University of Pittsburgh

Predicting drug sensitivity based on omics data with self-attention mechanism and multi-label learning

graduate student
Friday, April 5, 2019 - 1:00pm - 1:30pm

Precision oncology has achieved great success with the capability of prescribing personalized treatments targeting aberrations to an individual patient’s tumor specifically. However, there are significant limitations of current single-gene-based therapeutic indication (STI) based precision oncology. In order to overcome the limitations of STI-based approaches, combining contemporary AI methodologies and omics data is proposed to predict drug sensitivity. The model I choose utilizes multi-head self-attention mechanism to identify omics data of a cell line including gene expression, gene mutation status and somatic copy number alteration that likely determine the drug sensitivity status. This model learns a vector as an abstract representation (omics embedding) of functional impact for each omics data, then further generates an abstract representation (cell line embedding) of functional impact for each cell line, thus instantiating the states of the hidden layer.  The cell line embedding is the weighted sum of omics embedding using self-attention mechanism, and can be used as the input of a multi-layer perceptron to predict drug sensitivity of the cell line to different drugs using multi-label learning.

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