57 research outputs found

    Additional file 1 of Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

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    Supplementary figures and tables. Figure S1. The AUCs of precision recall curves of our proposed method when the number of dimensions K increases. Figure S2. Performance comparison of our proposed method and existing network-based methods, evaluated by IntOGen list. Figure S3. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where 位 RV , 位 LV and 位 RU are fixed and 位 LU varies. Figure S4. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where 位 RU , 位 LU and 位 RV are fixed and 位 LV varies. Figure S5. Performance comparison of our proposed method and existing network-based methods, applied on GBM, COADREAD and BRCA datasets and evaluated by IntOGen list. Figure S6. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by CGC list. Figure S7. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by IntOGen list. Figure S8. Performance comparison of our proposed method and existing network-based methods with network information from both iRefIndex and String v10. Table S1. Fisher鈥檚 exact test on the top scored candidates of BRCA results for CGC and IntOGen benchmarking genes. Table S2. Fisher鈥檚 exact test on the top scored candidates of GBM results for CGC and IntOGen benchmarking genes. Table S3. The full list of the top 200 genes detected by our model on GBM dataset. Table S4. The full list of the top 200 genes detected by our model on COADREAD dataset. Table S5. The full list of the top 200 genes detected by our model on BRCA dataset. Table S6. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on COADREAD dataset. Table S7. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on BRCA dataset. (PDF 5670kb

    Compared AUC values of the four algorithms: SLapRLS, SVM, BDT and KNN.

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    <p>Compared AUC values of the four algorithms: SLapRLS, SVM, BDT and KNN.</p

    Pterostilbene鈥揘icotinamide Cocrystal: A Case Report of Single Cocrystals Grown from Melt Microdroplets

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    Screening and single-crystal growth of cocrystals is a time-consuming process, typically performed by solution crystallization. Here we reported a case that a single pterostilbene鈥搉icotinamide cocrystal was efficiently cultivated from melt within 30 min, resulting in successful structure elucidation. This new cocrystal was discovered from melts and can be prepared on a gram scale within 10 min by simply seeding melts at 80 掳C. This work demonstrates the possibility of growing single cocrystal from the melts and highlights the high efficiency of melt crystallization in research of pterostilbene鈥搉icotinamide cocrystals

    Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme-7

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    <p><b>Copyright information:</b></p><p>Taken from "Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme"</p><p>BMC Bioinformatics 2006;7():32-32.</p><p>Published online 22 Jan 2006</p><p>PMCID:PMC1403803.</p><p></p>to data C to construct the five noisy datasets

    Assessment of different methods using dilution series data.

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    <p>Self-consistency for copy number identification by TAFFYS (blue), ASCAT (green) and OncoSNP (red).</p

    Weblogos of S/T substrate kinases and Y substrate kinases.

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    <p>A and C are the Weblogos of kinase ATM and ck2 alora2 and B and D are the Weblogos of kinase EGFR and INSR.</p
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