Integrative Modeling of the Tumor Specific Structural Networks in Human Cancers



Integrative network modelling of the dasatinib treatment in glioblastoma stem cells
Senger, Gökçe; Tunçbağ, Nurcan; Department of Bioinformatics (2019)
Glioblastoma (GBM), the most aggressive type of the glial tumours, is thought to be widely promoted by stem-like cells. Although certain cancer types have been radically treated with Receptor Tyrosine Kinases (RTKs) inhibitors, prior studies demonstrate that treatment Glioblastoma Stem Cells (GSCs) with RTK inhibitors led to dynamic interconversion from proliferative to slow-cycling, persistent state. In this work, we use the publicly available RNA-Seq and ChIP-Seq data in naive patient-derived GBM cell lin...
Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data
Kutlay, Aysegul; Aydın Son, Yeşim (2021-09-01)
Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regul...
Ünsal Beyge, Şeyma; Tunçbağ, Nurcan; Department of Medical Informatics (2021-9-6)
Classification of cancer drugs is crucial for drug repurposing since the cost and innovation deficit make new drug development processes challenging. Heterogeneity of cancer causes drug classification purely based on known mechanism of action (MoA) and the list of target proteins to be insufficient. Multi-omic data integration is necessary for a systems biology perspective to understand molecular mechanisms and interactions between cellular entities underlying the disease. This study integrates drug-target ...
Integrative Omics Strategies for Rare Gynecological Diseases
Özcan Kabasakal, Süreyya (2022-09-18)
Quantifying the dynamics of acquired treatment resistance and evolutionary herding for the prediction of collateral sensitivity in cancer model systems
Acar, Ahmet; Nichol, Daniel; Thavasu, P; Sagastume, I; Mateos, Jm; Stubbs, M; Burke, R; Maley, C; Banerji, U; Sottoriva, A (2018-06-30)
Citation Formats
N. Tunçbağ, “Integrative Modeling of the Tumor Specific Structural Networks in Human Cancers,” 2017, Accessed: 00, 2021. [Online]. Available: