MOA Prediction Platform to Help With COVID-19 Research

BioSymetrics Mechanism of Action Prediction Platform framework will be put to work both in prioritizing chemical libraries and in predicting mechanism for identified compounds.

Per the three COVID-19 research initiatives announced by the SCN in the post below, we will be working with William Stanford, Amy Wong, Molly Shoichet, Stephen Juvet, Samira Mubareka, Scott Gray-Own, and Mitchel Sabloff as a component of their research.

We will also have the opportunity to screen one of our own pre-clinical leads.

Please follow us on LinkedIn where were will post updates on this project as they become available.

De-noising CMap L1000 Data

As with any assay, L1000 data is noisy. Experimental replicates (the same compound tested on the same cell line under the same conditions) often result in different levels of expression being measured. The process of de-noising the L1000 data makes it easier to see true assay response, and pick a representative concentration for each compound.

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Identifying and Addressing the Challenges in the Diagnosis of Sepsis

Sepsis is the leading cause of death in the Intensive Care Unit, and it’s responsible for 1 in 3 hospital deaths. Each hour without treatment increases a patient’s risk of death by 4-8%. Thus, early detection of sepsis is crucial for improving survival.

With the inclusion of advanced data preprocessing and machine learning, our research has been able to better predict which patients will get sepsis during their hospital stay.

In our study, we sought to develop a robust sepsis prediction model using physiological data (vital signs and lab results) from the 2019 PhysioNet Challenge. In the first phase of our analysis, we trained a recurrent neural network using long short term memory (LSTM). While the LSTM parameters themselves can be optimized in well-understood ways to produce a more accurate classifier, the impact of pre-processing parameters on sepsis prediction performance remain largely unknown.

Now entering into our second phase, we are applying Augusta™ on a patient data set consisting of ~40,000 patients with the intent of more systematically considering the impact of upstream decisions made in processing the data before training a model.

Initial results look promising.

Dishing Dirt About Clean Data

A daughter’s desire to please her parents demonstrates how a data scientist with good intentions can cause far more harm and expense in the long run, through the selection and creation of the wrong features during data pre-processing.

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‘Contingent AI’, What is it?

Machine Learning with ContingentAI
What is Contingent AI? 

In any data science pipeline there are a number of options that are selected for data processing (e.g. contrast settings for medical images, data imputation approaches, bandpass filter cut-offs for ecg signals). Typically, these options are selected manually based on previous experience of the Data Scientist or recommendations from previous, similar studies. In contingent AI, any “settable” parameter for data processing, data integration, or feature selection is permuted and the corresponding effects on the downstream predictive model measured. This process is similar to hyperparameter tuning in machine learning, however instead of optimizing only the machine learning model, the entire data science pipeline (including model selection) is subject to optimization.

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