DATA SOURCES

Our database is anchored in the vast reservoir of information from PharmGKB, the premier repository aggregating recommendations from pharmacogenetic consortiums (CPIC, DPWG, CPNDS, RNPGx) and state regulators (FDA, EMA, Swissmedic), supplemented with other pharmacogenetic research findings. Additionally, our own groundbreaking research findings enrich this database.

DATA PREPERATION

Before delving into analysis, we meticulously preprocess the data. This involves encoding categorical variables and partitioning the data into training and test sets.

NEURAL NETWORKS

We employ a deep neural network, crafted using the Keras library. Comprising multiple layers, this network "learns" from the data provided. Its essence lies in predicting the most effective and safe medication for a patient based on their genetic information.

TESTING AND EVALUATION

Post-training, we assess the model's performance on the test dataset. Various metrics, such as the ROC curve and PR curve, are employed to ensure the precision of our predictions.

FEATURE IMPORTANCE ANALYSIS

Leveraging the random forest algorithm, we discern which genetic markers are paramount in drug selection.

REPORT GENERATION

We offer customized pharmacogenetic report designs for labs and hospitals, tailored to their unique specifications. Our reports are clear, concise, and integrate smoothly into clinical workflows. Explore our design excellence with a Sample Report.