Our research group focuses on developing computational approaches to analyze high-throughput drug and genetic screens, along with multi-omics datasets, to study the fundamental regulatory mechanisms underlying cancer cell response to treatments. Working closely with experimental and clinical groups, we develop integrative approaches to address one of the most pressing challenges in cancer: drug resistance.
We aim to develop the next generation of multidisciplinary engineers who can bridge the fields of computer science, cell biology, and biomedicine.
Machine Learning | Multi-omics | Functional Genomics | Drug resistance | Cancer
Despite remarkable progress in cancer precision medicine, drug resistance and low success rates underscore the...
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New Project SARC-RON-AI
Sarcoma Clinical Data Integration with AI-Driven Automation for the National Oncology Registry
The SARC-RON-AI project aims to develop an AI-based system to process unstructured EHRs efficiently, automating...
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ELLIS Scholar
Emanuel accepted as an ELLIS Scholar
Emanuel has been accepted as an ELLIS Scholar with the ELLIS Lisbon Unit. He is...
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Synthetic Cancer Dependency Map
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
This study presents the first synthetic Cancer Dependency Map (DepMap) using a novel deep generative...
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Fulbright Scholarship
Deep learning application to combinatorial saturation mutagenesis screens
Emanuel will visit the Broad Institute of MIT and Harvard, where we will work with...
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