社会的態度構造研究

Articledepartmental bulletin pape

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.journal articl

ニュー・レイバーのヘゲモニック・プロジェクト(三) : 「新しい政治」の左派党戦略

2000-12-25departmental bulletin pape

粉末X線回折装置の取り扱い (平成15年度工学部技術部技術講習会(機器・分析グループ担当)報告)

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Metadata supporting data files of the related article: Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses

In this study, the authors investigated the heterogeneity of luminal-A breast cancers based on heterocellular gene signatures. The aim was to stratify luminal-A breast cancer samples according to well-characterised and cancer-associated heterocellular subtype signatures defined in colorectal cancers, representing stem, mesenchymal, stromal, immune and epithelial cell types. While differences between intrinsic breast cancer subtypes have been well studied, heterogeneity within each subtype, especially luminal-A cancers, requires further interrogation to personalise disease management. At the molecular level, breast cancer was one of the first cancer types to be subtyped into intrinsic gene expression subtypes, with five to ten “intrinsic” subtypes now recognised based on gene expression or integrated molecular characteristics, respectively. It has been established that while some luminal-A tumors are highly responsive to endocrine therapies like tamoxifen, a significant proportion possess intrinsic and/or acquired resistance. Despite luminal-A tumors being a relatively well-characterised breast cancer sub-type, genetic changes alone (mutations and copy number alterations) do not explain the entire spectrum of luminal-A heterogeneity. The factors leading to tumor heterogeneity, including in luminal-A tumors, are complex and include interactions between different cell types and the tumor microenvironment.Methodology and aims:The aim of this study was to further investigate breast cancer heterogeneity, especially in the luminal-A subtype, using heterocellular subtype signatures defined in colorectal cancers (CRCs). This was done similar to the application of breast cancer subtype signatures to other cancers and with an intention to identify low frequency and novel subtypes that are not apparent based on unsupervised approaches.To characterise the breast cancers using heterocellular subtypes, the classifyCMS function from the published R package CMSClassifier was used. The authors applied the Consensus Molecular Subtypes (CMS) of Colorectal Cancer Classifier to two independent breast cancer datasets (the Cancer Genome Atlas; TCGA n=817 and GSE42568 n=104).The intrinsic breast cancer classification for GSE42568 dataset was performed using an R-based Bioconductor package-genefu.Raw gene expression and the corresponding survival data of patient tumors that were analysed during this study, were downloaded from gene expression omnibus (GEO) –GSE42568 and GSE6532 (combined Affymetrix Human Genome U133A and U133B arrays were used). The gene expression profiles for the TCGA breast cancer data were downloaded from the cBioPortal repository.Affymetrix GeneChip microarray data processing and quality control were performed using robust multi-array normalisation (RMA) from R-based Biocunductor package –affy.Hypergeometric sample enrichment analysis of the TCGA dataset was carried out to understand the relationship between the intrinsic breast cancer subtypes and heterocellular subtypes.The results of luminal-A heterogeneity described by heterocellular subtypes, were validated using an additional dataset enriched for estrogen receptor (ER)-positive tumors (luminal-A, GSE6532).To further characterise heterocellular subtypes in luminal-A breast cancers, heatmap analysis of heterocellular gene expression signatures was performed, comparing luminal-A to non-luminal-A (other subtypes) breast cancer samples. For the heatmap, genes were clustered (hierarchical clustering) by Cluster 3.0, using the default settings, followed by visualisation of the clusters using GENEE from GenePattern.Gene enrichment analysis was performed to characterise the immune gene expression heterogeneity in luminal-A tumors.To predict if inflammatory luminal-A tumors may potentially respond to anti-immune checkpoint blockade therapy, a “published expanded immune gene” signature (https://doi.org/10.1172/JCI91190) was used, which potentially predicts anti-PD1 immune-checkpoint responses in melanoma and other cancers.Association between heterocellular subtypes and breast cancer phenotypes such as proliferation was performed using Kruskal-Wallis statistical test.To assess the association of tamoxifen treatment response with heterocellular subtypes, the authors evaluated the relationship between the heterocellular luminal-A subtypes and clinical outcomes in patient samples treated with tamoxifen using the GSE6532 dataset. These results were then compared to recurrence free survival (RFS) from risk of occurrence (ROR) and OncotypeDx.RNAseq data from adjacent normal breast samples were used to evaluate the relationship between heterocellular subtypes and fat, stroma and epithelium in adjacent normal breast tissue. Please see the related article and its supplementary information files for more details on the methodology. Datasets:Data file Poudel et. al_.xlsx provides persistent links, file formats and repository names of the publicly available datasets that were analysed during this study and in turn, used to generate the figures, tables and supplementary figures and tables in this article. Additional files/information derived from published articles, used to generate the figures and/or tables in this study, are given as doi links in the data descriptions below.Supplementary table 1, SupplementaryTable_1_final_20190614.xlsx, and supplementary table 2, Supplementary Table_2_20190614.xlsx and their descriptions, are included in this metadata record.Data supporting figure 1 show the association of breast cancer with heterocellular subtypes, including the proportion of consensus molecular subtypes (CMS) subtypes of colorectal cancer in multiple breast cancer datasets (TCGA and GSE42568) and the proportions of different heterocellular subtypes in luminal-A breast cancer samples (from TCGA).Data supporting figure 2 show heterocellular subtype-based heterogeneity in luminal-A breast cancers. These include gene expression data of the top highly variable and selected marker genes between stem-like and other subtypes within the luminal-A breast cancer subtype and subtypes other than luminal-A (non-luminal A) from TCGA breast cancer data. This figure is also supported by gene set enrichment analysis (GSEA) data showing gene sets enriched in stem-like and inflammatory heterocellular subtype samples compared to the other subtypes from TCGA breast cancer.Data supporting figure 3 show the enrichment of immune checkpoint genes, immune cells, expanded immune (18-gene) signature (https://doi.org/10.1172/JCI91190) and other phenotypes in luminal-A heterocellular subtypes. Gene set enrichment analysis (GSEA) was carried out to compare immune cell types enriched in inflammatory heterocellular subtype samples compared to the other subtypes using the Rooney et al gene sets (https://doi.org/10.1016/j.cell.2014.12.033).Data supporting figure 4 show the association of heterocellular subtypes with published other luminal-A breast cancer subtype classifications (the Ciriello subgroups of luminal-A subtype (https://doi.org/10.1007/s10549-013-2699-3) and two Netanely et al. luminal-A breast cancer subtypes (https://doi.org/10.1186/s13058-016-0724-2)).Data supporting figure 5 show the survival differences in heterocellular subtypes from ER-positive tamoxifen-treated samples.Data supporting figure 6 summarise the luminal-A heterocellular subtypes epithelial-mesenchymal transition (EMT) and copy number alterations.Data supporting supplementary figure 1: Data show the association between intrinsic breast cancer subtypes and normal breast tissue with heterocellular subtypes.Data supporting supplementary figure 2: Data show a comparison of heterocellular subtypes and clusters of luminal-A breast cancers as defined by Aure et.al (https://doi.org/10.1186/s13058-017-0812-y).Data supporting supplementary figure 3: Data normalisation and analysis for the GSE6532 dataset for estrogen receptor-positive and tamoxifen-treated samples, as well as survival differences in heterocellular subtypes from estrogen receptor-positive samples.Data access: All the data analysed in this study were derived from publicly available datasets. The datasets analysed during this study, and in turn used to generate the figures and tables in this article can be found in the NCBI Gene Expression Omnibus (GEO) and cBioPortal for Cancer Genomics repositories and in the UCSC Xena browser (https://xena.ucsc.edu/). Please see Poudel et. al_.xlsx file for links to specific datasets.</div

