Biomedical subjects

Matthias E Futschik

Publications and source records attributed to Matthias E Futschik.

6 recordsLinked to original sources

PubMed2006

UniHI: an entry gate to the human protein interactome.

Systematic mapping of protein-protein interactions has become a central task of functional genomics. To map the human interactome, several strategies have recently been pursued. The generated interaction datasets are valuable resources for scientists in biology and medicine. However, comparison reveals limited overlap between different interaction networks. This divergence obstructs usability, as researchers have to interrogate numerous heterogeneous datasets to identify potential interaction partners for proteins of interest. To facilitate direct access through a single entry gate, we have started to integrate currently available human protein interaction data in an easily accessible online database. It is called UniHI (Unified Human Interactome) and is available at http://www.mdc-berlin.de/unihi. At present, it is based on 10 major interaction maps derived by computational and experimental methods. It includes more than 150,000 distinct interactions between more than 17 000 unique human proteins. UniHI provides researchers with a flexible integrated tool for finding and using comprehensive information about the human interactome.

Computer Graphics↗

PubMed2005

Noise-robust soft clustering of gene expression time-course data.

Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.

Algorithms↗

PubMed2004

OLIN: optimized normalization, visualization and quality testing of two-channel microarray data.

UNLABELLED: Microarray data are generated in complex experiments and frequently compromised by a variety of systematic errors. Subsequent data normalization aims to correct these errors. Although several normalization methods have recently been proposed, they frequently fail to account for the variability of systematic errors within and between microarray experiments. However, optimal adjustment of normalization procedures to the underlying data structure is crucial for the efficiency of normalization. To overcome this restriction of current methods, we have developed two normalization schemes based on iterative local regression combined with model selection. The schemes have been demonstrated to improve considerably the quality of normalization. They are implemented in a freely available R package. Additionally, functions for visualization and detection of systematic errors in microarray data have been incorporated in the software package. A graphical user interface is also available. AVAILABILITY: The R package can be downloaded from http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN. It underlies the GPL version 2. CONTACT: m.futschik@biologie.hu-berlin.de SUPPLEMENTARY INFORMATION: Further information about the methods used in the OLIN software package can be found at http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN.

Algorithms↗

PubMed2003

Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue.

Microarray techniques have made it possible to observe the expression of thousands of genes simultaneously. They have recently been applied to study gene expression patterns in tissue samples. This may lead to highly desirable improvements in the diagnosis and treatment of human diseases. Statistical and machine learning methods have recently been used to classify cancer tissue based on gene expression data. Although some of these methods have achieved a high degree of accuracy, they generally lack transparency in their classification process. This, however, is crucial for the application in the medical field. In order to overcome this obstacle, we used knowledge-based neurocomputing (KBN), since KBN seeks to gain knowledge that is comprehensible to humans. In particular, we applied evolving fuzzy neural networks (EFuNNs) to classify cancer tissue, which is illustrated on the case studies of leukaemia and colon cancer. EFuNNs belong to the evolving connectionist system paradigm (ECOS) that has been recently introduced. They are well suited for adaptive learning and knowledge discovery. Fuzzy logic rules can be extracted from the trained networks and offer knowledge about the classification process in an easily accessible form. These rules point to genes that are strongly associated with specific types of cancer and may be used for the development of new tests and treatment discoveries.

Algorithms↗

PubMed2003

Prediction of clinical behaviour and treatment for cancers.

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Algorithms↗

PubMed2002

Association of CDH1 haplotypes with susceptibility to sporadic diffuse gastric cancer.

Truncating mutations in the gene for the cell to cell adhesion protein E-cadherin are the most consistent genetic alterations observed in sporadic and hereditary diffuse gastric cancer (DGC). In addition to these inactivating mutations, a CDH1 promoter polymorphism at position -160 has been reported to lead to transcriptional downregulation of the gene in vitro. We therefore performed a case-control study to investigate whether this variant is associated with an increased susceptibility to DGC. The frequency of the -160A allele was significantly higher (P<0.005) in 53 diffuse gastric cancer cases compared to 70 matched controls. The odds ratio associated with the A-allele was 2.27 for CA-heterozygotes (95%CI 1.16-4.44) and 7.84 for AA-homozygotes (95%CI 2.89-21.24). Two additional polymorphisms (the 48+6T-->C and the 2076C-->T variant) were genotyped and shown to be equally distributed among cases and controls. Haplotype analysis with the three polymorphisms confirmed an association with disease (P<0.004). However, this analysis suggested the -160C-->A CDH1 promoter polymorphism may be in linkage disequilibrium with a distinct aetiological locus or acts in combination with other functional variants in or near the CDH1 region.

Adenocarcinoma↗