アメリカ合州国の信用組合産業

Articledepartmental bulletin pape

Locational Characteristics of Apartments in the central part of Kawaguchi City

研究ノートdepartmental 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

平成14年度東海・北陸地区国立学校等教室系技術職員合同研修(化学コース)に参加して

application/pdfdepartmental bulletin pape

中国製造物責任の研究(三)

2000-09-30departmental bulletin pape

Compressive Strength and Elastic Modulus of Polystyrol Concrete : Application of a Simplified Two-Phase Structural Model

departmental bulletin pape

Outcome Prediction Using the SPC Risk Score

<div><p>(A) Overview of the gene expression patterns of the 259 prognostic genes in the training set, with their SPC risk scores arranged in ascending order and the survival time in descending order. Each row represents a single gene, and each column a patient sample. The degree of color saturation corresponds to the ratio of gene expression in each sample compared to the mean expression across all samples.</p> <p>(B) Gene expression profiles of the 259 prognostic genes in the test set.</p> <p>(C) Kaplan-Meier estimates of disease-specific survival in low-, intermediate-, and high-risk groups of patients in the training set defined by the tertiles of SPC risk scores.</p> <p>(D) Kaplan-Meier estimates of disease-specific survival of low, intermediate and high-risk groups of patients in the test set defined based on the tertiles of the SPC risk scores of the training set.</p> <p>(E) Kaplan-Meier estimates of disease-specific survival in stage group I and II patients in the test set.</p> <p>(F) Kaplan-Meier estimates of disease-specific survival in stage III and IV patients in the test set.</p></div

Relationship of Gene Expression Subgroups to Clinical Parameters and SPC Risk Score in 177 cRCCs

<div><p>(A) Dendrogram from the hierarchical cluster, with the clinical information for each of the samples. Subgroups are color-coded as in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0030013#pmed-0030013-g001" target="_blank">Figure 1</a>. Color shade corresponds to the ranges of each of the clinical parameters displayed. Expected survival times for the censored observations were estimated from the Kaplan-Meier curve for all patients.</p> <p>(B) Distribution of stage, grade, and patient performance status among five subgroups.</p> <p>(C) Kaplan-Meier estimates of disease-specific survival in the two main gene expression groups of patients (subgroups 1 and 2 shown by the red bar below the dendrogram, compared to subgroups 3, 4, and 5 designated by the green bar).</p> <p>(D) Kaplan-Meier estimates of disease-specific survival in the five subgroups of patients.</p> <p>The X symbols in (C) and (D) denote censored data.</p></div

Overview of the Strategy Used for the Development and Validation of a Prognostic Gene List

<p>Overview of the Strategy Used for the Development and Validation of a Prognostic Gene List</p