Genomic Landscape and Clinical Utility in Korean Advanced Pan-Cancer Patients from Prospective Clinical Sequencing: K-MASTER Program.

Unlabelled: The fundamental principle of precision oncology is centralized on the identification of therapeutically exploitable targets that provides individual patients with cancer an opportunity to make informed decisions on a personalized level. To facilitate and adopt such concepts within clinical practice, we have initiated a nationwide, multi-institutional precision oncology screening program to examine and enroll patients into the most appropriate clinical trial based on their tumor's unique molecular properties. To determine the prevalence of essential major driver mutations and to explore their dynamic associations at both molecular and pathway levels, we present a comprehensive overview on the genomic properties of East Asian patients with cancer.

Comparison of the Data of a Next-Generation Sequencing Panel from K-MASTER Project with That of Orthogonal Methods for Detecting Targetable Genetic Alterations.

Purpose: K-MASTER project is a Korean national precision medicine platform that screened actionable mutations by analyzing next-generation sequencing (NGS) of solid tumor patients. We compared gene analyses between NGS panel from the K-MASTER project and orthogonal methods.

Materials And Methods: Colorectal, breast, non-small cell lung, and gastric cancer patients were included.

Clinical Application of Targeted Deep Sequencing in Metastatic Colorectal Cancer Patients: Actionable Genomic Alteration in K-MASTER Project.

Purpose: Next-generation sequencing (NGS) can facilitate precision medicine approaches in metastatic colorectal cancer (mCRC) patients. We investigated the molecular profiling of Korean mCRC patients under the K-MASTER project which was initiated in June 2017 as a nationwide precision medicine oncology clinical trial platform which used NGS assay to screen actionable mutations.

Materials And Methods: As of 22 January 2020, total of 994 mCRC patients were registered in K-MASTER project.

DialysisNet: Application for Integrating and Management Data Sources of Hemodialysis Information by Continuity of Care Record.

Objectives: Health Avatar Beans was for the management of chronic kidney disease and end-stage renal disease (ESRD). This article is about the DialysisNet system in Health Avatar Beans for the seamless management of ESRD based on the personal health record.

Methods: For hemodialysis data modeling, we identified common data elements for hemodialysis information (CDEHI).

ITF2 prevents activation of the β-catenin-TCF4 complex in colon cancer cells and levels decrease with tumor progression.

Background & Aims: Immunoglobulin transcription factor 2 (ITF2) was believed to promote neoplastic transformation via activation of β-catenin. However, ITF2 recently was reported to suppress colon carcinogenesis. We investigated the roles of ITF2 in colorectal cancer cell lines and tumor formation and growth in mice.

Lifelog agent for human activity pattern analysis on health avatar platform.

Objectives: To provide accurate personalized medical care, it is necessary to gather individual-related data or contextual information regarding the target person. Nowadays a large number of people possess smartphones, which enables sensors in the smartphones to be used for lifelogging. The objective of the study is to analyze human activity pattern by using lifelog agent cooperating with the Health Avatar platform.

Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases.

Background: Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions.

BioLattice: a framework for the biological interpretation of microarray gene expression data using concept lattice analysis.

Motivation: A challenge in microarray data analysis is to interpret observed changes in terms of biological properties and relationships. One powerful approach is to make associations of gene expression clusters with biomedical ontologies and/or biological pathways. However, this approach evaluates only one cluster at a time, returning long unordered lists of annotations for clusters without considering the overall context of the experiment under investigation.

ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics.

ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics (SVG).

ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics.

Biological pathways can provide key information on the organization of biological systems. ArrayXPath (http://www.snubi.

ChromoViz: multimodal visualization of gene expression data onto chromosomes using scalable vector graphics.

Summary: ChromoViz is an R package for the visualization of microarray gene expression data, cross-species and cross-platform comparisons, as well as non-expression genomic data obtained from public databases onto chromosomes. Chromosomal visualization format is proposed for the clear decoupling of the data layer from the procedure layer and the combined visualization of genomic data from heterogeneous data sources. Visualization with Javascript-enabled scalable vector graphics enables interactive visualization and navigation of data objects on the Web.