The current members of the Network Pharmacology for Precision Medicine group.
Jing Tang

Dr. Jing Tang is a tenure-track Assistant Professor in statistics at the Faculty of Medicine and Group Leader for Network pharmacology for precision medicine. He received his PhD in Statistics from the University of Helsinki in 2009. He was a research scientist in Systems biology at the Technical Research Centre of Finland (VTT) in 2008-2011. Since 2012 he started at FIMM as a senior researcher focusing on computational network medicine. He received the prestigious ERC Starting Grant in 2016.

Johanna Eriksson

Dr. Johanna Eriksson joined the group in November of 2019 to perform the experimental validation of the pharmacology network models developed by computational methods. She received her PhD in genetics from the University of Helsinki in 2015. In her PhD and postdoctoral studies, she has focused on identifying prognostic markers and therapeutic targets for metastasized melanoma.

Research interests: gene expression profiling, biomarker discovery

Mohieddin Jafari

Dr. Mohieddin Jafari is a senior researcher working on systems pharmacology and integrating complex biological networks to develop mechanistic biomedicine. He received his Ph.D. in Applied Proteomics from Beheshti University of Medical Science in 2013. During his scholar fellowship in Proteomics Resource at Harvard School of Public Health (HSPH) and the close collaboration with Institute for research in fundamental sciences (IPM), he has got experience in both pipetting (wet-lab methods) and programming (dry-lab methods) skills. After that, He has worked as a research scientist in computational systems biology at Pasteur Institute of Iran in 2013-2018. 

Research interests: network biology, proteomics


Ziaurrehman Tanoli

Dr. Ziaurrehman Tanoli is working as senior researcher in the field of computational biology. He completed his PhD in machine learning from Pakistan Institute of Engineering and Applied Sciences (PIEAS) in 2013. During PhD, he also worked as visiting researcher at university of Helsinki for 1.5 years (topic: reclassification of ambiguous GPCRs). After PhD, he has worked as postdoctoral researcher at university of Sannio, Italy on project: ‘Identification of long noncoding RNAs using machine learning’. Later on, he joined Institute for Molecular Medicine Finland (FIMM) as postdoctoral researcher (2015-2018). 

Research interests: computational biology, machine learning, bioinformatics

Google scholar:

Ali Amiryousefi

Dr. Ali Amiryousefi is a postdoctoral researcher focusing on the statistical integration of drug response and omics data. He is holding the MSc in Bayesian statistics and decision analysis and received his PhD in Bioinformatics from the University of Helsinki. He has also worked at the THL as a statistical consultant. 

Research interests: Bayesian statistics, bioinformatics, single-cell data analysis.

Alina Malyutina

Alina Malyutina is a doctoral researcher whose main focus is on development of clinically-relevant computational and statistical methods for the rational design of drug combinations for individual cancer patients. Additionally, she is investigating molecular mechanisms underlying proteasome inhibitors' resistance in Multiple Myeloma and exploring the ways to resensitize the malignant cells to those inhibitors again. Alina received her MSc degree in Computational Engineering at the Lappeenranta University of Technology and joined FIMM as a FIMM-EMBL/HIIT PhD rotation student.

Research interests: bioinformatics, biostatistics, multi-omics & drug screening data analysis, multiple myeloma, biomarker detection

Google Scholar:

Yinyin Wang

Yinyin Wang is a post-doctoral researcher. She got her Bachelor's degree in Science of Chinese Pharmacology from the Nanjing University of Chinese Medicine in 2014. Then, she received her Master's degree from the East China University of Science and Technology in 2017. After that, she studied Bioinformatics at the University of Helsinki for four years and received her Ph.D. from the University of Helsinki in December of 2021. Her research focuses on applying network medicine and artificial intelligence (AI) methods to explore the underlying mechanism of herbal products, especially the combination of traditional medicine. She is also interested in drug respositioning and precision medicine, especially identifying novel diagnostic biomarkers and therapeutic targets by integration of multiple levels of data, such as transcript-omics, genomics, proteomics, and metabolomics.

Research interest: Network pharmacology modeling, systems medicine

Wenyu Wang

Wenyu Wang is a PhD researcher focusing on applying modern data science to biomedical questions. He started his research life doing statistical and bioinformatics analysis to omics dataset. He received his Master degree in Epidemiology and Health Statistics from Xi’an Jiaotong University in 2017. After that, he joined FIMM-EMBL international PhD programme and worked in three different computational groups before starting his PhD thesis project in NetPhar. His thesis project aims to improve drug target discoveries by integrating functional genetic and drug screening data. In addition, he is taking an active part in multiple collaborations and data science competitions relevant to his research of interest.

Research interests: Omics data integration, system biomedicine, computational statistics and machine learning

Google scholar:

Bulat Zagidullin

Bulat Zagidullin is a PhD student working on predicting the effects of drug combinations using AI-based approaches He received a BSc in Biochemical Engineering degree from the Jacobs University Bremen (JUB) in 2011. In 2017 he received his MSc in Pharmaceutical Biotechnology from the Martin Luther University Halle-Wittenberg. He is using computational statistics, machine learning and network-based methods to integrate pharmacological and molecular biology datasets. 

