-data space of reduced dimensionality.

Authors: Hans Binder, Henry Löffler-Wirth

Date Published: 2015

Publication Type: Not specified

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.
LHA - Leipzig Health Atlas

Abstract (Expand)

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance … with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Authors: S. A. Munro, S. P. Lund, P. S. Pine, H. Binder, D. A. Clevert, A. Conesa, J. Dopazo, M. Fasold, S. Hochreiter, H. Hong, N. Jafari, D. P. Kreil, P. P. Labaj, S. Li, Y. Liao, S. M. Lin, J. Meehan, C. E. Mason, J. Santoyo-Lopez, R. A. Setterquist, L. Shi, W. Shi, G. K. Smyth, N. Stralis-Pavese, Z. Su, W. Tong, C. Wang, J. Wang, J. Xu, Z. Ye, Y. Yang, Y. Yu, M. Salit

Date Published: 25th Sep 2014

Publication Type: Not specified

A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche.
LHA - Leipzig Health Atlas

Abstract (Expand)

Despite progress in identifying the cellular composition of hematopoietic stem/progenitor cell (HSPC) niches, little is known about the molecular requirements of HSPC support. To address this issue, … we used a panel of six recognized HSPC-supportive stromal lines and less-supportive counterparts originating from embryonic and adult hematopoietic sites. Through comprehensive transcriptomic meta-analyses, we identified 481 mRNAs and 17 microRNAs organized in a modular network implicated in paracrine signaling. Further inclusion of 18 additional cell strains demonstrated that this mRNA subset was predictive of HSPC support. Our gene set contains most known HSPC regulators as well as a number of unexpected ones, such as Pax9 and Ccdc80, as validated by functional studies in zebrafish embryos. In sum, our approach has identified the core molecular network required for HSPC support. These cues, along with a searchable web resource, will inform ongoing efforts to instruct HSPC ex vivo amplification and formation from pluripotent precursors.

Authors: P. Charbord, C. Pouget, H. Binder, F. Dumont, G. Stik, P. Levy, F. Allain, C. Marchal, J. Richter, B. Uzan, F. Pflumio, F. Letourneur, H. Wirth, E. Dzierzak, D. Traver, T. Jaffredo, C. Durand

Date Published: 4th Sep 2014

Publication Type: Not specified

Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective
LHA - Leipzig Health Atlas

Abstract (Expand)

Genome-wide ‘omics'-assays provide a comprehensive view on the molecular landscapes of healthy and diseased cells. Bioinformatics traditionally pursues a ‘gene-centered' view by extracting lists … of genes differentially expressed or methylated between healthy and diseased states. Biological knowledge mining is then performed by applying gene set techniques using libraries of functional gene sets obtained from independent studies. This analysis strategy neglects two facts: (i) that different disease states can be characterized by a series of functional modules of co-regulated genes and (ii) that the topology of the underlying regulatory networks can induce complex expression patterns that require analysis methods beyond traditional genes set techniques. The authors here provide a knowledge discovery method that overcomes these shortcomings. It combines machine learning using self-organizing maps with pathway flow analysis. It extracts and visualizes regulatory modes from molecular omics data, maps them onto selected pathways and estimates the impact of pathway-activity changes. The authors illustrate the performance of the gene set and pathway signal flow methods using expression data of oncogenic pathway activation experiments and of patient data on glioma, B-cell lymphoma and colorectal cancer.

Authors: L. Nersisyan, Henry Löffler-Wirth, A. Arakelyan, Hans Binder

Date Published: 1st Jun 2014

Publication Type: Not specified

Analysis of microRNA expression using machine learning.
LHA - Leipzig Health Atlas

Abstract (Expand)

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter … we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

Authors: H. Wirth, M. V. Cakir, L. Hopp, H. Binder

Date Published: 26th Nov 2013

Publication Type: Not specified

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