US 12,482,566 B2
Systems and methods for predicting cancer metastasis and screening of drugs
Arnab Roy Chowdhury, Bangalore (IN); Debabani Roy Chowdhury, Bangalore (IN); Manoj Pandre, Hospet (IN); Samrat Roy, Bangalore (IN); and Sundarajan Kannan, Bangalore (IN)
Assigned to MESTASTOP SOLUTIONS PRIVATE LIMITED, Bangalore (IN)
Appl. No. 18/026,630
Filed by MESTASTOP SOLUTIONS PRIVATE LIMITED, Bangalore (IN)
PCT Filed Sep. 17, 2021, PCT No. PCT/IN2021/050915
§ 371(c)(1), (2) Date Mar. 16, 2023,
PCT Pub. No. WO2022/059026, PCT Pub. Date Mar. 24, 2022.
Claims priority of application No. 202041040890 (IN), filed on Sep. 21, 2020.
Prior Publication US 2023/0343452 A1, Oct. 26, 2023
Int. Cl. G16H 50/20 (2018.01); G01N 33/50 (2006.01); G16B 5/20 (2019.01); G16B 40/20 (2019.01)
CPC G16H 50/20 (2018.01) [G01N 33/5011 (2013.01); G16B 5/20 (2019.02); G16B 40/20 (2019.02)] 10 Claims
OG exemplary drawing
 
1. A method for training a predictive model the method comprising:
a) obtaining an initial datasets from at least one of wild type tumor cell lines, a genetically engineered tumor, a patient dataset associated with a specific tumor type from at least one of wild type tumor cell lines and genetically engineered tumor;
b) normalizing the initial dataset in accordance with the respective patient dataset;
c) generating first correlations between the initial dataset and a first output indicative of whether the tumor is a metastatic tumor, by assigning weightages to each parameter of the initial dataset using classified supervised learning and selecting an optimized set of parameters from datasets comprising-chemosensitivity, stemness, adhesion, exosome-uptake, autophagy, migration, angiogenesis, and spheroid formation;
d) mapping the first correlations by associating the initial dataset and the patient dataset with historical training data to refine the predictive model;
e) dividing the initial dataset and the patient dataset into a training set, a validation set, and a test set; and
f) iteratively training, validating, and testing, the predictive model using the training set; and
g) dynamically adjusting the first correlations based on the assigned weightages using the predictive model.