I am a PhD student in Bioinformatics at Karolinska Institute with experiences in Proteomics, Transcriptomics, computational biochemistry & biophysics as well as biology.
In my PhD, I mainly focus on breast cancer from the medical point of view. I study on genes & proteins that control cell cycle to understand their involvement in cancer progression.
I use several bioinformatics tools and various methods of biostatistical analysis in my project such as aCGH analysis, TMA analysis, GEX, IHC, survival analysis.
Let me give you a brief information about my experiences and my educational background.
Before I start my PhD, I attended to a Pre-PhD program at Stockholm university (Computational biochemistry/biophysics) and I involved in projects related to the protein structure homology modelling, molecular simulation techniques and protein-ligand docking methods. I worked with several algorithms/packages.(VMD, PyMol, Gromacs and DOCK)
Due to my interest of learning more about Pharmaceutical researches, I participated in two courses at Uppsala University that dealt with Chemo-bioinformatics field, listed below in my educational background.
During my master education at Lund university, I worked in proteomics research area and I introduced to protein crystallography, LC/MS mass spectrometry, peptide finger printing, chromatography and worked with algorithms used for analysing data from MS (MaxQuant,MsInspect,OpenMS). Additionally, I learnt about gene expression profiles, gene mapping, signalling pathways of tumour cells, Cancer ICAT analysis and mRNA sequence analyses from deep sequencing data sets.
I also have a background in biology (my bachelor) with more than 2 years of experience of wet-lab working. (MLPA, DNA extraction, PCR).
I’d be happy to hear from my former colleagues, managers, or creative/interesting minds, so please feel free to contact me. You can reach me on my Linkedin profile or by my email address:
Gene expression signatures and immunohistochemical subtypes add prognostic value to each other in breast cancer cohorts