Evolucijski algoritam temeljen na off-line planeru putanje za navigaciju bespilotnih letjelica

An off-line path planner for Unmanned Air Vehicles is presented. The planner is based on Evolutionary Algorithms, in order to calculate a curved pathline with desired characteristics in a three-dimensional environment. The pathline is represented using B-Spline curves, with the coordinates of its control points being the genes of the Evolutionary Algorithm artificial chromosome. The method was tested in an artificial three-dimensional terrain, for different starting and ending points, providing very smooth pathlines under difficult constraints.Predstavljen je off-line planer putanje za bespilotne letjelice. Planer je temeljen na evolucijskim algoritmima za proračun zakrivljene putanje sa željenim karakteristikama u 3D prostoru. Putanja je predstavljena pomoću B-spline krivulja, gdje su koordinate kontrolnih točaka geni umjetnih kromosoma evolucijskih algoritama. Metoda je provjerena na umjetnom 3D prostoru s različitim početnim i konačnim točkama, gdje su dobivene vrlo glatke putanje uz zadovoljenje strogih ograničenja

Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis

Background B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance. Results We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0–15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expressed genes include, among others, multiple genes related to WNT signaling as well as the miRNAs miR-150-5p and miR-155-5p. Conclusions Our findings demonstrate the presence in prediagnostic blood of future CLL patients, more than 10 years before diagnosis, of CLL-like cells which evolve as preclinical disease progresses, and point to early molecular alterations with a pathogenetic potential

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

<p>Abstract</p> <p>Background</p> <p>Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.</p> <p>Results</p> <p>PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.</p> <p>Conclusions</p> <p>The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.</p

Networks and hybrid algorithms for the study of interrelations within biological/environmental data

