Nooshin O.
Senior Computational Biologist
Experience
Senior Computational Biologist
Max-Planck-Institute for Molecular Genetics
- Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
- Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
- Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
- Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.
Senior Computational Biologist
Charité - Universitätsmedizin Berlin
- Applied metric learning approaches to identify protein signatures distinguishing presymptomatic sepsis from general inflammation.
- Developed proteomic signatures to predict therapeutic response and resistance in oncology patients.
- Conducted survivability analyses integrating clinical and proteomic datasets.
- Performed large-scale mass spectrometry data analysis, supporting biomarker discovery.
- Investigated molecular mechanisms underlying antifungal drug resistance through computational modeling and proteomics.
Senior Computational Biologist/Machine Learning Scientist
Eagle Genomics
- Designed and implemented scalable bioinformatics pipelines (Nextflow, Python, R) for microbiome and multi-omics analysis in production environments.
- Led integration of microbial interaction networks and host–microbiome analyses.
- Served as subject matter expert (SME) in cross-functional product development teams.
- Contributed to two product launches, ensuring reproducibility and compliance with CI/CD practices.
Computational Biology Scientist
Max-Planck Institute of Molecular Plant Physiology
- Mentored students in various research capacities.
- Used machine learning approaches to predict the interactome of different species and used different approaches for model selection.
- Inferred gene regulatory networks (context-agnostic as well as context-specific).
- Analyzed different omics data sets, including transcript expression from microarray and RNA-Seq technologies, protein abundances, and metabolite levels, and integrated them.
- Worked with Git and GitHub.
- Worked with Weka machine learning tool.
- Applied advanced analysis methods including network analysis, time series data analysis, segmentation of time series data sets, identification of mechanisms of action, phylogenetic analysis, and quantification of heritability from population genetics studies.
- Assisted Ph.D. students in writing papers, theses, and structuring presentations.
Data Engineer
LifeGlimmer GmbH
- Performed extensive text mining and created a database of all gene regulatory interaction networks related to NF-κB from publications.
- Trained a colleague to perform classical and advanced network analysis on transcription data.
- Used R for all programming steps.
Researcher
Institute of Biochemistry and Biology, Department of Molecular Biology, University of Potsdam
- Started programming with R.
- Used Python, Perl, and SQL as well as text mining functions to create and annotate all available gene expression data sets in GEO to identify genes involved in signal crosstalk.
- Conducted classical computational analyses of these data sets, including differential behavior, clustering, and enrichment analysis.
Summary
Computational Biologist (Ph.D.) with interdisciplinary experience at Charité, Eagle Genomics, and the Max Planck Institute for Molecular Genetics. Expertise in multi-omics data integration, proteomics, and AI-driven modeling for translational and predictive applications. At Charité, led projects applying machine learning and mass spectrometry to identify proteomic signatures for early sepsis detection, predict therapy response and resistance in oncology, and characterize antifungal drug resistance. At Eagle Genomics, developed scalable and reproducible bioinformatics pipelines (Nextflow, Python, R) for microbiome and multi-omics data analysis in production environments. Currently at the Max Planck Institute, advancing the use of artificial intelligence and deep learning in proteomics, teaching AI methodologies, and developing data-driven models to explore biological mechanisms.
Skilled in predictive modeling, data fusion, statistical analysis, and interdisciplinary collaboration. Passionate about leveraging AI and computational modeling to transform complex biological systems into actionable insights for healthcare, biotechnology, and digital-twin research.
Languages
Education
Max-Planck Institute of Molecular Plant Physiology - Institute of Biochemistry and Biology, Department of Molecular Biology,
Ph.D. · Systems Biology · Potsdam, Germany
Shiraz University
M.Sc. · Software Engineering · Shiraz, Iran, Islamic Republic of
Shiraz University
B.Sc. · Software Engineering · Shiraz, Iran, Islamic Republic of
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