Predictive Cancer Classification Algorithm Based on Similar Gene-Expression Signatures

Smith Yoav, HUJI, School of Medicine - IMRIC

Genomic big data analysis enabling better diagnostics and therapeutic approach


Oncology software, cancer diagnostics, personalized medicine

Development Stage

TRL 5  Validated in relevant operation environment





  • Current cancer diagnostic` methods rely on descriptive histopathological data.
  • New technology measures patient genomic data against detailed classification of cancer types from databases of measured  tumour genomic expression profiles
  • The method monitors genetic changes enabling improved accuracy of diagnosis
  • Since molecular changes often precede morphological changes, genetic assessment of cancer patients may be used for early detection of the disease.


Our Innovation

This new tool measures the similarity between gene data derived from DNA gene tests (microarray, deep sequencing, nanostring ..) of a patients malignant tissue with sets of gene test data from pre-classified malignancies. The actual similarity distance obtained provides a powerful and sensitive diagnostic tool for each patient.


Key Features

  • Increased diagnostic and detection accuracy, reclassification into new subgroups. A clear similarity value to different clinical states.
  • Enables cancer diagnosis, prediction of clinical outcomes and formulation of therapeutic approach
  • General method may be used for any type of disease if an adequate size sample of microarrays from previously classified disease is available

Development Milestones

Seeking industry cooperation for further development with companies that can prepare datasets


The Opportunity

25 million people in Japan, Europe and North America have cancer; 10.1 million additional cases diagnosed worldwide each year. By 2020, that number will grow to 15 million new cases annually. (World Health Organization)



Researcher Information          bioinfo.md.huji.ac.il/contact.asp



Contact for more information:

Mel Larrosa
VP Business Development Healthcare
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