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Probe PINGS™

Product Overview

Protein Interaction Network Generation System
(System for the acquisition of the mutual relationship information of the protein)
Consist of 5 modules (PPI module, Path-Finder module, Path-Linker module, Path-maker module, Path-Lister module)
Visualization Module of Protein Interaction Network & Signal Pathway Information

Main Function

  • 1. PPI Module (Protein-Protein Interaction module)
    • PPI module displays the diagram showing the relationship between the interested protein and the related protein.
    • Uniprot site linkage when Protein Box double clicked in PPI result view
    • Interaction information list displayed when the line linked Protein Box in PPI result view and link view is displayed when double clicked the information you want
  • 2. Path-Finder Interaction module
    • Finder module provides the function which suggests the specific signal transduction pathway including the protein that interacts with the proteins through search function.
  • 3. Path-Linker module
    • Linker module displays the interested protein and the related proteins included within the entered same signal transduction pathway by the interaction distance
  • 4. Path-Marker module
    • Marker module displays the reference frequency of interested protein in combination with the related protein included within same signal transduction pathway
  • 5. Path-Lister module
    • Lister module provides the statistical function which displays the list of the interested protein-associated pathway, the list of first protein included in the interested protein-associated pathway and the list of the interaction frequency with the last protein.
    • Priority statistics of protein interacting input protein Selecting the protein user want by providing the protein list corresponding with the keyword in UniProtKB/SwissProt DB

Filter setting Function

(Organism, interaction Type, Interaction detection method, Number of information[number of information providing, number of related articles, number of interaction detection process etc.)

CBS STAT™ & CBS MINES™

Product Overview

Statistical analysis of a single gene and Statistical analysis and verification through a combination of multiple genes
Consist of 18 modules and 104 functions
Database System for development of Liver cancer prognostic gene markers

Main Function

  • 1. Major Statistical Module
    • 1) Module for analysis on survival(Kaplan-Meier analysis) - Analyze the graph after estimation or estimate the survival function (Recurrence, death, and the survival without disease)
    • 2) Cox regression analysis - Risk Function or Risk Rate analysis by analyzing risk factor till survival or recurrence period Cox regression analysis ‒ Analyze the effect of several risk factors discovered during the patient's survival period or until the recurrence using the function on risk or the risk rate
    • 3) Cross-validation module Validate the accuracy of validation group by applying the cutoff value of training group after the randomization of data
    • 4) Receiver Operating Characteristic - Analyzing accuracy of test, AUC(area under the curve) value calculation Analyze the test's accuracy (by calculating the area under the AUC curve), sensitivity, and specificity
    • 5) T-test module - Mean difference analyzing of sample group (NT vs. T, Stage, Size, VI, VP …) Analyze the differences of average between two groups ex) Clinical parameters (NT vs. T, Stage, Size, VI, V, and etc.)
    • 6) Other module - Chi-square test module, Fisher's exact test module, Scatter plot module, and etc.
  • 2. Characteristics of Statistical Module
    • 1) Operating System: Linux
    • 2) Own developed statistical system by R-package make possible big data processing
    • 3) Batch mode provided
    • 4) Data cable provided (KM result, Cox result, T-test, ROC etc)
    • 5) Graph generating function provided (KM graph, ROC curve, Box-plot, Scatter plot etc)
    • 6) User Customizing, expandability, big data processing, multi-user
  • 3. Cancer Prognosis gene marker development Data Base system
    • Specimen information (Clinical information, specimen information, Protein yield, remains of cDNA)
    • Experiment Information (Proteomics, gene expression, protein expression, cell line experiment)
    • Previous Research (Collecting the information of target gene and protein) (Reference(PubMed), GeneCards, Disease relationship etc)
    • Other paper (related antibody information etc)

CBS LIMS™

Product Overview

Use of management for liver-specific clinical information, Historical queries of patients, clinical research and clinical statistics/frequency & trend
Use for customized cure of operation & post operation by clearly viewed summary of the liver-cancer specified information such as the patient's medical history, test, treat, cure, surgery, complications, pathology, recurrence, death
Easy search of category particular interest in liver cancer patients (medical history, test, treat, cure, surgery, etc.)
Effective use and application of data searched with detailed conditions for the liver cancer related clinical research
Research connecting with clinical information of each hospital by standardization
Convenient management of live cancer patient by multi user function, (input data in outpatient and each department)

Main Function

  • 1. Function
    • 1) Easy data input (Input with chekcing method)
    • 2) User access menu management
    • 3) Various search function ( Clinical and whole category search function)
    • 4) Saving various file format
    • 5) Manual and auto saving
    • 6) Multi-User able to input data number of patient clinical information simultaneously
  • 2. Configuartion
    • 1) Patient information/History/Test of pre-operation/Treatment of pre-operation/ Supportive care of pre-operation
    • 2) Operation/Post-operation complication/Supportive care of post operation
    • 3) Pathological opinion/ Hepato pathological opinion
    • 4) Recurrence/Death
    • 5) OncoHepa Test

Dr. Medic™

Product Overview

Based on the medical expertise database, suggesting disease prediction and proper test from the symptoms / signs of patients, assistance system to help doctor's diagnosis confirmed through a comparison of the predicted disease and diagnostic tests (expert)

Main Function

  • Prediction of disease from patient symptom and sign through 'Identifying Diagnosis DB', providing various references of predicted disease (clinical symptom, test, diagnosis, research, cure etc)
  • Various platforms are available, PC, PDA, Server etc.
  • Connectivity and expandability to the existing medical information system in hospital
  • Providing information based on the KCD(Korean Standard Classification of Diseases) and ICD-9-CM ( international classification of diseases, 9th revision, clinical modification )
  • Supporting wide range of disease prediction, fracture, infection, bleeding, allergy, deficiency, metabolism, inflammation, cancer, disability, cardiac disease etc.
  • To build a dedicated DB utilized disease prediction in emergency and special environments (military, polar area, space, school)
  • Search the result to 10 complex symptom, result view showing by disease type, numbering of disease symptom combination
  • Result view showing of applicable symptoms for the predicted disease (Function selecting the number of symptom combination)
  • Building a deduction model using similar disease test, suggesting important test to deduction, Disease deduction by input the test results when new patients are suspected similar disease

Inject Medic™

Product Overview

Android OS based smart device systems to prevent medication error utilize a device which block the possibility of injection error when administered blood transfusion or chemotherapy

Main Function

  • Client, Server security communication function
  • Device ID, User management function
  • Connectivity to the medical information system
  • TTS (Text to Speech) module function
  • Bar code, RFID recognition function
  • Prescription search function
  • History management function
  • Preventing transfusion error and anti-cancer injection error