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Relevant Projects

Photo of Dvir Aran
Assistant Professor
Predicting response to therapies

Using biological data, such as single-cell and bulk RNA-seq data from patients treated with different onoclogical treatments, to train ML models that can predict response to the therapies.

Transformers-based approach to model clinical data

Clinical data is both temporal and structural. Thus, transformers can be a great way to model this kind of data. We are developing transformers-based architectures to model clinical data and use it to improve risk predictions

Approaches for integration, clustering, and annotation of single-cell RNA-seq data

We are developing semi-supervised approaches for problems that are usually treated as unsupervised.