Modern molecular biology measurement techniques such as microarrays, sequencing and mass-spectrometry produce large amounts of data and are often applied to large sets of samples. To interpret these data scientists apply statistics and data mining techniques that are tuned to identify certain structures in the data and to assess their statistical significance. Personalized medicine is largely driven by findings that stem from a combination of high throughput measurement and effective data analysis. I will describe measurement techniques and data analysis approaches developed or adapted to study the results they produce. I will discuss applications and findings in the context of cancer and of understanding molecular regulation.
Dr Zohar Yakhini is Master Scientist at Agilent Laboratories and Adjunct Faculty in the Computer Science Department, Technion, Haifa. Dr Yakhini leads a group of computational biologists working on information aspects of genomics, proteomics and glycomics. He earned a BSc in mathematics and computer science at the Hebrew University in Jerusalem and a PhD in mathematics at Stanford University, 1997. He is working in computational biology and bioinformatics since after graduation, focusing on statistical and algorithmic aspects of high throughput measurement technologies and synthetic biology. Dr Yakhini led data analysis in several early gene expression studies and then led the development of probe design and data analysis methods and software tools, for Agilent’s aCGH microarray platform. Dr Yakhini’s group developed several data analysis tools that are widely used by the genomics community, including differential expression and statistical enrichment analysis tools, such as GOrilla.