|
September 16, 2010
|
|
15:00-16:10
|
Tutorial: M. Cannataro
Management and analysis of protein-to-Protein Interaction (PPI) data
The tutorial describes main aspects of Interactomics starting from technologies for data generation, databases for data storage, standards for data modelling, and methods and techniques for data analysis and knowledge extraction.
Interactomics is a new discipline in the “omics” world that focuses on the modeling, storage and retrieval of protein-to-protein interactions (PPI), as well as on algorithms for analysing and predicting interactions.
Different protein functions are performed when proteins interact each others. Interactions may involve two or more proteins and be differently stable along the time, e.g. by forming a protein complex.
Wet lab technologies allow both to find binary interactions (I.e. involving only two proteins) as well as multiple interactions (e.g. a protein complex).
PPIs are often stored in specialized databases where each binary interaction is represented by a couple of interacting proteins (Pi, Pj).
The set of all protein-to-protein interactions happening in an organism is represented by a graph said protein-to-protein interaction network (PIN). The nodes of PINs, i.e. the proteins, represent biological entities, while the edges represent the interactions among them.
The set of all interactions occurring in an organism, i.e. its PIN, is obtained by extracting all interaction (Pi, Pj) contained in a given PPI database, i.e. the edges, and by building the related graph.
Thus, from the computer side point of view, Interactomics regards the generation of PPI data, their storage and querying through PPI databases and finally the analysis of the graphs representing PINs.
The tutorial discusses technologies, standards and databases for generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs, Moreover, some case studies and applications of PINs are also discussed.
|
| 16:10-16:40 |
Coffee Break |
|
16:40– 17:50
|
Tutorial: A. Floares
RODES - a class of algorithms for reverse engineering drug gene regulatory networks
Mathematical modeling is essential for understanding and controlling gene networks by drugs or genes replacements. Various formalisms, such as Bayesian networks, Boolean networks, differential equation models, qualitative differential equations, stochastic equations, and rule-based systems, have been used. The ordinary differential equations (ODE) approach tries to elucidate a deeper understanding of the exact nature of the regulatory circuits and their regulation mechanisms. RODES (Reversing Ordinary Differential Equations Systems) algorithms decouple the systems of differential equations, reducing the problem to that of reverse engineering individual algebraic equations. It automatically identifies the structure of accurate ODE systems models of gene regulatory network and drug gene regulatory network, estimate their parameters and the biochemical and pharmacological mechanisms involved. RODES algorithm reduces the complexity of the problem, and the execution time, due to the fact that for evaluating the fitness function is not necessary to integrate the ODE system. It is also able to deal with the common situations of information as variables missing from data. Applied to drug gene networks the neural network version of RODES algorithm enable and automate the reconstruction of the time-series of the transcription factors, microRNAs, or drug related compounds which are usually missing in microarray experiments.
|
SOCIAL EVENT: CONCERT IN SAN GIOVANNI DECOLLATO CHURCH
|
| September 17, 2010 |
|
09:00–09:15
|
Welcome message from President of the Provincia di Palermo G. Avanti
Welcome message from Prof. Salvatore Gaglio, University of Palermo
|
|
09:15–10:30
|
Invited Talk: Raffaele Giancarlo
Title:The Three Steps of Clustering in the Post-Genomic Era*
*Joint work with G. Lo Bosco, L. Pinello and F. Utro
Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. It can be summarized as a three step process:
(a) Choice of a Distance Function;
(b) Choice of a Clustering Algorithm;
c) Choice of a Validation method.
Although such a purist approach to Clustering is hardly seen in many areas of Science, genomic data require that level of attention if inferences made from Cluster Analysis have to be of some relevance to Biomedical research. Unfortunately, the high dimensionality of the data and their noisy nature makes Cluster Analysis of genomic data particularly difficult. In this talk, the state of the art on the subject will be presented, discussing specific limitations of the steps involved in Clustering and possible ways to make progress.
|
|
10:30-11:00
|
Coffee Break |
|
11:00–12:15
|
Session 1 : Sequence analysis, promoter analysis and identification of transcription factor binding sites.
Osmoprotectants in the Sugarcane (Saccharum spp.) Transcriptome revealed by in silico evaluation, P. Barros dos Santos, N.M. Soares-Cavalcanti, G.S. Vieira-de-Mello, A.M. Benko-Iseppon.
IP6K gene identification in plant cells via tag discovery, F. Fassetti, O. Leone,L. Palopoli, S. E. Rombo, A. Saiardi
Early Nodulins in the Sugarcane Transcriptome: Characterization and Expression Pattern Revealed by In Silico Analysis, G. Souto Vieira-de-Mello, P. Barros dos Santos, N.d.M. Soares-Cavalcanti, A.M. Benko-Iseppon
|
|
12:15–13:30
|
Session 2: Methods for the unsupervised analysis, validation and visualization of structures discovered in bio-molecular data -- Prediction of secondary and tertiary protein structures
An interactive method of anatomical segmentation and gene expression estimation for an experimental mouse brain slice A. Osokin, D. Vetrov, A Lebedev, V. Galatenko, D. Kropotov
Prediction of cysteine bonding state with machine-learning methods, C. Savojardo, P. Fariselli, P.L. Martelli, P. Shukla, R. Casadio
(**)Mathematical modelling and simulation of biological systems
(**) Supervised classification methods for mining cell differences as depicted by Raman spectroscopy, P. Xanthopoulos, R. De Asmudis, M. Guarracino, G. Pyrgiotakis, P. Pardalos
|
|
13:30-15:00
|
Lunch Break
|
|
15:00-16:15
|
Invited Talk: Lisboa Paulo
The continuum from bioinformatics to biostatistics*
*Joint work with D. Bacciu, I.H. Jarman, T.A. Etchells, S.J. Chambers, J. Whittaker and J. Garibaldi
The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and identifying therapeutic targets. This paper will highlight the need for multidisciplinary research across computational intelligence methods and traditional statistics, by reference to a data set of cytometric protein expression markers for breast cancer. In particular, it will focus on the interplay between robust clustering, visualization by dimensionality reduction and modeling with directed acyclic graphs.
