Memorial Sloan Kettering Cancer Center had one patents in big data during Q1 2024. The patent filed by Memorial Sloan Kettering Cancer Center in Q1 2024 describes a method for creating a “disease fingerprint” using biosamples and organic color center-modified carbon nanotubes. This approach involves collecting data on physicochemical interactions with a sensor array, training machine learning models to differentiate between diseases and healthy individuals, and using the models to classify patients based on nanosensor array emission data. GlobalData’s report on Memorial Sloan Kettering Cancer Center gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Memorial Sloan Kettering Cancer Center had no grants in big data as a theme in Q1 2024.
Recent Patents
Application: Machine perception nanosensor arrays and computational models for identification of spectral response signatures (Patent ID: US20240071566A1)
The patent from Memorial Sloan Kettering Cancer Center describes a method for acquiring a "disease fingerprint" using nanosensor arrays composed of organic color center-modified carbon nanotubes. The approach involves training machine learning models to differentiate between diseases and healthy individuals based on the responses of the nanosensor arrays to biological samples. The trained models can then be used to classify patients based on the emission data from the nanosensor arrays. The method includes receiving emission data, generating spectral feature changes, training machine learning models, and providing the models for classification of medical conditions in patients.
The method involves utilizing semiconducting single-walled carbon nanotubes that are covalently functionalized and encapsulated by nucleic acids in the nanosensor arrays. The machine learning model can be trained using various algorithms such as logistic regression, decision tree, artificial neural networks, random forest, or support vector machine. The patent also covers the functionalization of SWCNTs with organic color centers and encapsulation with single-strand deoxyribonucleic acid. The method further includes processing emission data from patient samples to obtain classifications for medical conditions, potentially leading to personalized treatment based on the classification provided by the machine learning model.
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