Computational Omics and Precision Oncology
Fighting cancer and solving the mystery of life through the power of multi-omics and data science
We are a computational group embedded in a clinical setting. We focus on developing and applying statistical and machine learning tools for mining complex biological and clinical data, in order to identify cancer biomarkers and drug targets. We work closely with physicians and clinician scientists to translate our search outputs into better patient care.
We are part of the SMART-CARE consortium, which aims to develop novel systems medicine approaches to battle cancer recurrence, using the integration of high-quality proteome and metabolome mass spectrometry data with other ‘omics and clinical data. The role of our group in the consortium is to provide innovative and robust computational solutions for mining and integrating mass-spectrometry data, in order to discover cancer biomarkers. Read more about SMART-CARE.
The Molecular Medicine Partnership Unit (MMPU) is a joint venture between the Medical Faculty of the University of Heidelberg and the European Molecular Biology Laboratory (EMBL). We are part of the Systems Medicine of Cancer Drugs MMPU group. Together with the groups of Wolfgang Huber and Sascha Dietrich, we aim to understand intra- and inter-patient heterogeneity of response to anti-cancer drugs, a major clinical and scientific challenge.
Functional Precision Medicine
Functional precision medicine is the approach that directly uses perturbation assays on primary patient samples to guide clinical decisions. We develop computational tools, in the forms of R/Bioconductor packages and Shiny apps, for streamlined processing, analyzing, and interpreting the results from functional assays, such as ex-vivo drug sensitivity assays, in order to increase their sensitivity and robustness, which are critical for clinical applications. Read a relevant study.
Multi-Omics & Machine Learning
Multi-omics integration is a powerful tool for understanding complex disease mechanisms. It also has unique advantages in clinical biomarker identification as it enables us to distinguish biological signals from incidental variation due to measurement noise or confounding experimental factors that tend to affect only individual data sources. We apply machine learning tools, such as multi-table factor analysis, for better integration and interpretation of multi-omics datasets. Read a relevant study.
Junyan Lu, Ph.D
Research Group Leader
Dr. Junyan Lu studied Computational Biology and Drug Design at the Shanghai Institute of Materia Medica, Chinese Academy of Sciences. After obtaining his PhD in 2015, Dr. Lu joined Wolfgang Huber’s group at European Molecular Biology Laboratory (EMBL) as a postdoc and later as a staff scientist, focusing on advancing personalized oncology of blood cancers through machine learning and multi-omics data integration. In December 2021, he joined University Hospital Heidelberg and is currently leading a junior research group leader within the SMART-CARE consortium.
Shubham Agrawal, M.Sc
Data Engineer / Technical Assistant
Shubham Agrawal obtained his Master’s degree in Computer Science from University of Freiburg in June 2022. He then joined Lu group on July 1st to work on the computational infrastructure for the MS data analysis pipeline in SMART-CARE.
Caroline Lohoff, M.Sc
Caroline Lohoff studied Molecular Biosciences with a focus on Systems and Computational Biology at Heidelberg University. After getting her Master’s degree in July 2022, she joined Lu group as a PhD student. Her PhD projects focus on identifying protein/metabolite-based biomarkers for tumor recurrence through mass spectrometry and multi-omics integration.
Qian-Wu Liao, M.Sc
Qian-Wu Liao obtained his Master’s degree in System Biology from Heidelberg University. He joined Lu group as a PhD student on September 1st, 2022, working on applying machine/deep learning models for identifying biomarkers for drug resistance and tumor recurrence.
Jiaojiao He, M.Sc
Jiajiao He got her Master’s degree in Bioinformatics and Systems Biology from the University of Amsterdam and Vrije Universiteit Amsterdam in August 2021. She joined Lu group as a PhD student on September 1st, 2022. Her PhD thesis focuses on developing and applying machine learning models to integrate omics and medical data for precision oncology.
Get In Touch
Im Neuenheimer Feld 130.3, Room 10.318