We have developed a unique algorithm with a suite of software tools to enable the use of Artificial Intelligence (AI), specifically deep learning, for sparse, or noisy data, that is typicaly found in experimental research and development environments. Our core software encapsulates a new algorithm and architecture for deep learning that allows very sparse data to be used to train new models. Currently deep learning requires large amounts of high quality data to train on to generate accurate models. This requirement is currently preventing the use of AI in areas where it could add the most value.
This new approach is being applied in a number of domains such as:
This new algorithm for deep learning can be easily used by scientific users with no detailed knowledge of how deep learning works. The core application consists of 3 key stages:
Additionally, reports are produced during client engagements with qualification of estimates indicative of uncertainty by robust and meaningful quality metrics (ie accuracy tests, Area Under the Curve (AUC), pairwise comparisons). We may also include external data that, a priori, should correlate with the target outcome in order to improve overall predictive power.
Proven applications with the following type of problems:
Our tool helps gain and maximize insights from available data, even when such data is sparse. The outputs from the tool deliver knowledge about variables' correlations and highly accurate predictions of target variables. Users define the target condition and our model predicts the rest. During our engagement with healthcare clinics, we were given datasets with confidential information about patients' medical background, treatment type, lifestyle habits etc. These clinics then specified the target treatment outcome (ie Outcome A) to be achieved by doctors. As such, a deep learning model was generated that optimized the target variable (Outcome A) and estimated what the range of values for the rest of parameters should be, and gave optimum patient pathways to achieve this outcome. During our engagement with drug discovery companies, our model predicted the likely compounds that may interact with certain target proteins, thus reducing the need to engage in expensive experimentation. The tool can be used to support professionals in each of these spaces to help guide actions, reduce the chance of errors and ultimately save costs (eg. when a new material is designed in 3 cycles instead of 10, or a drug can treat an illness more effectively)