Additional file 2: of Novel 18-gene signature for predicting relapse in ER-positive, HER2-negative breast cancer

Table S1. List of 585 candidate genes. Table S2. List and identifiers for the 747-patient microarray expression data cohort. Table S3. List of 454 Affymetrix probes studied. Table S4. List of 212 genes significantly prognostic (p < 0.01) in any of the three time periods in the microarray data. Table S5. List of 88 genes by multivariable selections in any of the three time periods in the microarray data. Nodal status was used as a covariate in the regressions. Table S6. List of 17 genes manually removed from the multivariable list. Table S7. List of 29 genes added to the candidate list. Table S8. Details of the 100-probe NanoString code set used in TransATAC. Table S9. HRs, CIs and p values for the 92 genes assessed in TransATAC in univariate analyses. (XLSX 130 kb

負例を厳選した対話応答選択による対話応答生成システムの評価

雑談対話応答生成システムの日々の改良が望ましい方向に効いているか継続的に評価するといった用途として，システムを低コストで評価できる自動評価の枠組みの確立が求められている．しかし，BLEU など，応答生成の自動評価に広く用いられている既存の指標は人間との相関が低いことが報告されている．これは，一つの対話履歴に対し適切な応答が複数存在するという対話の性質に起因する．この性質の影響を受けにくいシステムの評価方法の一つに対話応答選択が考えられる．対話応答選択は，対話履歴に対し適切な応答を応答候補から選ぶタスクである．このタスクではシステムの応答が候補内の発話に限られるため，前述した対話の性質の影響を回避した評価が可能である．一般に対話応答選択では，対話履歴に対する本来の応答（正例）に加え，誤り候補（負例）を無関係な対話データから無作為抽出し応答候補を構成する．しかし，この方法では，正例とかけ離れすぎていて応答として不適切と容易に判別できる発話や，応答として誤りとはいえない発話が負例として候補に混入し，評価の有効性が低下する可能性がある．本論文では，負例を厳選することで不適切な負例の混入を抑制した対話応答選択テストセットの構築方法を提案する．構築したテストセットを用いた対話応答選択によるシステム評価が，BLEU など既存の広く用いられている自動評価指標と比べ人手評価と強く相関することを報告する．journal articl

Dispensatorii universalis ad tempora nostra accomodati et ad formam lexici chemico-pharmaceutici redacti pars secunda.

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