Biomedical subjects

Stefan Kramer

Publications and source records attributed to Stefan Kramer.

9 recordsLinked to original sources

PubMed2006

Mitochondria are not required for death receptor-mediated cytosolic acidification during apoptosis.

In addition to cell shrinkage, membrane blebbing, DNA fragmentation and phosphatidylserine exposure, intracellular acidification represents a hallmark of apoptosis. Although the mechanisms underlying cytosolic acidification during apoptosis remained largely elusive, a pivotal role of mitochondria has been proposed. In order to investigate the involvement of mitochondria in cytosolic acidification during apoptosis, we blocked the mitochondrial death pathway by overexpression of Bcl-2 and subsequently activated the death receptor pathway by anti-CD95 or TRAIL or the mitochondrial pathway by staurosporine. We show that Bcl-2 but not caspase inhibition prevented staurosporine-induced intracellular acidification. Thus, intracellular acidification in mitochondrial apoptosis is a Bcl-2-inhibitable, but caspase-independent process. In contrast, Bcl-2 only slightly delayed, but did not prevent intracellular acidification upon triggering of death receptors. The Na(+)/H(+) exchanger NHE1 was partially degraded during apoptosis but only to a small extent and and at a delayed time point when cytosolic acidification was almost completed. We therefore conclude that cytosolic acidification is mitochondrially controlled in response to mitochondria-dependent death stimuli, but requires additional caspase-dependent mechanisms during death receptor-mediated apoptosis.

Amino Acid Chloromethyl Ketones↗

PubMed2006

A new representation for protein secondary structure prediction based on frequent patterns.

MOTIVATION: A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using support vector machines (SVMs). RESULTS: Three different sets of features based on frequent patterns are evaluated in a blind testing setup using 150 targets from the EVA contest and compared to predictions of PSI-PRED, PHD and PROFsec. Despite being trained on only 940 proteins, a simple SVM classifier based on this new representation yields results comparable to PSI-PRED and PROFsec. Finally, we show that the method contributes significant information to consensus predictions. AVAILABILITY: The method is available from the authors upon request.

Algorithms↗

PubMed2006

Learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data: does gene expression make a difference?

We address the problem of learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data. Extending the classical Quantitative Structure Activity Relationship paradigm, we investigate whether including gene expression data leads to a statistically significant improvement of prediction quality. Our analysis shows that the straightforward approach of including individual gene expression as features does not necessarily improve, but on the contrary, may degrade performance significantly. When gene expression information is aggregated, for instance by features representing the correlation with reference cell lines, performance can be improved significantly. Further improvements may be expected if the learning task is structured by grouping features and instances.

Cell Line, Tumor↗

PubMed2005

Analyzing microarray data using quantitative association rules.

MOTIVATION: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. RESULTS: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data. AVAILABILITY: The code is available from the authors on request.

Algorithms↗

PubMed2005

Protein microarrays: catching the proteome.

After the completion of the human genome sequencing project, DNA microarrays and sophisticated bioinformatics platforms give scientists a global view of biological systems. In today's proteome era, efforts are undertaken to adapt microarray technology in order to analyse the expression of a large number of proteins simultaneously and screen entire genomes for proteins that interact with particular factors, catalyse particular reactions, act as substrates for protein-modifying enzymes and/or as targets of autoimmune responses. In this review, we will summarise the current stage of protein microarray technology. We will focus on the latest fields of application for the simultaneous determination of a variety of parameters from a minute amount of sample. Future challenges of this cutting-edge technology will be discussed.

Animals↗

PubMed2003

Statistical evaluation of the Predictive Toxicology Challenge 2000-2001.

MOTIVATION: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. RESULTS: Fourteen machine learning groups generated 111 models. The use of Receiver Operating Characteristic (ROC) space allowed the models to be uniformly compared regardless of the error cost function. We developed a statistical method to test if a model performs significantly better than random in ROC space. Using this test as criteria five models performed better than random guessing at a significance level p of 0.05 (not corrected for multiple testing). Statistically the best predictor was the Viniti model for female mice, with p value below 0.002. The toxicologically most interesting models were Leuven2 for male mice, and Kwansei for female rats. These models performed well in the statistical analysis and they are in the middle of ROC space, i.e. distant from extreme cost assumptions. These predictive models were also independently judged by domain experts to be among the three most interesting, and are believed to include a small but significant amount of empirically learned toxicological knowledge. AVAILABILITY: PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.

Algorithms↗

PubMed2003

Protein microarrays: promising tools for proteomic research.

Miniaturized and parallelized ligand binding assays are of great interest in postgenomic research because microarray technology allows the simultaneous determination of a large number of parameters from a minute amount of sample within a single experiment. Assay systems based on this technology are used for the identification and quantification of proteins as well as for the study of protein interactions. Protein affinity assays have been implemented that allow the analysis of interactions between proteins with other proteins, peptides, low molecular weight compounds, oligosaccharides or DNA. Microarray technology is an emerging technology used in global analytical approaches and has a considerable impact on proteomic research.

Animals↗

PubMedDate not supplied

Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning.

We present a case study of machine learning and data mining in intensive care medicine. In the study, we compared different methods of measuring pressure-volume curves in artificially ventilated patients suffering from the adult respiratory distress syndrome (ARDS). Our aim was to show that inductive machine learning can be used to gain insights into differences and similarities among these methods. We defined two tasks: the first one was to recognize the measurement method producing a given pressure-volume curve. This was defined as the task of classifying pressure-volume curves (the classes being the measurement methods). The second was to model the curves themselves, that is, to predict the volume given the pressure, the measurement method and the patient data. Clearly, this can be defined as a regression task. For these two tasks, we applied C5.0 and CUBIST, two inductive machine learning tools, respectively. Apart from medical findings regarding the characteristics of the measurement methods, we found some evidence showing the value of an abstract representation for classifying curves: normalization and high-level descriptors from curve fitting played a crucial role in obtaining reasonably accurate models. Another useful feature of algorithms for inductive machine learning is the possibility of incorporating background knowledge. In our study, the incorporation of patient data helped to improve regression results dramatically, which might open the door for the individual respiratory treatment of patients in the future.

Adult↗

PubMedDate not supplied

Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds.

This paper explores the utility of data mining and machine learning algorithms for the induction of mutagenicity structure-activity relationships (SARs) from noncongeneric data sets. We compare (i) a newly developed algorithm (MOLFEA) for the generation of descriptors (molecular fragments) for noncongeneric compounds with traditional SAR approaches (molecular properties) and (ii) different machine learning algorithms for the induction of SARs from these descriptors. In addition we investigate the optimal parameter settings for these programs and give an exemplary interpretation of the derived models. The predictive accuracies of models using MOLFEA derived descriptors is approximately 10-15%age points higher than those using molecular properties alone. Using both types of descriptors together does not improve the derived models. From the applied machine learning techniques the rule learner PART and support vector machines gave the best results, although the differences between the learning algorithms are only marginal. We were able to achieve predictive accuracies up to 78% for 10-fold cross-validation. The resulting models are relatively easy to interpret and usable for predictive as well as for explanatory purposes.

Algorithms↗