Santiago Schnell

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Dr. Schnell works at the interface between biophysical chemistry, mathematical and computational biology, and pathophysiology. As an independent scientist, his primary research interest is to use mathematical, computational and statistical methods to design or select optimal procedures and experiments, and to provide maximum information by analyzing biochemical data. His laboratory deals with the following topics:

(i) Development and implementation of mathematical, computational, and statistical methods to identify and characterize reaction mechanisms.

(ii) Investigate and test performance design of experiments or standards to quantify, interpret and analyze biochemical data.

(iii) Development of new algorithms and software to analyze biochemical data.

The key objective of my research is to create suitable standards and appropriate support of standards leading to reproducible results in the biochemical sciences. Reproducibility is central to scientific credibility. Meta-research has repeatedly shown that accurate reporting and sound peer-review do not by themselves guarantee the reproducibility of scientific results. One of the leading causes of poor reproducibility is limited research efforts in quantitative biology and chemometrics. In my laboratory, we are developing new ways to assess the reproducibility of quantitative findings in the biochemical sciences.

As a team scientist, Dr. Schnell’s research interest is to investigate complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole. His collaborators are primarily basic scientists who focus on the identification of molecular, biochemical or developmental mechanisms associated with diseases. To this end, Dr. Schnell’s expertise plays a central role in the identification of these mechanisms. Using mathematical and computational models, Dr. Schnell can formulate several hypothetical model mechanisms in parallel, which are compared with independent experimental data used to construct the models. The resulting comparisons are then independent between models, and any models that satisfy statistical measures of similarity will be used to make predictions, which will be tested experimentally by his collaborators. The model validated by the experiments will be considered the mechanism capable of explaining the behavior of the systems.

Issam El Naqa

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Our lab’s research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. We operate at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease. These research interests are divided into the following themes:

  • Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (-omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
  • Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
  • Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.
Machine Learning in Radiation Oncology: Theory and Applications

Machine Learning in Radiation Oncology: Theory and Applications