About
Substantial amounts of data are being generated within cancer research. Datasets range from gene expression and microRNA array data through to next generation sequence data. Data interpretation draws on mathematical and computational skills and thus the subject has engaged the interest of researchers in areas such as machine learning, statistics, bioinformatics and computer science. The goal of this cross-disciplinary Workshop is therefore to bring together researchers from these disciplines and cancer researchers who have an interest in data analysis, to explore and present innovative approaches to this subject. Presented papers should:
- Propose novel data analysis methods applicable to this domain or:
- Present bioinformatics-driven studies in which mathematical or computational methods played an important role in finding results of potential significance in cancer research.
For novel data analysis methods, a non-exhaustive list of suitable topics include:
* Unsupervised, semi-supervised and biclustering methods to highlight disease subtypes or dysregulated genes within these subtypes,
* Data integration/data fusion methods to integrate different types of data such as gene expression, microRNA expression and array CGH data,
* Inference of gene regulatory networks,
* Pathway modeling and probabilistic ranking of pathway models,
* Biomarker discovery,
* Genome-wide association studies,
* Rational drug design methods and chemoinformatics,
* Protein function, structure prediction and structural bioinformatics,
* microRNA target site prediction,
* Analysis of high throughput sequencing data,
* Gene expression and post-transcriptional regulation,
* Methods for the detection of fusion genes,
* Prediction of disease progression,
* Probabilistic inference, Bayesian methods and Kernel-based methods for classifier design with applications to cancer bioinformatics,
* Methods for the detection and quantification of copy number alterations and deletions.
The Workshop is principally focused on the intepretation of omics datasets and does not cover related areas such as cancer imaging or development of software tools unless in the context of novel methodology.
More about the workshop at http://www.enm.bris.ac.uk/cig/cb/.
Related categories
Uploaded videos:
Invited Speakers
Towards Evidential Inference of Signalling Pathway Topologies
Oct 11, 2010
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4086 Views
Gene expression state space models and cell fate transitions
Oct 11, 2010
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4547 Views
Cancer-specific high throughput analysis of somatic mutations
Oct 11, 2010
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4492 Views
Integrating genetic and gene expression evidence into genome-wide association an...
Oct 11, 2010
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3903 Views
Learning and retrieval from multiple sources
Oct 11, 2010
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3438 Views
The Importance of Reproducible Research in High-Throughput Biology: Case Studies...
Oct 11, 2010
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41699 Views
Lectures
Uncovering signalling differences between primary and transformed hepatocytes us...
Nov 29, 2010
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3361 Views
Estimating Rearrangement Evolution in Cancer with Massively Parallel Paired End ...
Oct 11, 2010
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3240 Views
Differential regulation of gene expression by copy-number alterations in cancer ...
Oct 11, 2010
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4365 Views
Fast joint segmentation of multiple array CGH profiles for detecting frequent co...
Oct 11, 2010
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3171 Views
An algorithm to detect copy number aberrations in cancer genomes of tumour speci...
Oct 11, 2010
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3062 Views
A random coefficients model for regional co-expression associated with DNA copy ...
Oct 11, 2010
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2739 Views
Spatial clustering of array CGH features in combination with hierarchical multip...
Oct 11, 2010
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3003 Views
Finite-state transducers for inferring tumour evolution from copy number variati...
Oct 11, 2010
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3002 Views
Developing a substitution calling algorithm to analyse breast cancer exomes by n...
Oct 11, 2010
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4688 Views
A comprehensive analysis combining network inference and pathway analysis for tr...
Oct 11, 2010
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3567 Views
Biomarkers Discovery in Breast Cancer by Interactome-Transcriptome Integration
Oct 11, 2010
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4540 Views
Non-Negative Matrix Factorisation finds Connections in Complex Data
Oct 11, 2010
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3984 Views