Graphs in Colorectal Cancer Prediction

1. Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, Ph.D. PargaPondal Biomedical Researcher RNASA - Artificial Neural Networks and Adaptative Systems Group TIC - Department of Information and Communication Technologies Computational Science Faculty, University of A Coruña, Spain Web: http://miaja.tic.udc.es Email: muntisa@gmail.com; cmunteanu@udc.es

2. Outlines • Education • Networks & Graphs • Previous Work • Graphs in CRC Prediction • IBERO-NBIC • Results • Outlook Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

3. Education Sanitary - Nursery High School Hospital training and theoretical specific medical-nursery courses B.Sc. in Technological Chemistry Molecular modelling thesis: Interaction of Nucleic Acids with Minor Groove Binders M.Sc. in Applied Enzymology Molecular modelling thesis: The Influence of the DNA-Drug Interaction on the Enzymatic Activity Ph.D. in Theoretical and Computational Chemistry Quantum Chemistry thesis: Accurate Intermolecular Ground State Potentials of van der Waals Complexes Postdoctoral Researcher Complex Network, Graphs and QSAR applications Protein Folding Studies Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

4. Networks & Graphs • Network- any interconnected group or system that shares information • Graph - symbolic representation of a network and of its connectivity; it implies an abstraction of the reality so it can be simplified as a set of nodes (vertex) connected by edges (links) • Topological Indices (TIs) - any invariant numerical parameter of a graph which characterizes its topology/geometry/ structure TIs are coding information about the functions of the real network Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

5. Networks & Graphs Graph Matrices Connectivities, Node Distances, Node Degrees, Transition Probabilities Network Graph Plot Topological Indices (TIs) Protein Classification Models (by using General Discriminant Analysis, Neural Networks, Machine Learning, Evolutionary Computation etc.) Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

6. Previous Work - Prot-2S • Python Bioinformatics Web application specially devised for the Protein Secondary Structure research http://www.requimte.pt:8080/Prot-2S/ • All the calculations are based on: DSSP application, Dunbrack's Lab protein homology, user PDB lists and PDB files • Protein statistics calculations on specific 2S motifs such as AA global propensities, user defined propensities, secondary structure composition • Display the 2S motif sequences and the entire protein sequence from DSSP files • Find non-standard AA and basic AA patterns • Python / XHTML / PHP interface with Gnuplot graphical back-end on Linux Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

7. Previous Work - S2SNet • S2SNettransforms sequences of any character in Star Network Topological Indices: Shannon Entropy of Markov Matrices (Sh), Trace of connectivity matrices (Tr), Harary number (H), Wiener index (W), Gutman index (S6), Schultz index (S), Moreau-Brotoindices, Balaban distance connectivity index (J), Kier-Hall connectivity indices (0,2-5X) and Randic connectivity index (1X) wxPython Graphviz Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

8. S2SNet Features • It is a free Python application with Graphviz graphical back-end • It has a user friendly interface • Its code is portable to Linux or Mac OS X systems • It is dedicated to Star Network calculations • It can be used in many fields such as • Protein models • Mass spectroscopy • Clinical proteomics and imaging • DNA/RNA structure analysis • Linguistics studies Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

9. Graphs in CRC Prediction Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices Journal of Theoretical Biology 257 (2009) 303–311 • 189 HBC/CRC cancer proteins and 865 non-cancer proteins from experimental analysis of 13,023 genes in 11 breast and 11 colorectal cancers • Protein sequences => Star Graphs => Topological indices => Statistical methods => Classification Model (Quantitative Proteome - Disease Relationship, QPDR) • GDA - General Discriminant Analysis Method • 89.9%, 90.3% and 90.0% for the training, cross-validation and full sets • Forward Stepwise model type • 75% cases for training and 25% cases for cross-validation CRC/nCRC-score = -20.8+1.7*Tr3e+124.8*Se-Je+0.2*X2e-45.9*X5e Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

10. IBERO-NBIC • Ibero-American Network of Nano-Bio-Info-Cogno Convergent Technologies is new network funded by CYTED and has the main goal the integration of the Nano-Bio-Info-Cogno fields in Ibero-America. • The network contains 11 research groups with 84 researchers from 7 counties. It will last four years, from 2009 to 2012. • The groups • Spain (academic research): Alejandro Pazos Sierra (UDC - Universidad de A Coruña), Fernando MartínSánchez (ISCIII - Instituto de Salud Carlos III), Rosa Villa (GAB-CNM – Grupo de AplicacionesBiomedicas del Instituto de Microelectronica del Barcelona del Centro National de Microelectronica, CSIC), Victor Maojo (GIB – Universidad Politécnica de Madrid); • Chile (academic research): Fernando DaniloGonzálezNilo (CBSM – Universidad de Talca), Tomas Pérez-Acle (CBUC – Universidad Catolica de Chile); • Portugal (academic research): José Luis Oliveira (IEETA - Instituto de EngenhariaElectrónica e Telemática de Aveiro); • Brasil (research): Ana Tereza R. Vasconcelos (LABINFO/LNCC - Laboratório National de ComputaçãoCientifica); • Argentina (research): Daniel Luna (HIBA - Hospital Italiano de Buenos Aires); • Venezuela (research): RaúlIsea (IDEA - Fundación de EstudiosAvanzados); • Uruguay (private company): Álvaro Margolis (EVIMED); Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

11. Results • Book chapter: Alignment-free models in Plant Genomics: Theoretical, Experimental, and Legal issues in “Plant Genomics”, Nova Science Publishers, Inc. – accepted (February 2009) • Conference Proceedings: Data mining in complex diseases using Evolutionary Computation in Lecture Notes on Computer Science (LNCS) at International Work-Conference on Artificial Neural Networks, IWANN2009 - accepted (March 2009) • Software Description Paper: S2SNet: A Tool for Transforming Sequences into Star Network Topological Indices - sent (March 2009) • Book chapter: Markov Entropy Centrality: Chemical, Biological, Crime and Legislative Networks in “Towards an information theory of Complex Networks: Statistical Methods and Applications”, Springer Publisher - accepted (March 2009) • Software: MCeCoNet - Markov Centralities for Complex Networks - ready to register (March 2009) • My Web site: scientific projects and networks, publications/results, linked to other network webs, offers latest news about Colon Cancer, Arthritis, Schizophrenia, Personalized Medicine, Oncology, Nanotechnology, Health, Quantum World, Genetics (http://miaja.tic.udc.es). Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

12. Results - MCeCoNet • Calculates the TIs and the node centralities during a network attack • Introduces a new class of centralities based on the Markov Topological indices and node transition probabilities • *Metabolic network wxPython Graphviz-- Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es gnuplot

13. Outlook • Design and programming of software dedicated to the Complex Network/Graph analysis • Searching the important SNPs in the patients with schizophrenia and creation of diagnostic models • Diagnostic models and clinical data mining for the colorectal cancer and other types of cancer • Imaging analysis with graphs for the protein gels in rheumatoid infection • Optimization of the rule/function networks in neural network/evolutionary computation algorithms • Comparison of disease ontology networks • Coordinate scientific projects and networks • Teaching classes and Ph.D. student monitor Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, http://miaja.tic.udc.es; muntisa@gmail.com; cmunteanu@udc.es

14. Thank you! Graphs in Colorectal Cancer Prediction Cristian R. Munteanu, Ph.D. PargaPondal Biomedical Researcher RNASA - Artificial Neural Networks and Adaptative Systems Group, TIC Computational Science Faculty, University of A Coruña, Spain Web: http://miaja.tic.udc.es Email: muntisa@gmail.com; cmunteanu@udc.es