Biomedical subjects

Tim W Nattkemper

Publications and source records attributed to Tim W Nattkemper.

11 recordsLinked to original sources

PubMed2006

GISMO--gene identification using a support vector machine for ORF classification.

We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license.

Algorithms↗

PubMed2006

Construction of a large signature-tagged mini-Tn5 transposon library and its application to mutagenesis of Sinorhizobium meliloti.

Sinorhizobium meliloti genome sequence determination has provided the basis for different approaches of functional genomics for this symbiotic nitrogen-fixing alpha-proteobacterium. One of these approaches is gene disruption with subsequent analysis of mutant phenotypes. This method is efficient for single genes; however, it is laborious and time-consuming if it is used on a large scale. Here, we used a signature-tagged transposon mutagenesis method that allowed analysis of the survival and competitiveness of many mutants in a single experiment. A novel set of signature tags characterized by similar melting temperatures and G+C contents of the tag sequences was developed. The efficiencies of amplification of all tags were expected to be similar. Thus, no preselection of the tags was necessary to create a library of 412 signature-tagged transposons. To achieve high specificity of tag detection, each transposon was bar coded by two signature tags. In order to generate defined, nonredundant sets of signature-tagged S. meliloti mutants for subsequent experiments, 12,000 mutants were constructed, and insertion sites for more than 5,000 mutants were determined. One set consisting of 378 mutants was used in a validation experiment to identify mutants showing altered growth patterns.

Base Sequence↗

PubMed2005

Libraries of synthetic stationary-phase and stress promoters as a tool for fine-tuning of expression of recombinant proteins in Escherichia coli.

Due to their induction characteristics stationary-phase promoters have a great potential in biotechnological processes for the production of heterologous proteins on a large-scale. In order to broaden the utility of stationary-phase promoters in bacterial expression systems and to create novel promoters induced by metabolic conditions, a library of synthetic stationary-phase/stress promoters for Escherichia coli was constructed. For designing the promoters the known -10 consensus sequence as well as the extended -10 region and an A/T-rich region downstream of the -10 region were kept constant, while sequences from -37 to -14 were partially or completely randomized. For detection and selection of stationary-phase promoters GFP with enhanced fluorescence was used. The expression pattern of the GFP reporter system was compared with that of the LacZ reporter system. To screen and characterize colonies containing stationary-phase/stress promoters a bioinformatic approach was developed. In total, 33 promoters were selected which cover a broad range of promoter activities and induction times indicating that the strength of promoters can be modulated by partially randomizing the sequence upstream of the -10 region. The induction ratio of synthetic promoters at the transition from exponential to stationary-phase was from 4 to over 6000 and the induction time relative to the entrance into stationary-phase from -1.4 to 2.7 h. Ninety-one percentage of the promoters had no or only low background activity during exponential growth. The broad variability of the promoters offers good possibilities for fine-tuning of gene expression and for applications in industrial bioprocesses.

Escherichia coli Proteins↗

PubMed2005

Tumor feature visualization with unsupervised learning.

Dynamic contrast enhanced magnetic resonance imaging (DCE MRI) is applied for diagnosis and therapy control of breast cancer. The malignancy of a lesion is expressed in the average signal kinetics of selected regions of interest (ROI) representing the lesion. The technique is reported to characterize malignant tumors with high sensitivity and highly variable specificity. Computer-based diagnosis (CAD) systems have been proposed to analyze and classify signal time curve data, extracted from hand selected ROI in the DCE MRI data. In this paper, we apply the self-organizing map (SOM) to a set of time curve feature vectors of single voxels from seven benign lesions and seven malignant tumors. Applying the SOM we are able to project the time curve values of each voxel on a two-dimensional map. The results show, that the SOM is able to visualize the hidden two-dimensional structure of the six-dimensional signal space. Using the trained SOM, we are able to identify voxels with benign or malignant signal characteristics and to visualize lesion cross-sections with pseudo-colors. A comparison with the established three time points method shows that the SOM has clear potential for deriving visualization parameters in DCE MRI analysis.

Breast Neoplasms↗

PubMed2005

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.

Algorithms↗

PubMed2005

Machine learning approaches for phenotype-genotype mapping: predicting heterozygous mutations in the CYP21B gene from steroid profiles.

OBJECTIVE: Non-linear relations between multiple biochemical parameters are the basis for the diagnosis of many diseases. Traditional linear analytical methods are not reliable predictors. Novel nonlinear techniques are increasingly used to improve the diagnostic accuracy of automated data interpretation. This has been exemplified in particular for the classification and diagnostic prediction of cancers based on expression profiling data. Our objective was to predict the genotype from complex biochemical data by comparing the performance of experienced clinicians to traditional linear analysis, and to novel non-linear analytical methods. DESIGN AND METHODS: As a model, we used a well-defined set of interconnected data consisting of unstimulated serum levels of steroid intermediates assessed in 54 subjects heterozygous for a mutation of the 21-hydroxylase gene (CYP21B) and in 43 healthy controls. RESULTS: The genetic alteration was predicted from the pattern of steroid levels with an accuracy of 39% by clinicians and of 64% by linear analysis. In contrast, non-linear analysis, such as self-organizing artificial neural networks, support vector machines, and nearest neighbour classifiers, allowed for higher accuracy up to 83%. CONCLUSIONS: The successful application of these non-linear adaptive methods to capture specific biochemical problems may have generalized implications for biochemical testing in many areas. Nonlinear analytical techniques such as neural networks, support vector machines, and nearest neighbour classifiers may serve as an important adjunct to the decision process of a human investigator not 'trained' in a specific complex clinical or laboratory setting and may aid them to classify the problem more directly.

Adult↗

PubMed2004

Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods.

OBJECTIVE: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. MATERIAL: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. METHODS: The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. RESULTS: Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). CONCLUSION: It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important.

Artificial Intelligence↗

PubMed2004

Image fusion for dynamic contrast enhanced magnetic resonance imaging.

BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms. METHODS: In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study. RESULTS: The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue. CONCLUSION: Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation.

Algorithms↗

PubMed2004

Multivariate image analysis in biomedicine.

In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.

Algorithms↗

PubMed2004

Automatic segmentation of digital micrographs: a survey.

Digital micrographs and play a crucial role in today's bio-medical research. Due to progresses in experiment standardization and automation large sets of digital microscopy images, so called micrographs are recorded and stored to databases. The subsequent analysis of the large number of digital images needs the image information to be transformed into quantitative data, which can be processed by statistical methods and datamining. This article summarizes applications of optical microscopy in biomedical research and describes the individual characteristics in micrograph segmentation and classification. An overview on past works based on image processing and artificial neural networks is given and the problem of segmentation evaluation. It concludes with recommendations for future works.

Image Processing, Computer-Assisted↗

PubMed2003

Human vs machine: evaluation of fluorescence micrographs.

To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs. As a part of this, we focus on the question of how to measure the accuracy of microscope image interpretation, by human experts or a computer system. Following standard practice we use methods motivated by receiver operator characteristics to discuss the accuracies of human experts and of neural network-based algorithms. For images of good quality the algorithms achieve the accuracy of the medium-skilled experts. In images with increased noise, the classifiers are outperformed by some of the experts. Furthermore, the neural network-based cell detection is much faster than the human experts.

Algorithms↗