About Us

Mass Spectral Imaging Software

• Software Functionality:
  • Displays mass spectral data for a specific region of interest.
• Visualization Format:
  • Presents mass intensity through a color plot.
• Application in Spatial Analysis:
  • Useful for visualizing the spatial distribution of proteins, peptides, and lipids.
• Tissue Slices Analysis:
  • Particularly beneficial for examining thin slices of tissue from animals or plants.
• Medical Application:
  • Applicable in cancer treatment diagnostics.
• Diagnostic Capability:
  • Differentiates among tumor stages and identifies metastasis.

Polymer Analysis Software

Signature Database

• Data Collection:
  • Collect multiple spectra for the target species.
• Spectrum Quality Assessment:
  • Apply a spectrum quality algorithm to identify and reject poor-quality spectra.
• Noise Reduction:
  • Use a smoothing algorithm (e.g., Savitzky-Golay) to filter out noise from the spectra.
• Baseline Correction:
  • Correct baseline fluctuations using the Top Hat algorithm.
• Peak Detection:
  • Utilize a peak detection algorithm to select the highest-intensity peaks in the spectra.
• Peak Ordering and Truncation:
  • Order the selected peaks by frequency.
  • Truncate the peak list to a predetermined number of peaks.
• Signature Spectra Creation:
  • Combine the truncated spectral data to create one or more signature spectra representing the species.

Micro-Organism Signature Database

• CoreDB: signature database for human-originated microorganisms. It is made with
  spectrum data from over 2,700 species of bacteria.
• MycoDB: signature database with 100 species of myco-bacteria.
• FungiDB: signature database with 116 species of fungi.
• VetDB: signature database with 627 species of animal-originated bacteria.

MALDI-TOF Spectral Database Management Software

• Software Functionality:
  • Data Management Software for MALDI-TOF spectral data.
• Storage and Retrieval:
  • Capable of storing and retrieving spectral data efficiently.
• User Query Interface:
  • Allows users to retrieve specific spectral data using queries.
• Cloud Environment Compatibility:
  • Operates in a cloud environment for scalability.
  • Enables the storage of spectral data on a server without limitations.

Disease Analysis Software

• Software Purpose:
  • Micro-Organism Identification Software for identifying infectious microorganisms.
• Data Source:
  • Utilizes MALDI-TOF spectral data obtained from patients.
• Algorithm Implementation:
  • Implements algorithms for evaluating spectral quality.
  • Provides matching scores for accurate microorganism species identification.
• Process Flow:
  • Receives and utilizes MALDI-TOF data generated by the machines.
• Matching Algorithm:
  • Utilizes a scoring algorithm to compare generated data with a database of spectrum signatures.
• Identification Outcome:
  • Determines the most likely microorganism present in the sample.
  • Displays the nearest-matching microorganism species along with a score representing signal quality.
• Cross-correlation:
  • Cross-correlates the passed spectrum against the signature database.
  • Incorporates additional marker data when necessary for distinguishing visually-similar candidates.

Cancer Diagnosis Software

• Software Purpose:
  • Cancer Diagnosis Software for diagnosing cancer from MALDI-TOF spectrum data.
• Data Source:
  • Analyzes mass spectra from blood samples containing proteins or glycans related to early signs of cancer.
• Spectral Differentiation Challenges:
  • Recognizes that spectral patterns may slightly vary based on patient factors like age, sex, and disease history.
• Optimal Clustering Method:
  • Utilizes a sophisticated method for finding optimal clustering in the spectral data.
• Statistical Approach:
  • Employs statistical methods, including p-value analysis, for optimal clustering.
• Machine Learning Method:
  • Implements machine learning methods, such as Random Forest, for identifying optimal clusters.
• Spectral Pattern Analysis:
  • Analyzes mass spectra to differentiate and identify patterns associated with cancer.
• Customized Diagnosis:
  • Tailors diagnosis based on individual patient characteristics captured in the spectral data.

Automatic DB Generation Software

• Software Purpose:
  • Automatic DB Generation Software for streamlining the complex database development process.
• Manual Database Development Challenges:
  • Acknowledges challenges in manual database development, including spectrum quality control, identification reliability, and addressing false positive/negative tendencies.
• Automation Tasks:
  • Pipelines a series of tasks to automate the entire updating process of a database.
    • Generates and validates a CMS DB.
    • Generates and validates a Marker DB.
• CMS Generation:
  • Combines replicated spectra of a species into a single representative spectrum (CMS – Combined Mass Spectrum).
• Identification of Similar Species:
  • Utilizes a Marker DB for identifying similar species with closely related strains.
• Machine Learning Clustering:
  • Clusters profiles of similar species groups using machine learning algorithms such as SVP (Support Vector Machine) and Random Forest.
• Marker DB Creation:
  • Stores clustering features in a Marker DB.
  • Includes positive and negative markers to enhance sensitivity and specificity.

Custom Database Builder

• Software Purpose:
  • Custom Database Building Software for user-created databases tailored to specific purposes.
• User Flexibility:
  • Allows users to create customized databases based on mass profiles obtained from experiments.
• Additional On-Premise Databases:
  • Enables users to build add-on databases on their premises in addition to the standard provided database.
• Analysis Tools:
  • Provides analysis tools, including PCA (Principal Component Analysis), Heatmap, and Dendrogram.
• Data Processing:
  • Process spectral data into database-ready format.
  • Turns MALDI-TOF data into a composite dataset for a specified sample.
• Statistical Visualization:
  • Offers various statistical visualization tools for the dataset:
    • Heatmap
    • Dendrogram
    • MS Profiling
    • PCA
• Database Generation:
  • Allows users to finalize the dataset and convert the collection of signatures into a database file.