Front Matter

• Venue: Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
• Year: 2009

Pisa is the town where Computer Science was born in Italy, and where many prestigious institutions in this field are located, starting from the Department of Computer Science of its University on to the Institute of Information Science and Technologies of the Italian National Research Council. From the Seventies, there has been a hive of activity around the outstanding figure of Professor Antonina Starita (or Tonina for most of us), aimed at the development of models of information processing based on biological metaphors and at their applications in Biomedicine, involving master and doctoral students in Computer Science in Pisa, her Italian and European colleagues and their students. These activities, at the beginning of the seventies, were still known as Cybernetics. They have then taken different names, according to the main perspective adopted, such as Bioengineering, Neural Networks, Fuzzy Logic, and Evolutionary Computation. More recently, they have one again been reunited under almost equivalent denominations, such as Soft Computing, Computational Intelligence, and Natural Computing. Prof. Starita was born on January 31st in 1939. She studied Physics in Naples with Prof. Eduardo Caianiello, and then moved to Pisa as a researcher at the National Research ouncil, before becoming Associate Professor of Bioengineering and then Professor of Computer Science at the University of Pisa. After a long illness, Tonina left us on August 4, 2008. Nowadays, the seminal activity of Tonina continues in the different fields of Computational Intelligence, Machine Learning and their applications to innovative interdisciplinary fields of research, through the “Computational Intelligence & Machine Learning Group” (http://www.di.unipi.it/groups/ciml) and the “Neurolab” founded by her at the Department of Computer Science of Pisa and through her alumni and collaborators outside of Pisa. This volume, presented during a symposium in memory of Antonina Starita which was held in Pisa at the Department of Computer Science, is to be a witness as well as a big thanks to this extraordinary researcher and guide from her former students and from those who have had the privilege of meeting her in the path of their lives and to collaborate scientifically with her. It gathers scientific contributions by Tonina's alumni and main collaborators in the three main areas where Tonina was most active in the last period of her research activity: Clustering and Learning Applications, Biomedical Applications, and Motor Control and Evaluation. The Clustering and Learning Applications part opens with a contribution by D.A. Ingaramo, M.L. Errecalde, L.C. Cagnina, and P. Rosso, concerning the clustering of short-text corpora by Particle Swarm Optimization (PSO). Short-text collections are becoming more and more frequent due to the recent development of new communication modalities, e.g. blogs, text messaging, snippets, etc., and thus it is important to develop computational tools for dealing with them. The contribution shows how PSO-based approaches can be highly competitive alternatives for clustering short-text corpora. Tonina was fascinated by PSO due to its evocation of computational mechanisms embedded in nature, and herself contributed to explore the application of this approach, e.g. to the alignment of medical images [1]. The second contribution in this part, by D. Sona, E. Olivetti, P. Avesani, and S. Veeramachaneni, investigates the use of Neural Networks (NN), and specifically of Recurrent NN, to interpret brain images obtained by functional Magnetic Resonance Imaging (fMRI). This technology aims to map the cognitive states of a human subject to specific functional areas of the brain. Unfortunately, the interpretation of fMRI images is not easy, and computational tools able to help in such a task are highly desirable. The authors of the contribution disclose how by using Recurrent Neural Networks they were able to win the Pittsburgh Brain Activity Interpretation Contest, edition 2006, consisting of a brain decoding problem based on free design protocol of stimuli. Tonina was very interested in Neural Networks, and in particular to the kind of models exploited by the above mentioned contribution. In fact, she contributed to the definition of a class of Neural Network models, the Recursive Neural Networks, which are generalization of Recurrent Neural Networks to the treatment of structured input, such as trees and acyclic graphs, e.g. [2]. This class of Neural Network models is the subject of the third contribution, where A. Micheli, C. Bertinetto, C. Duce, R. Solaro, and M.R. Tiné report on the latest advances on an innovative cheminformatics approach based on them which helps into the development of new molecules and materials of biomedical interest. The contribution presents a comprehensive survey of the results obtained on acrylic and methacrylic polymers, including statistical copolymers. Previous pioneering results in the field of Cheminformatics were introduced with the contribute of Tonina, e.g. [3]. The first part of the book is closed by F. Aiolli and A. Ciula. In their contribution, they describe the System for Paleographic Inspections (SPI) software suite developed at the University of Pisa thanks also to