Structural properties and complexity of a new network class: Collatz step graphsFrank Emmert-Streib
Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine, Health and Life Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 8:e56461. 2013
..Interestingly, in contrast to many other network models including the small-world network from Watts & Strogatz, we find that CS graphs become 'smaller' with an increasing size...
Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methodsFrank Emmert-Streib
Computational Biology and Machine Learning Laboratory, Queen s University Belfast, Belfast, UK
Biol Direct 7:44. 2012
..Further, we provide recommendations for the selection of such analysis methods...
- A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data
Frank Emmert-Streib
Computational Biology and Machine Learning Lab, Center forCancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, 97 Lisburn Road, Belfast, UK
Sci Rep 2:513. 2012
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- Structural measures for network biology using QuACN
Laurin A J Mueller
Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology UMIT, EWZ 1, Hall in Tirol, Austria
BMC Bioinformatics 12:492. 2011
..Hence, there is a strong need to provide freely available software packages to calculate and demonstrate the appropriate usage of structural graph measures in network biology...
- Exploring statistical and population aspects of network complexity
Frank Emmert-Streib
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 7:e34523. 2012
..In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples...
- Limitations of gene duplication models: evolution of modules in protein interaction networks
Frank Emmert-Streib
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 7:e35531. 2012
..This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level...
- Parametric construction of episode networks from pseudoperiodic time series based on mutual information
Frank Emmert-Streib
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 6:e27733. 2011
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- Information processing in the transcriptional regulatory network of yeast: functional robustness
Frank Emmert-Streib
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, 97 Lisburn Road, Belfast, UK
BMC Syst Biol 3:35. 2009
..In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism...
- Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae
Frank Emmert-Streib
Center for Cancer Research and Cell Biology, Queen s University Belfast, UK
BMC Syst Biol 3:76. 2009
..g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated...
- Network biology: a direct approach to study biological function
Frank Emmert-Streib
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Biomedical Sciences, Queen s University Belfast, Belfast, UK
Wiley Interdiscip Rev Syst Biol Med 3:379-91. 2011
..WIREs Syst Biol Med 2011 3 379-391 DOI: 10.1002/wsbm.134 For further resources related to this article, please visit the WIREs website...
- Statistic complexity: combining kolmogorov complexity with an ensemble approach
Frank Emmert-Streib
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 5:e12256. 2010
..The evaluation of the complexity of an observed object is an old but outstanding problem. In this paper we are tying on this problem introducing a measure called statistic complexity...
- A topological algorithm for identification of structural domains of proteins
Frank Emmert-Streib
Stowers Institute for Medical Research, Kansas City, MO 64110, USA
BMC Bioinformatics 8:237. 2007
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Exploratory analysis of spatiotemporal patterns of cellular automata by clustering compressibilityFrank Emmert-Streib
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
Phys Rev E Stat Nonlin Soft Matter Phys 81:026103. 2010
..Our numerical results are not only plausible confirming previous classification attempts but also shed light on the intricate influence of random initial conditions on the classification results...
- Predicting cell cycle regulated genes by causal interactions
Frank Emmert-Streib
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 4:e6633. 2009
..This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first...
- Bagging statistical network inference from large-scale gene expression data
Ricardo de Matos Simoes
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 7:e33624. 2012
..An implementation of BC3NET is freely available as an R package from the CRAN repository...
- Structural influence of gene networks on their inference: analysis of C3NET
Gökmen Altay
School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, BT9 7BL, UK
Biol Direct 6:31. 2011
..For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited...
- Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma
Ricardo de Matos Simoes
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, UK
BMC Syst Biol 6:38. 2012
..However, our current knowledge about the gene regulatory mechanisms that are governed by extracellular signals is severely limited...
- Assessment method for a power analysis to identify differentially expressed pathways
Shailesh Tripathi
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 7:e37510. 2012
..Our study highlights the sensitivity of the studied methods on the parameters of the system...
- Algorithmic computation of knot polynomials of secondary structure elements of proteins
Frank Emmert-Streib
Stowers Institute for Medical Research, Kansas City, Missouri 64110, USA
J Comput Biol 13:1503-12. 2006
..This paves the way for an automatic classification of protein structures...
- Statistical inference and reverse engineering of gene regulatory networks from observational expression data
Frank Emmert-Streib
Computational Biology and Machine Learning Lab, School of Medicine, Dentistry and Biomedical Sciences, Center for Cancer Research and Cell Biology, Queen s University Belfast Belfast, UK
Front Genet 3:8. 2012
..We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms...
- Inferring the conservative causal core of gene regulatory networks
Gökmen Altay
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK
BMC Syst Biol 4:132. 2010
..Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically...
- Influence of statistical estimators of mutual information and data heterogeneity on the inference of gene regulatory networks
Ricardo de Matos Simoes
Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, Belfast, United Kingdom
PLoS ONE 6:e29279. 2011
..Hence, our study provides guidance for an efficient design of a simulation study in the context of network inference, supporting a systems approach...
- Revealing differences in gene network inference algorithms on the network level by ensemble methods
Gökmen Altay
Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
Bioinformatics 26:1738-44. 2010
..Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing...
- Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data
Frank Emmert-Streib
Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, Northern Ireland
BMC Med 11:12. 2013
..We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies...
- The chronic fatigue syndrome: a comparative pathway analysis
Frank Emmert-Streib
Stowers Institute for Medical Research, Kansas City, Missouri 64110, USA
J Comput Biol 14:961-72. 2007
..The structural comparison of UDGs of sick versus non-sick patients allows us to make predictions about the modification of pathways due to pathogenesis...
