Case Studies
Problem:
We use Brainome to identify genes that are consistently over-expressed in different tumor types
The Data:
21,000 gene expressions for patients with 33 different types of cancer and a sample set of healthy patients combined into a 11,000 row, 21,000 column, 34 class table
Result:
Brainome model achieves 90% accuracy in predicting cancer type based on over expression of specific genes
Problem:
Rework of 2014 model published in Nature Communications:
The Data:
5 million observations; 18 features; 2 classes
Result:
IoT & Mobile

Problem:
Video infrastructure client needs very small models for mobile devices
The Data:
16K instances; 107 features; 2 classes
Result:
Problem:
Brainome builds three models in under 10 minutes for the Bank Telemarketing dataset provided by Satoshida Tamoto, via Kaggle. The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).
Problem:
Identify predictors of inflammatory bowel disease (IBD)
The Data:
1,627 observations; 71,266 features; 2 classes (healthy vs IBD)
Result:
*Data set analyzed using Dell XPS-13 laptop, Intel i7 CPU 3 GHz, 32 GB RAM, 2 TB SSD
Problem:
We use Brainome to classify tissue types based on RNA seq-data
The Data:
16K observations; 56K features (genes); 21 classes
Result:
*Run on 48 Core AMD Ryzen 3960X, 128 GB RAM