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BACKGROUND: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These

Empirical evidence shows that a strong correlation exists between the flexion angles of the distal interphalangeal joint (D.I.P.), and the proximal interphalangeal joint (P.I.P.) of the human finger. The aim of the study therefore is to find an analytical expression for this correlation, from where the first derivative may be calculated. Especially the visualisation of the rate of change of the D.I.P. flexion versus P.I.P. flexion provides an additional and clear-cut discriminatory tool between normal and pathological states of the finger.

Focal dystonia (FD) - a neuromuscular disorder - severely interferes with occupational activities – moreover, it may be an early Multiple Sclerosis symptom (Yücesan et al., 2000) - Focal dystonia of hand and fingers, described as an imbalanced tension of their different muscles, leads to painful, impaired and often aberrant motions (Van Zwieten et al., 2008) - Normal coordinated finger flexion becomes clear by a kinematic model (Sholukha et al., 1998) - Such a model can also be used to analyze uncoordinated finger movement patterns, e.g. those visible after long standing peripheral neuropathies (Van Zwieten et al., 2008) - An example of such a peripheral neuropathy is the “intrinsic minus” hand, caused by chronic neurodegeneration of n. ulnaris, which innervates most of intrinsic hand muscles - Fingers of “intrinsic minus” hands are characterized by o.a. hyperextension of their metacarpophalangeal joints, simultaneous with flexion of the both interphalangeal joints - Muscle tension imbalances in focal dystonia too, will produce “intrinsic minus” patterns - As focal dystonia in instrumentalists’ hands also presents itself with “intrinsic minus” patterns (Van Zwieten et al., 2009), similar patterns may be seen in PC-workers Endorsed by the Belgische Vereniging voor Dystoniepatiënten VZW / Association Belge des Patients Dystoniques ASBL / Belgische Vereinigung für Dystoniepatienten VoG, Website : www.dystonie.be

Endorsed by the Belgische Vereniging voor Dystoniepatiënten VZW / Association Belge des Patients Dystoniques ASBL / Belgische Vereinigung für Dystoniepatienten VoG, Website : www.dystonie.be

Excerpt of introduction: Unfortunately, in spite of the above mentioned advantages, the presence of a schema is not mandatory and many XML documents are not accompanied by one. For instance, in a recent study, Barbosa et al. have shown that approximately half of the XML documents available on the web do not refer to a schema. In another study, we have noted that about two-thirds of XSDs gathered from schema repositories and from the web are not valid with respect to the W3C XML Schema specification, rendering them essentially useless for immediate application (see Chapter 6). A similar observation was made by Sahuguet concerning DTDs. Based on the lack of schemas in practice, it is essential to devise algorithms that can infer a schema for a given collection of XML documents when none, or no syntactically correct one, is present. This is also acknowledged by Florescu who emphasizes that in the context of data integration: “We need to extract good-quality schemas automatically from existing data and perform incremental maintenance of the generated schemas.” It should be noted that even when a schema is already available, there are situations where inference can be useful. One such situation is schema cleaning: sometimes a schema is too general with respect to the XML data that it is supposed to describe. In that case, it can be advantageous to infer a new schema based solely on the data at hand.... In general, schema inference can be used to restrict schemas to a relevant subset of data needed by the application at hand, thereby facilitating difficult tasks like schema matching and data integration. Indeed, as argued by Hinkelman [Hin05], industry-level standards are too loosely defined in general, which can result in XML schemas where many business structures are formally specified as being optional.... Based on the above observations, it is hence essential to devise algorithms that can automatically infer a DTD or XSD from a given corpus of XML documents....

This paper is concerned with the learning process of a sparse interaction network, for example, a gene-protein interaction network. The advantage of the process we purpose is that there will always be a student S that fits the teacher T very well with a relatively small data set and a high number of unknown components, i.e., when the number of measurements M is significantly smaller than the system size N. To measure the efficiency of this learning process, we use the generalization error, epsilon_gen, which represents the probability that the student is a good fit to the teacher. From our experiments it follows that the quality of the fit depends on several factors: First, the ratio α = M/N of the number of measurements to the system size has a strong impact. Surprisingly, we find that a sudden identification transition occurs for value α ≈ αgen which corresponds to epsilon_gen = 1/2. From this sample size onwards the student will be a good fit to the teacher. Interestingly, the generalization threshold αgen, will always be significantly smaller than 1. Second, the quality of the fit depends on the sparsity of the network. If the number of non-zero components increases, as sparsity disappears, the efficiency of the process will gradually increase. Finally there is an impact of the noise level. The learning process is robust to noise upto a certain threshold. We see that, at this level, the impact on the noise suddenly and dramatically increases as a consequence of which the student will no longer be a good fit to the teacher.

In this paper we study the potential of gene-protein interaction networks to store input-output patterns. The central question in this study concerns the memory capacity of a network of a given number of genes and proteins, which interact according to a linear state space model with external inputs. Here it is assumed that to a certain combination of inputs there exists an optimal state of the system, i.e., values of the gene expressions and protein levels, that has been attained externally, e.g., through evolutionary learning. Given such a set of learned optimal input-output patterns, the design question here is to find a sparse and hierarchical network structure for the gene-protein interactions and the gene-input couplings. This problem is formulated as an optimization problem in a linear programming setting. Numerical analysis shows that there are clear scale-invariant continuous second-order phase transitions for the network sparsity as the number of patterns increases. These phase transitions divide the system in three regions with different memory characteristics. It is possible to formulate simple scaling rules for the behavior of the network sparsity. Finally, numerical experiments show that these patterns are stable within a certain finite range around the patterns.

In this study we will focus on piecewise linear state space models for gene-protein interaction networks. We will follow the dynamical systems approach with special interest for partitioned state spaces. From the observation that the dynamics in natural systems tends to punctuated equilibria, we will focus on piecewise linear models and sparse and hierarchic interactions, as, for instance, described by Glass, Kauffman, and de Jong. Next, the paper is concerned with the identification (also known as reverse engineering and reconstruction) of dynamic genetic networks from microarray data. We will describe exact and robust methods for computing the interaction matrix in the special case of piecewise linear models with sparse and hierarchic interactions from partial observations. Finally, we will analyze and evaluate this approach with regard to its performance and robustness towards intrinsic and extrinsic noise.