Tonina's contribution [4]. The goal of the SPI system, based on Tangent Distance and related learning methods, is to help in dating and localizing books produced by hand through the analysis of images of their ancient scripts. The authors discuss how the system has been used by paleographers in their attempts to classify and identify scripts, and how SPI can be improved further to meet the research needs of paleographers. The SPI system is another concrete example of Tonina's attitude to put innovative computational approaches at the service of other disciplines. Tonina also contributed to the development of new learning models using Tangent Distance [5,6]. The second part of this book is devoted to contributions in the medical and biological areas. These areas constituted Tonina's main research interest and much of her body of work is devoted to the definition of automatic, efficient and effective computational tools for addressing problems in these areas, with a focus on Computational Intelligent methods and techniques (e.g. [7--10]) as well as more traditional Artificial Intelligence tools, such as Expert Systems (e.g. [11]). The contribution by G.C. Manikis, M.G. Kounelakis, and M. Zervakis addresses the prediction of response to induction treatment in Acute Myeloid Leukemia (AML). Different supervised learning techniques are benchmarked and evaluated and most significant indicators that contribute to the improvement of diagnosis are examined. A further contribution to the study of the effects of treatment in AML is given by P.J.G. Lisboa, I.H. Jarman, T.A. Etchells, F. Ambrogi, I. Ardoino, M. Vignetti, and E. Biganzoli. Since AML may require aggressive systemic treatment, it is important to characterize quantitatively the response to treatment. To this aim a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD) is applied to a significant cohort of patients diagnosed with AML “de novo” and treated according to a strict protocol defined by the “Gruppo Italiano Malattie EMatologiche dell'Adulto” (GIMEMA) in order to follow the disease progression. M. Pelosini, F. Baronti, and M. Petrini investigate the application of a partitioning recursive algorithm, known as Hypothesis testing Classifier System algorithm (HCS), for the characterization of patients affected by Non Hodgkin Lymphomas (NHL). The aim of the study is to discover features potentially useful to detect patients' subsets with different clinical behavior and prognosis, so to be able to personalize the treatment. In addition to that, multi-objective analysis is proposed as a tool to assess follow up schedules. The use of Bayesian models to distinguish between benign and adnexal ovarian tumors is the topic of the contribution by B. Van Calster, O. Gevaert, C. Van Holsbeke, B. De Moor, S. Van Huffel, and D. Timmerman. The authors describe the results obtained by the many projects supported by the International Ovarian Tumor Analysis (IOTA) study group. The aim of these projects was to explore advanced mathematical modeling options for ovarian tumor diagnosis through interdisciplinary collaborations involving clinicians, statisticians, and engineers. The study reported by P. Aretini and G. Bevilacqua focuses on the problem to automate the analysis of FISH images. A novel automated system developed by the Aristotle University of Thessaloniki with this aim is used for the evaluation of HER2 status in breast cancer cases, obtaining improved results with respect to semi-automated analysis, which has the drawback of requiring substantial user intervention. The contribution by L. Fiaschi, J.M. Garibaldi, and N. Krasnogor investigates the possibility of discovering a correlation between variations of the individual nucleotides in DNA (Single Nucleotide Polymorphism) of a person and his/her response to drug therapy or personal susceptibility or resistance to a certain disease. Specifically, the contribution exploits the Transmission Disequilibrium Test (TDT) to perform a multiple-test analysis of SNPs for the assessment of susceptibility to pre-eclampsia. Combinations of SNPs of interest with respect to pre-eclampsia are identified. The closing contribution of the second part reports on how new information and communications technologies, such as Computational Intelligence algorithms and tools for biodata analysis, grid computing and web services and clinical user interfaces, can be exploited to create a knowledge infrastructure to support personalised care for Alzheimer's disease (AD). The authors, E. Ifeachor, P. Hu, L. Sun, N. Hudson and M. Zervakis report the results obtained within the EU-funded project BIOPATTERN, to which Tonina actively participated. The contribution provides highlights and insights into the requirements and challenges of personalized care for AD, the characteristic features and requirements of bioprofiles within the context of AD, techniques for the acquisition of useful parameters for inclusion in the bioprofile for AD, and a grid-based prototype system to demonstrate the concepts of bioprofiling for AD within the EU setting. The third part of the book covers a topic to which Tonina devoted a relevant portion of her research time: motor control and evaluation. Motor control was studied by Tonina in the context of Robotics (e.g. [12,13]), while motor evaluation for medical applications was the research