Andrei Zinovyev Be realistic, demand the impossible! 
PhD in Computer Science: My formal speciality (as written in my Russian state PhD diploma): "Mathematical and software tools for computers, computational complexes and computer networks". My university diploma was in theoretical physics (cosmology). Since 2000 I am working in Bioinformatics and Systems Biology. 
Current occupation: Researcher at the Bioinformatics, biostatistics, epidemiology and computational systems biology of cancer unit of Institut Curie, Paris Coordinator of Computational Systems Biology of Cancer team was involved in the project Functional Genomics, past member of Systems Epigenomics Group team 
Research interests:
My citations in Google Scholar My repositories in GitHub 
See the wordcloud of my publications in PUBMED: 
The wordcloud of this page: 
Computational Systems Biology of Cancer (Amazon link) by Emmanuel Barillot, Laurence Calzone, Philippe Hupe, JeanPhilippe Vert and Andrei Zinovyev CRC Press Inc, Chapman & Hall/CRC Mathematical & Computational Biology, 2012. p. 452 "This is the first book specifically focused on computational systems biology of cancer with coherent and proper vision on how to tackle this formidable challenge..." (c) Hiroaki Kitano The book is accompanied by a website containing additional materias 
Principal Manifolds for Data Visualization and Dimension Reduction (Alexander Gorban, Balázs Kégl, Donald Wunch, Andrei Zinovyev (eds.))
(Amazon link, whole book text)
Lecture Notes in Computational Science and Engineering, Vol. 58 Springer, November 2007, 340 pages In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and Kmeans decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics. This book is accompanied by highly recommended site pca.narod.ru (in Russian) Together with editing, in this book I contributed to writing three chapters:

Visualization of Multidimensional Data (In Russian) (
PDF
) by Andrei Zinovyev Krasnoyarsk Technical State University Press, 2000. Method of multidimensional data visualization: theory, applications and software. This is a literature version of my PhD thesis, written 1 year before the thesis. 
Coping with Complexity: Model Reduction and Data Analysis Research Workshop, Ambleside, Lake District, UK, August 31 – September 4, 2009 
Modeling cell life and cell death in cancer (PDF) 

How much noncoding DNA do eukaryotes require? (PDF) 

Hierarchical cluster structures and symmetries in genomic sequences (PPT) 

Invariant manifolds for reaction kinetics (PPT) 

Nonlinear Principal Manifolds  elastic maps approach (animated PPT) 

ACE&RACE: annotation of complex/combinatorial expressions
(PPT) 

Doubling wood harvest with tree group planting (PPT) 