SHOVAL LAGZIEL

Ph.D. candidate,

Machine Learning  & Computational Biology

ABOUT

I am a Ph.D. candidate in the Computer Science Faculty at the Technion (Haifa, Israel), under the supervision of Prof. Tomer Shlomi. I am passionate about cancer research, aiming to combine high-throughput data analysis with dedicated algorithms and machine learning approaches in order to reveal cancer mechanisms for personalized treatment and diagnosis.

In the Shlomi lab, we study how cellular metabolism is rewired in cancer cells and how this can be exploited for cancer diagnosis and treatment. Towards this end, we combine quantitative experimental approaches with computational modeling.

In my research, I work on the development of computational methods for cancer genomics and metabolomics analysis, using publicly available genomics and metabolomics data or using mass-spectrometry-based metabolomics data generated in our lab. Utilizing machine-learning approaches, we aim to develop fast and sensitive early cancer diagnosis methods and to understand cancer cell mechanisms following gene perturbations or drug treatments.

Learn more about my work by checking out my current projects and past publications.

MY RESEARCH

Unlocking Cancer’s Mysteries

UTILIZING PUBLICLY AVAILABLE CANCER GENOMICS DATA TO INFER METABOLIC DYSFUNCTION

Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. 

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EXPLORING DRUG 

MECHANISMS OF ACTION

Using an integrated experimental-computational approach, we are working on understanding the metabolic phenotype of cancer cells following drug treatments.

METABOLOMICS FOR POPULATION WIDE DIAGNOSIS

We are working on a high-throughput mass-spectrometry based analysis which is cost-effective, fast and sensitive in order to use it for population-wide disease diagnosis. As a first step, optimizations of the analytical platform and the computational pipeline were made. Collaborations with a healthcare provider and hospitals are currently in progress.  

Stay tuned :)

PUBLICATIONS