Systems Biology in Cancer Research and Drug Discovery

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We apply our predictive models to highlight hidden opportunities in novel drug targets, identify risks associated with them, and suggest key experiments to de-risk them. It is also widely used by more than , users from over counties. It is used by academia and industry to inform their drug discovery. Chemical probes are widely used in translational biological research and target validation to study specific proteins. However, the use of poor, non-selective probes by the community is rife due to lack of resources to better inform chemical probe selection, with profound implications for data robustness and reproducibility.

Probe Miner uses medicinal chemistry and chemical biology curated within canSAR to analyse chemical probes for fitness Probe Miner is regularly updated and provides a user friendly access point for researchers. To address these problems, we must develop a picture of each patient that embraces the complex interplay between a large number of factors.

We also provide a hub for information and bioinformatics capabilities at the ICR. We organise and run bioinformatics training courses and flash talks. If you are the author of this article you still need to obtain permission to reproduce the whole article in a third party publication with the exception of reproduction of the whole article in a thesis or dissertation. Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.

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Department of Data Science

Front Mol Biosci ; 2 : Evolving roles for physicians and genetic counselors in managing complex genetic disorders. Clin Transl Gastroenterol ; 6 : e Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res ; 43 : e FRes ; 3 : Using exposomics to assess cumulative risks and promote health. Environ Mol Mutagen ; 56 : — Systems biology-driven hypotheses tested in vivo: the need to advancing molecular imaging tools.

Methods Mol Biol ; : — Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. BMC Bioinformatics ; 14 : 7. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics ; 14 : FunRich: an open access standalone functional enrichment and interaction network analysis tool. Proteomics ; 15 : — Nucleic Acids Res ; 43 : D — D Lloyd S. Least squares quantization in PCM.

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OpenUrl CrossRef. Cluster analysis and display of genome-wide expression patterns. Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc ; 58 : Kaufman L , Rousseeuw PJ.

New York , Wiley , Langfelder P , Horvath S. BMC Bioinformatics ; 9 : Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics ; 13 : Nucleic Acids Res ; 44 : W — W Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet ; 25 : 25 — KEGG: new perspectives on genomes, pathways, diseases and drugs.

Spotlight on Research

Nucleic Acids Res ; 45 : D — D The Reactome pathway Knowledgebase. Nucleic Acids Res ; 44 : D — D Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res ; 13 : — Techniques for clustering gene expression data. Comput Biol Med ; 38 : — Maimon , O , Rokach L. Data Mining and Knowledge Discovery Handbook. New York , Springer , An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

BMC Syst Biol ; Similarity network fusion for aggregating data types on a genomic scale. Nat Methods ; 11 : — Bioinformatics ; 26 : i — i Integrative multi-omics module network inference with Lemon-Tree. PLoS Comput Biol ; 11 : e Bayesian correlated clustering to integrate multiple datasets. Bioinformatics ; 28 : — Patient-specific data fusion defines prognostic cancer subtypes.

PLoS Comput Biol ; 7 : e Bayesian consensus clustering. Bioinformatics ; 29 : — An integrated approach to uncover drivers of cancer.

An Integrative Systems Biology Approach for Prioritizing Biomarkers and Drug Targets in Diseases

A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics ; 27 : i — i Pattern discovery and cancer gene identification in integrated cancer genomic data. Bayesian joint analysis of heterogeneous genomics data. Bioinformatics ; 30 : — ATHENA: identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData Min ; 6 : Discovering regulatory and signalling circuits in molecular interaction networks.

Bioinformatics ; 18 : Suppl. A statistical framework for genomic data fusion. Bioinformatics ; 20 : — A pathway-based data integration framework for prediction of disease progression. Bayesian methods for expression-based integration of various types of genomics data. An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.

BMC Syst Biol ; 4 : Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme.

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