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Clinical

Sysoft® Corporation develops, integrates, and implements big data software tools, techniques and methodologies. Sysoft’s integrated software framework seamlessly integrates RStudio, Python, Matlab, and SAS for translational bioinformatics, data driven medicine, biotechnology application, biochemical circuits, and clinical image processing. In addition it also integrates front end visualization tools like Tableau, Qlik, Microsoft BI, and SAS Visual Studio for interactive visualization.
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Typical applications include:
  1. Mining medical billing records, EHR and other records for pattern recognition, readmission studies, drug-drug interaction studies and more.
  2. Advanced Dicom and unstructured text mining for Radiology application.
  3. Image feature extractions and application of advanced deep learning algorithms for feature recognition and validation.
  4. GWAS and eQTL studies for translational bioinformatics applications.
  5. Diagnostics: genomics, subtyping to develop markers
  6. Diagnostics in rare disease space
  7. Prognostics in cancer: Gene Expression
  1. 8. Application of clustering algorithms on gene expression levels to detect leukemia subtypes
  2. 9. Survival analysis involving integration of clinical data as well as gene expression data.
  3. 10.Advanced Gene expression analysis: Medical focus: Leukemia -applying supervised algorithms to microarrays for medical diagnostics, recapitulating pathological classification schema.
  4. 11. Advanced Gene expression analysis: Medical focus: B-cell lymphoma. Applying unsupervised algorithms to microarrays for disease discovery: new disease sub-types.
  5. Unsupervised algorithms: clustering, self-organizing maps, Principal components analysis, Cross-validation, permutation testing, q-values
  6. 12. Advanced Gene expression analysis: Medical focus: Circadian rhythms. Time series analysis and dynamics, determining involvement of pathways, getting from the list of genes to the
  7. story.
  8. 13. Proteomics and metabolomics: Medical focus: Ovarian cancer. Finding biomarkers for diseases for which none exist - different spectroscopic methods, data produced by each method,
  9. Protein identification, quantitation, function.
  10. 14. Polymorphisms: Medical focus: diabetes. polymorphisms are associated with insulin resistance syndromes, designs for genetic analysis, Calculating linkage disequilibrium, Haplotypes,
  11. Complex traits, Bayesian networks: Discretization,Metabolic flux analysis
  12. 15. Tools for drug discovery: QSAR, DOCK.
  13. 16. Data Driven Medicine.