subject of many of Tonina's projects (e.g. [14]). The first contribution is by P. Morasso, V. Mohan, G. Metta, G. Sandini. They describe a method of motion planning that is based on an artificial potential field approach (Passive Motion Paradigm) combined with terminal-attractor dynamics. Besides holding interesting computational characteristics, the proposed approach addresses in a satisfactory way a feature that is crucial for complex motion patterns in humanoid robots, such as bimanual coordination or interference avoidance: precise control of the reaching time. The second and last contribution of this part by A. Cappozzo, V. Camomilla, U. Della Croce, C. Mazzà, and G. Vannozzi, reports on the results obtained over a decade of work within the VAMA (Italian acronym for “evaluation of motor ability in the elderly”) project. The objective of VAMA was to devise, through a biomechanical analysis, quantitative methods for assessing the locomotor functional limitation of a given individual and, as a further step, to investigate the relationship between relevant impairments and disability. The contribution describes the steps constituting the successful methodology originated from this research. The book is closed by a contribution covering an additional dimension of Tonina research activity, i.e. her effort in trying to support as much as possible the transfer of successful research products to the market, so that the quality of life of everybody could be improved in a significant way. The author of the contribution, D. Majidi, using a very personal perspective, reports on how Tonina supported Majidi's journey from her safe and comfortable lab to the difficult, and sometimes “dangerous”, world of business. We completely agree with Majidi's statement about Tonina: “Everybody who knew her, appreciated her wisdom, her creativity, her love for life and her love for research. This article is for Tonina.” This book is for Tonina. Francesco Masulli, Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, Italy Alessio Micheli, Dipartimento di Informatica, Università di Pisa, Italy Alessandro Sperduti, Dipartimento di Matematica Pura ed Applicata, Università di Padova, Italy Acknowledgment The editors would like to thank the “Dipartimento di Informatica” of Pisa for their support in the organization of the symposium and the many people who have contributed to this book and to the organization of the symposium. Sincere thanks to Paulo Lisboa, Elia Biganzoli, Davide Bacciu, Umberto Barcaro, Franco Alberto Cardillo, Katuscia Cerbioni, Claudio Gallicchio, Darya Majidi, Stefania Pellegrini, and K. Brent Venable. References [1] G. Da San Martino, F.A. Cardillo, A. Starita. A new Swarm Intelligence Coordination Model Inspired by Collective Prey Retrieval and its Application to Image Alignment. Parallel Problem Solving from Nature (2006): 691--700. [2] A. Sperduti, A. Starita. Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8 (1997): 714--735. [3] A.M. Bianucci, A. Micheli, A. Sperduti, A. Starita, Application of cascade correlation networks for structures to chemistry, Appl. Int. 12 (2000): 117--146. [4] F. Masulli, D. Sona, A. Sperduti, A. Starita, G. Zaccagnini. A System for the Automatic Morphological Analysis of Medieval Manuscripts. Journal of Forensic Document Examination 9 (1996): 45--55. [5] D. Sona, A. Sperduti, and A. Starita. A Constructive Learning Algorithm for Discriminant Tangent Models. NIPS (1996): 786--792. [6] D. Sona, A. Sperduti, and A. Starita. Discriminant Pattern Recognition Using Transformation Invariant Neurons. Neural Computation 12(2000):1355--1370. [7] B. Rossi, F. Sartucci, A.Starita. Automatic Analysys of the Spontaneous EMG Activity During Ischaemic Test in Tetany. Electromyogr. Clin. Neurophysiol. 24 (1984): 75--80. [8] S. La Manna, Darya Majidi, Antonina Starita, Davide Caramella, A. Cilotti. Magnetic resonance in mammography: a tool for the automatic detection of the regions of interest in contrast-enhanced magnetic resonance of the breast. Computer Assisted Radiology and Surgery (2001): 1275--1276. [9] F.A. Cardillo, A. Starita, D. Caramella, A. Cilotti. A hybrid method for breast MR images processing and classification. IEEE International Symposium on Biomedical Imaging (2002): 181--184. [10] F. Baronti, A. Micheli, A. Passaro, A. Starita. Machine Learning Contribution to Solve Prognosis Medical Problems, in Outcome Prediction in Cancer, A.F.G. Taktak and A.C. Fisher Eds., Elsevier Science, 2006. [11] A. Starita, D. Majidi, A. Giordano, M. Battaglia, R. Cioni. NEUREX: A tutorial expert system for the diagnosis of neurogenic diseases of the lower limbs, Journal of Artificial Intelligence in Medicine 7 (1995): 25--36. [12] F. Leoni, M. Guerrini, C. Laschi, D. Taddeucci, P. Dario, A. Starita. Implementing Robotic Grasping Tasks Using a Biological Approach. ICRA (1998): 2274--2280. [13] G. Asuni, F. Leoni, E. Guglielmelli, A. Starita, P. Dario. A Neuro-controller for Robotic Manipulators Based on Biologically-Inspired Visuo-Motor Co-ordination Neural Models. International IEEE EMBS Conference on Neural Engineering (2003): 450--453. [14] G. Vannozzi, U. Della Croce, A. Starita, F. Benvenuti, A. Cappozzo. Knowledge discovery in data-bases of biomechanical variables: application to the sit to stand motor task. Journal of Neuroengineering Rehabil. 1 (2004): 1--10.