|
|
16:15-16:40
|
Coffee Break |
| 16:40-17:00 |
Panel and Discussion: INNS and IEEE-CIS Perpectives in Bioinformatics and Biostatistics.
|
|
17:00-18:15
|
Session 3: Gene expression data analysis
Simultaneous Clustering and Gene Ranking: A Multiobjective Genetic Approach, K.C. Mondal, A. Mukhopadhyay, U. Maulik, S. Bandhyapadhyay, N. Pasquire
Use of biplots and Partial Least Squares regression in microarray data analysis for assessing association between genes involved in different biological pathways, N. Bassani, F. Ambrogi, D. Coradini, E. Biganzoli
Qualitative Reasoning on systematic gene perturbation experiments, F. Sambo, B. Di Camillo
|
|
18:15-19:05
|
Session 4: Gene expression data analysis
Biclustering by resampling, E. Nosova, R. Tagliaferri, F. Masulli, S.Rovetta
Labeling Negative Examples in Supervised Learning of Unlabeled Gene Regulatory Connections, L. Cerulo, V. Paduano, P. Zoppoli, M. Ceccarelli
|
SOCIAL DINNER AT THE ROOF GARDEN OF THE GRANDE ALBERGO SOLE
|
| . |
| September 18, 2010 |
|
09:15–10:30
|
Invited Talk: Gianluca Pollastri
De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks
Knowledge of the subcellular location of a protein provides valuable information about its function and possible interaction with other proteins. In the post-genomic era, fast and accurate predictors of subcellular location are required if this abundance of sequence data is to be fully exploited.
We have developed a subcellular location predictor (SCL_pred) using high throughput machine learning models trained on large non-redundant sets of protein sequences.
The algorithm powering SCL_pred is a new Neural Network (N-to-1 Neural Network, or N1-NN) which is capable of mapping whole sequences into single properties (a functional class, in this work) without resorting to predefined transformations, but rather by adaptively compressing the sequence into a hidden feature vector.
I will describe the model, and report on extensive benchmarking of SCL_pred against other state-of-the-art predictors of subcellular location. The results are favourable, moreover the N1-NN algorithm is fully general and may be applied to a host of problems of similar shape, that is, in which a whole sequence needs to be mapped into a fixed-size array of properties. The adaptive compression operated by N1-NN may even shed light on the space of protein sequences. |
|
10:30-11:00
|
Coffee Break |
|
11:00–12:15
|
Special Session: Data Clustering
COMPARISON OF CORRELATION–BASED DISSIMILARITY MEASURES FOR CLUSTERING GENES WITH DAG STRUCTURE F. Marta L. Di Lascio, Alberto Roverato
THE USE OF THE JOINT DISTRIBUTION OF SUMS OF SUCCESS AND FAILURE RUNS FOR DISCRMINATING MEMBRANE PROTEINS Bersimis, S., Bagos, P.G.
APPLYING GAP STATISTIC FOR AUTOMATED UNSUPERVISED ESTIMATION OF OPTIMAL NUMBER OF CLUSTERS IN MICROARRAY DATASETS Panagiotis Moulos, Ilias Maglogiannis,Aristotelis Chatziioannou
|
| 12:15–13:30 |
Session 5: Bio-medical text mining and imaging -- Methods for diagnosis and prognosis
A Multi-relational Learning Framework to support Biomedical Applications, T.M.A. Basile, F. Esposito, L. Caponetti
Data drivengeneration of fuzzy systems: an application to breast cancer detection, A. d'Acierno, G. De Pietro, M. Esposito
A Knowledge Based Decision Support System for Bioinformatics and System Biology, A. Fiannaca, S. Gaglio, M. La Rosa, D. Peri, R. Rizzo, A. Urso
|
|
13:30-15:00
|
Lunch Break |
|
15:00-16:15
|
Session 6: Mathematical modelling and simulation of biological systems
Dynamic simulations of pathways downstream of ErbB-family. Exploration of parameter space and effects of its variation on network behavior, L. Tortolina, N. Castagnino, C. De Ambrosi, R. Pesenti, F. Patrone
ROBUSTNESS ANALYSIS OF A LINEAR DYNAMICAL MODEL OF THE DROSOPHILA GENE EXPRESSION, A. Haye, J. Albert, M. Rooman
|
|
16:15-17:30
|
Special Session: i.CDSS
Gaussian Processes for Classification on Cancer and MicroRNA Datasets. Comparison with Support Vector Machines Calin-Rares¸ Turliuc, Liviu Ciortuz
Intelligent Clinical Decision Support Systems For Non-Invasive Bladder Cancer Prognosis Alexandru G. Floares, , Carmen Floares, Oana Vermesan, Tiberiu Popa, Michael Williams, Sulaimon Ajibode,Liu Chang-Gong, Diao Lixia, Wang Jing, Traila Nicola, David Jackson, Colin Dinney, Liana Adam
Automatic Unsupervised Segmentation of Retinal Vessels using Self-organizing Maps and K-means clustering
Carmen Alina Lupascu Domenico Tegolo
Classification Of Clinical Gene-Sample-Time Microarray Expression Data Via Tensor Decomposition Methods,
Yifeng Li, Alioune Ngom
Metaheuristics for Maximum Likelihood Regression Analysis, Leif E. Peterson
|
|
Technical Program