Examples of Akai Kaeru at Work in the Biotech Industry
Akai Kaeru’s Explainable AI for Biotech
Our software can quickly identify the conditions under which features become important and then uncover the causal relations that connect them. First, our pattern mining engine identifies groups of data items with shared characteristics. These patterns are then passed on to our easy-to-use interactive Visual Pattern Explorer where analysts can inspect and act upon them. Use cases in Biotech include:
Furthermore, our correlation miner will help you determine:
Identify Salient Factors in Friedreich’s Ataxia
We took a data set comprising history data of subjects with Friedreich’s ataxia. Our purpose was to identify factors that could explain variations in the progression of the disease as measured by the modified Friedreich’s Ataxia Rating Scale (mFARS). The mFARS scale is an exam-based neurological test in which a higher score indicates more advanced progression. The goal of the study was to specifically identify factors that would indicate a higher than average one year change in the mFARS scale (denoted as cMFARS). These factors could then be used during a clinical trial to select subjects, which are likely to progress at a higher rate.
We used AK Miner to identify groups of subjects for which disease progression was unusually high or low. These groups were statistically robust and defined by simple criteria (e.g., low Age at Onset or Low Baseline mFARS). We then switched to AK Visual Pattern Explorer to view and further explore this information, as seen in the figures below.
In the Probability Histogram panel (bottom center of the dashboard) we can see the distribution of cMFARS for both the selected group (green) and the full set of subjects (gray). The bottom right panel shows the summary statistics for the selected group. Finally, the Feature Importance panel on the bottom left shows the importance of the different features both globally (gray) as well as the important features for the selected group (blue). Here we can see that although there are features that are more important globally, Age (as well as Education Level, the top bar) is more important for the selected group.
This is just one example of the many patterns of interest found using Akai Kaeru’s pattern mining and pattern browsing interface. Some other useful features not covered here include exploring alternative explanations for a specific group, correlation mining (i.e., finding groups of subjects for which two attributes become highly correlated), and statistically controlling for the effects of confounding variables.