Jonathan M Garibaldi - Academia.edu (original) (raw)
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Papers by Jonathan M Garibaldi
German Conference on Bioinformatics, Sep 1, 2010
Despite recent advances in DNA chip technology current microarray gene expression studies are sti... more Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninformative genes. Combining microarray data with cellular pathway data by using new integrative analysis methods could help to alleviate some of these problems and provide new biological insights. We present a method for learning simple decision rules for class prediction from pairwise comparisons of cellular pathways in terms of gene set expression levels representing the up- and down-regulation of pathway members. The procedure generates compact and comprehensible sets of rules, describing changes in the relative ranks of gene expression levels in pairs of pathways across different biological conditions. Results for two large-scale microarray studies, containing samples from prostate cancer and B-cell lymphoma patients, show that the method provides robust and accurate rule sets and new insights on differentially regulated pathway pairs. However, the main benefit of these predictive models in comparison to other classification methods like support vector machines lies not in the attained accuracy levels but in the ease of interpretation and the insights they provide on the relative regulation of cellular pathways in the biological conditions under consideration.
BMC Bioinformatics, Oct 28, 2009
Breast Cancer Research and Treatment, Aug 10, 2010
Nature Precedings, Jan 18, 2011
Journal of Statistical Software, 2010
IEEE Transactions on Fuzzy Systems, 2020
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
International Journal of Biomedical Engineering and Technology, 2013
Studies in Fuzziness and Soft Computing
2019 IEEE Congress on Evolutionary Computation (CEC)
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016
IEEE Transactions on Fuzzy Systems, 2021
IEEE Transactions on Fuzzy Systems, 2020
IEEE/CAA Journal of Automatica Sinica, 2019
British journal of cancer, Jul 12, 2016
There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) out... more There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) outcomes using robust and routinely available pathology techniques to identify men at most risk of premature death due to prostate cancer. Previous immunohistochemical studies suggest the proliferation marker Ki67 might be a predictor of survival, independently of PSA and Gleason score. We performed a validation study of Ki67 as a marker of survival and disease progression and compared its performance against another candidate biomarker, DLX2, selected using artificial neural network analysis. A tissue microarray (TMA) was constructed from transurethral resected prostatectomy histology samples (n=192). Artificial neural network analysis was used to identify candidate markers conferring increased risk of death and metastasis in a public cDNA array. Immunohistochemical analysis of the TMA was carried out and univariate and multivariate tests performed to explore the association of tumour prot...
2015 IEEE Trustcom/BigDataSE/ISPA, 2015
The Journal of Pathology: Clinical Research, 2016
European Journal of Cancer Supplements, 2008
European Journal of Cancer Supplements, 2007
German Conference on Bioinformatics, Sep 1, 2010
Despite recent advances in DNA chip technology current microarray gene expression studies are sti... more Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninformative genes. Combining microarray data with cellular pathway data by using new integrative analysis methods could help to alleviate some of these problems and provide new biological insights. We present a method for learning simple decision rules for class prediction from pairwise comparisons of cellular pathways in terms of gene set expression levels representing the up- and down-regulation of pathway members. The procedure generates compact and comprehensible sets of rules, describing changes in the relative ranks of gene expression levels in pairs of pathways across different biological conditions. Results for two large-scale microarray studies, containing samples from prostate cancer and B-cell lymphoma patients, show that the method provides robust and accurate rule sets and new insights on differentially regulated pathway pairs. However, the main benefit of these predictive models in comparison to other classification methods like support vector machines lies not in the attained accuracy levels but in the ease of interpretation and the insights they provide on the relative regulation of cellular pathways in the biological conditions under consideration.
BMC Bioinformatics, Oct 28, 2009
Breast Cancer Research and Treatment, Aug 10, 2010
Nature Precedings, Jan 18, 2011
Journal of Statistical Software, 2010
IEEE Transactions on Fuzzy Systems, 2020
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
International Journal of Biomedical Engineering and Technology, 2013
Studies in Fuzziness and Soft Computing
2019 IEEE Congress on Evolutionary Computation (CEC)
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016
IEEE Transactions on Fuzzy Systems, 2021
IEEE Transactions on Fuzzy Systems, 2020
IEEE/CAA Journal of Automatica Sinica, 2019
British journal of cancer, Jul 12, 2016
There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) out... more There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) outcomes using robust and routinely available pathology techniques to identify men at most risk of premature death due to prostate cancer. Previous immunohistochemical studies suggest the proliferation marker Ki67 might be a predictor of survival, independently of PSA and Gleason score. We performed a validation study of Ki67 as a marker of survival and disease progression and compared its performance against another candidate biomarker, DLX2, selected using artificial neural network analysis. A tissue microarray (TMA) was constructed from transurethral resected prostatectomy histology samples (n=192). Artificial neural network analysis was used to identify candidate markers conferring increased risk of death and metastasis in a public cDNA array. Immunohistochemical analysis of the TMA was carried out and univariate and multivariate tests performed to explore the association of tumour prot...
2015 IEEE Trustcom/BigDataSE/ISPA, 2015
The Journal of Pathology: Clinical Research, 2016
European Journal of Cancer Supplements, 2008
European Journal of Cancer Supplements, 2007