Toward better benchmarking: challenge-based methods assessment in cancer genomics

[1]  Joshua M. Stuart,et al.  Global optimization of somatic variant identification in cancer genomes with a global community challenge , 2014, Nature Genetics.

[2]  Minjae Lee,et al.  RNA design rules from a massive open laboratory , 2014, Proceedings of the National Academy of Sciences.

[3]  Daniel Nilsson,et al.  An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge , 2014, Genome Biology.

[4]  J. Harrow,et al.  Assessment of transcript reconstruction methods for RNA-seq , 2013, Nature Methods.

[5]  Jeroen F. J. Laros,et al.  Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories , 2013, Nature Biotechnology.

[6]  Ralf Zimmer,et al.  A Turing test for artificial expression data , 2013, Bioinform..

[7]  Adam A. Margolin,et al.  Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas , 2013, Nature Genetics.

[8]  Mario Lauria,et al.  Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge , 2013, Bioinform..

[9]  R. Daniel Kortschak,et al.  A comparative analysis of algorithms for somatic SNV detection in cancer , 2013, Bioinform..

[10]  Terence P. Speed,et al.  Comparing somatic mutation-callers: beyond Venn diagrams , 2013, BMC Bioinformatics.

[11]  J C Costello,et al.  Seeking the Wisdom of Crowds Through Challenge‐Based Competitions in Biomedical Research , 2013, Clinical pharmacology and therapeutics.

[12]  Ben Hamner,et al.  Crowd computing: using competitive dynamics to develop and refine highly predictive models. , 2013, Drug discovery today.

[13]  Adam A. Margolin,et al.  Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer , 2013, Science Translational Medicine.

[14]  Daniel W. A. Buchan,et al.  A large-scale evaluation of computational protein function prediction , 2013, Nature Methods.

[15]  Inanç Birol,et al.  Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species , 2013, GigaScience.

[16]  Eran Halperin,et al.  Identifying Personal Genomes by Surname Inference , 2013, Science.

[17]  Taking pan-cancer analysis global , 2013, Nature Genetics.

[18]  P. Lambin,et al.  Predicting outcomes in radiation oncology—multifactorial decision support systems , 2013, Nature Reviews Clinical Oncology.

[19]  H. Hakonarson,et al.  Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing , 2013, Genome Medicine.

[20]  Igor Jurisica,et al.  Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies , 2012, Genome Medicine.

[21]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[22]  Maqc Consortium The MicroArray Quality Control ( MAQC )-II study of common practices for the development and validation of microarray-based predictive models , 2012 .

[23]  Benjamin M. Good,et al.  Games with a scientific purpose , 2011, Genome Biology.

[24]  Glenn Fung,et al.  A Simple but Highly Effective Approach to Evaluate the Prognostic Performance of Gene Expression Signatures , 2011, PloS one.

[25]  R. Norel,et al.  The self-assessment trap: can we all be better than average? , 2011, Molecular systems biology.

[26]  David Venet,et al.  Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome , 2011, PLoS Comput. Biol..

[27]  Julio Saez-Rodriguez,et al.  Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge , 2011, Science Signaling.

[28]  M. Peitsch,et al.  Verification of systems biology research in the age of collaborative competition , 2011, Nature Biotechnology.

[29]  H. Waters New $10 million X Prize launched for tricorder-style medical device , 2011, Nature Medicine.

[30]  H. Waters FDA approval signals more 'homework' on the horizon in trials , 2011, Nature Medicine.

[31]  Rob J Hyndman,et al.  The value of feedback in forecasting competitions , 2011 .

[32]  Bradley P. Coe,et al.  Genome structural variation discovery and genotyping , 2011, Nature Reviews Genetics.

[33]  Joel T Dudley,et al.  In silico research in the era of cloud computing , 2010, Nature Biotechnology.

[34]  Steven L Salzberg,et al.  Between a chicken and a grape: estimating the number of human genes , 2010, Genome Biology.

[35]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

[36]  James H. Doroshow,et al.  AACR-FDA-NCI Cancer Biomarkers Collaborative Consensus Report: Advancing the Use of Biomarkers in Cancer Drug Development , 2010, Clinical Cancer Research.

[37]  D. Floreano,et al.  Revealing strengths and weaknesses of methods for gene network inference , 2010, Proceedings of the National Academy of Sciences.

[38]  N. D. Clarke,et al.  Correction: Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges , 2010, PLoS ONE.

[39]  N. D. Clarke,et al.  Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges , 2010, PloS one.

[40]  D. Ransohoff Proteomics research to discover markers: what can we learn from Netflix? , 2010, Clinical chemistry.

[41]  K. Coombes,et al.  Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology , 2009, 1010.1092.

[42]  Robert E. Kearney,et al.  A HUPO test sample study reveals common problems in mass spectrometry-based proteomics , 2009, Nature Methods.

[43]  Jimin Pei,et al.  Analysis of CASP8 targets, predictions and assessment methods , 2009, Database J. Biol. Databases Curation.

[44]  Gustavo Stolovitzky,et al.  Lessons from the DREAM2 Challenges , 2009, Annals of the New York Academy of Sciences.

[45]  Igor Jurisica,et al.  Prognostic gene signatures for non-small-cell lung cancer , 2009, Proceedings of the National Academy of Sciences.

[46]  David Warde-Farley,et al.  Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.

[47]  Anna Tramontano,et al.  Evaluation of CASP8 model quality predictions , 2009, Proteins.

[48]  R. Bernards,et al.  Enabling personalized cancer medicine through analysis of gene-expression patterns , 2008, Nature.

[49]  Joe W. Gray,et al.  Translating insights from the cancer genome into clinical practice , 2008, Nature.

[50]  K. Gunsalus,et al.  Network modeling links breast cancer susceptibility and centrosome dysfunction. , 2007, Nature genetics.

[51]  Kai Wang,et al.  Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks , 2007, ISMB/ECCB.

[52]  Catalin C. Barbacioru,et al.  Evaluation of DNA microarray results with quantitative gene expression platforms , 2006, Nature Biotechnology.

[53]  Anna Tramontano,et al.  Assessment of homology‐based predictions in CASP5 , 2003, Proteins.

[54]  K Fidelis,et al.  A large‐scale experiment to assess protein structure prediction methods , 1995, Proteins.