In the present PhD Thesis, network-based algorithms aiming at analyzing and revealing interrelations within biological/environmental data are proposed. The algorithms are applied here within two different contexts: i) analysis of protein sequence and structural space, and fold recognition using networks as simple graphs (first part) and ii) analysis of multifactorial disease-related traits using hybrid artificial neural network methods (second part). In the first part, protein similarity networks are constructed separately for two similarity criteria based on sequence derived features and structural alignment. Measurements, like network degree, clustering coefficient, characteristic path length and vertex centrality are utilized to characterize their overall and local topology. Protein similarity networks are classified as small world networks, an architecture that can host the similarity transition among proteins during evolution. Furthermore, the task of fold recognition on a protein similarity network basis is addressed. A classifier that recognizes the fold of a query protein sequence by calculating its affinities to sequences-vertices of the protein sequence network is, thus, constructed and compared with popular classifiers. The classifier is then optimized by employing a genetic algorithm which selects the subset of sequence-derived features yielding a protein sequence network that is most similar to the corresponding protein structure network. In the second part, two artificial neural network based methods have been designed and developed towards the analysis of multifactorial traits that have been related to cardiovascular diseases. The proposed methods combine an artificial neural network with either a parameter decreasing algorithm or a genetic algorithm that optimizes the training process and the input features set fed to the network. Both methods were used to determine the most important factors (among sex, age, genetic polymorphisms, clinical measurements and measurements that describe average nutrition habits) that affect postprandial lipemia and human weight. The artificial neural network based methods were successful in classifying subjects into output classes derived from the multifactorial traits.Στην παρούσα διατριβή προτείνονται και αναπτύσσονται αλγόριθμοι βασισμένοι σε δίκτυα για την επεξεργασία και ανάλυση βιολογικών/περιβαλλοντικών δεδομένων με κύριο σκοπό τη διερεύνηση συσχετίσεων σε αυτά. Συγκεκριμένα, οι αλγόριθμοι που αναπτύσσονται χρησιμοποιούνται για την ανάλυση και επεξεργασία (i) πρωτεϊνικών δεδομένων με στόχο την ανάλυση του χώρου των δομών και ακολουθιών και τη συνεισφορά στην αναγνώριση διπλώματος των πρωτεϊνών, (ii) δεδομένων που προκύπτουν από τη γενετική ταυτότητα ατόμων και περιβαλλοντικές παραμέτρους με σκοπό την αιτιολογική ανάλυση πολυπαραγοντικών φαινοτύπων που σχετίζονται με τις καρδιαγγειακές νόσους. Στο πρώτο μέρος της διατριβής χρησιμοποιούνται βασικές αρχές δικτύων για τη μελέτη της τοπολογίας δικτύων ομοιότητας πρωτεϊνών σε επίπεδο δομής και ακολουθίας. Σε επίπεδο ακολουθίας τα δίκτυα ομοιότητας κατασκευάζονται με χρήση της απόστασης διανυσμάτων χαρακτηριστικών εξαγόμενων από την ακολουθία, ενώ σε επίπεδο δομής με χρήση του βαθμού ομοιότητας που προκύπτει από τη δομική τους στοίχιση. Τα αποτελέσματα της ανάλυσης των δικτύων συνδέονται με εξελικτική πληροφορία των πρωτεϊνών, ενώ αξιολογείται η πληροφορία που περιέχουν τα εξαγόμενα από την ακολουθία χαρακτηριστικά σε σχέση με την πρωτεϊνική δομή. Με βάση το δίκτυο ομοιότητας σε επίπεδο ακολουθίας, κατασκευάζεται ταξινομητής που υπολογίζει τη συγγένεια πρωτεϊνικής ακολουθίας με ακολουθίες γνωστού διπλώματος και χρησιμοποιείται για την αναγνώριση διπλώματος. Το δεύτερο μέρος της εργασίας αφορά στον προσδιορισμό παραγόντων (φύλου, ηλικίας, γενετικών πολυμορφισμών, κλινικών μετρήσεων και διατροφικών συνηθειών) που αλληλεπιδρούν και συνδυαστικά επηρεάζουν την επικινδυνότητα ανάπτυξης καρδιαγγειακών νόσων. Αναλύονται δυο διαφορετικά διαθέσιμα σύνολα δεδομένων στα οποία η ποσοτικοποίηση της επικινδυνότητας βασίζεται στους φαινοτύπους της μεταγευματικής λιπαιμίας και της παχυσαρκίας, αντίστοιχα. Η μεθοδολογία που αναπτύσσεται βασίζεται στη χρήση τεχνητών νευρωνικών δικτύων σε συνδυασμό με τη μέθοδο της όπισθεν επιλογής χαρακτηριστικών και γενετικό αλγόριθμο για την επιλογή των σημαντικών παραγόντων και συνδυασμών τους. Η εφαρμογή των υβριδικών μεθόδων οδήγησε στο προσδιορισμό των βέλτιστων υποσυνόλων παραγόντων που επηρεάζουν τους υπό μελέτη φαινοτύπους, καθώς και σε αντίστοιχους ταξινομητές τεχνητού νευρωνικού δικτύου με ικανοποιητική ικανότητα γενίκευσης σε άγνωστα δεδομένα

A similarity network approach for the analysis and comparison of protein sequence/structure sets

AbstractA set of proteins is a complex system whose elements are interrelated on the concept of sequence- and structure-based similarity. Here, we applied a similarity network-based methodology for the representation and analysis of protein sequences and structures sets using a non-redundant set of 311 proteins and three different information criteria based on sequence-derived features, sequence local alignment and structural alignment. A wide set of measurements, like network degree, clustering coefficient, characteristic path length and vertex centrality were utilized to characterize the networks’ topology. Protein similarity networks were found medium or highly interconnected and the existence of both clusters and random edges classified their fully connected versions as Small World Networks (SWNs). The SWN architecture was able to host the continuous similarity transition among proteins and model the protein information flow during evolution. Recently reported ancestral elements, like the α/β class and certain folds, were remarkably found to act as hubs in the networks. Additionally, the moderate information value of sequence-derived features when used for fold and class assignment was shown on a network basis. The methodology described here can be applied for the analysis of other complex systems which consist of interrelated elements and a certain information flow