Research interests: AI-based models for drug discovery

Joseph Saad

Joseph Saad received his BSc in Bioinformatics from the Lebanese American University (LAU) in 2016 and his MSc in Drug Discovery and Development from the University of Turku (UTU) in 2018. He is currently a doctoral student in the fields of personalized and translational medicine, at the Institute for Molecular Medicine Finland (FIMM). His research is centered around the implementation of bioinformatics methods to identify molecular biomarkers associated with response to novel drugs and drug combinations for the treatment of hematological malignancies.

Jehad Aldahdooh

Jehad got his bachelor degree in 2013 with excellent grade in Computer Engineering from Islamic University of Gaza, Palestine. After that, he has worked at Islamic University of Gaza as a (part time) Teaching assistant. Also he has worked as a coordinator for fifth International Conference on Engineering and Sustainability (ICES5). He finished his master degree in computer science from Eötvös Loránd University-Hungary. After that, he has worked as a project researcher at University of Eastern Finland.  Currently, he is working as a project researcher and PhD student in text mining of drug-target interactions for DrugTargetCommons.

Research interests: machine learning, meta-learning, text mining, drug-target identification.

Shuyu Zheng

Shuyu Zheng is a doctoral researcher funded by the University of Helsinki Foundation focusing on predicting personalized anti-cancer drug combinations by integrating drug screening, single-cell lineage tracing, and spatial transcriptomics data.

She took her master’s degree of oncology at the Xi’an Jiaotong University in 2017 by investigating cancer radiosensitivity and the non-coding function of oncogenes. Since joining the group, she has been working on curation, analysis, and visualization of high-throughput drug combination screening data. She has updated the DrugComb data portal, synergyfinder R package and developed the SynergyFinder plus web application. In 2020, she started her study for PhD degree and now is focusing on integrating single-cell RNAseq, spatial transcriptomics, lineage tracing and drug screening data for anti-cancer drug combination discovery.

Research interest: drug combination, single-cell RNA sequencing, lineage tracing, spatial transcriptomics

Shuyu Zheng personal website

Shuyu Zheng Google scholar

Shuyu Zheng GitHub


Tolou Shadbahr

Tolou Shadbahr is a Doctoral researcher whose focus is on Data Science and application of Machine Learning methods for study and analysis of biological dataset, including multi-comics and clinical data. Tolou holds two master’s degree, one from Abo Akademi computer science program, and second one from Aalto university Life Science master. Her background is on computer science, system biology, and machine learning. Her previous works consist of application of machine learning models for drug sensitivity, drug combination prediction, prediction of patient survival status by machine learning, and imputation technique for missing values in clinical data. She is currently working on application of multi-modal machine learning methods for prognosis and diagnosis of Prostate Cancer patients.

Jie Bao

Dr. Jie Bao received her PhD in lung cancer pathology from Institute for Molecular Medicine Finland, University of Helsinki, in 2022. In her PhD study she established phenotyping methods and utilized animal models as well as clinical samples to understand the histotype-dependent heterogeneity in non-small cell lung cancer development. Currently, she wishes to continuously investigate cancer heterogeneity through integrating omic-approaches, in vitro/ex vivo drug perturbation studies and functional experiments, with a goal to search for precise solutions to combat drug resistance. Prior to coming to Finland, she has been trained with classical cell biology and neuroscience in UK and Belgium.

Research interests: cancer pathology, image analysis, drug-target profiling, cancer evolution and cell plasticity

Google scholar:

Mehdi Marzaie

Dr. Mehdi Mirzaie is a postdoctoral researcher focusing on the drug combination prediction using network-based approach. He completed his  Ph.D. theses in Applied Mathematics with focus on utilising game theory in protein fold recognition problem at Beheshti University (SBU) in Tehran. He worked as an assistant professor at Beheshti univeristy of medical sciences and Tarbiat modares university in Iran. He is also interested in network science, protein structure analysis, omics data analysis, and evolutionary game theory.

Research interests: Drug combination, protein structure,  game theory, systems biology.

Elham Gholizadeh

Elham is a research assistant with focus on drug response screening in cancer treatment using high throughput methods. She held her bachelors in cell and molecular biology and pursued her education for master’s in clinical biochemistry, to the end of 2020. Previously she has started working on combination therapy in cancer diseases to find the drug targeted proteins and the mechanism of action of drugs in treated samples.

Luping Gao

Dr. Gao completed her PhD in Medical Science from Digestive Surgery Department of Tokushima University, Japan, in October of 2021. During her PhD course, she focused on the drug resistance in Hepatocellular Carcinoma, hepatocyte-like cells differentiation from adipose derived stem cells and primary hepatocytes organoids formation.
Research interests: tumor organoids establishment, chemotherapeutic resistance, stem cells differentiation

Diogo Dias

Diogo Dias joined the group during his first year as a Master's student specializing in bioinformatics and systems medicine at the University of Helsinki after receiving his Bachelor's degree in Computer Science. He is currently working on the research and development of sensible experimental design strategies within a pharmacological framework together with computational and statistical tools to optimize cell-based cancer drug screening data analysis.

Research interests: Bioinformatics, drug sensitivity data analysis, computational biology, systems biology, mathematical modelling, and machine learning.

Denise Duma

Dr. Denise Duma is a visiting researcher broadly interested in Graph machine learning and its applications to the modeling of anti-cancer drug responses.