Supporting Products
 
 
 
 
Overview Why choose MDP?
Features  

MicroArray Discovery Platform
 

Overview

 

MDP is aimed at laboratories that strive to fabricate chips in-house. It is a cost effective system that acts as a super-wizard to guide users through every critical step in the fabrication processes, thus ensuring the quality and reliability of chips produced. Integrating web technologies seamlessly with various instruments and bio-repositories, MDP also helps users to perform data management of the large datasets and images generated. In addition, MDP comes packaged with advanced visualization and data analysis / mining tools. The objective is to enable the laboratories to provide end-to-end services for its users, ultimately facilitating new discovery and pushing the frontier in bioresearch.

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Why choose MDP?

    For Scientists:
  1. Able to make chips reliably and reduce wastage
  2. Awards winning algorithms and powerful visualization tools integrated
  3. Web based to allow collaborative research
  4. Public domain databases are mirrored in-house for large scale data processing
  5. In-house experimental data and publicly available annotations are seamlessly integrated
  6. MIAME compliance


    For Lab Operator:
  1. Sample/plate tracking and storage with barcodes
  2. Auto data collection and backup from instruments
  3. User entries (protocols, etc.) are validated
  4. Log files and emails to notify failure or error


    For Administrator:
  1. Automatic updating and synchronizing of various public databases with in-house database
  2. Complete setup within 3 months
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Features Overview

 

MDP comes with 3 modules to facilitate fabrication of microarrays, data management and data analysis.

 

  • Fabrication Module
  • User and Project Management
  • Probe Preparation
  • Microarray Printing
  • Data Management Module
  • Data upload as experiments and projects
  • QC plots
  • Set of Single-Array Normalization algorithms
  • Public database downloading and maintenance
  • Data Analysis Module
  • Analytical history of analysis performed
  • Data Filtering and cross normalization
  • Class Discovery with Data partitioning, hierarchical clustering and PCL algorithms
  • Class Prediction with Data partitioning and correlation test, Cross validation and CS4 algorithms
  • Visualization with Microarray Genome Mapper and other tools
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