453 research outputs found
Pattern Discovery from Biosequences
In this thesis we have developed novel methods for analyzing biological data, the primary sequences of the DNA and proteins, the microarray based gene expression data, and other functional genomics data. The main contribution is the development of the pattern discovery algorithm SPEXS, accompanied by several practical applications for analyzing real biological problems. For performing these biological studies that integrate different types of biological data we have developed a comprehensive web-based biological data analysis environment Expression Profiler (http://ep.ebi.ac.uk/)
Protein sequences classification by means of feature extraction with substitution matrices
<p>Abstract</p> <p>Background</p> <p>This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.</p> <p>Results</p> <p>In order to demonstrate the efficiency of such approach, we compare several encoding methods using some machine learning classifiers. The experimental results showed that our encoding method outperforms other ones in terms of classification accuracy and number of generated attributes. We also compared the classifiers in term of accuracy. Results indicated that SVM generally outperforms the other classifiers with any encoding method. We showed that SVM, coupled with our encoding method, can be an efficient protein classification system. In addition, we studied the effect of the substitution matrices variation on the quality of our method and hence on the classification quality. We noticed that our method enables good classification accuracies with all the substitution matrices and that the variances of the obtained accuracies using various substitution matrices are slight. However, the number of generated features varies from a substitution matrix to another. Furthermore, the use of already published datasets allowed us to carry out a comparison with several related works.</p> <p>Conclusions</p> <p>The outcomes of our comparative experiments confirm the efficiency of our encoding method to represent protein sequences in classification tasks.</p
Computing regularities in strings: A survey
The aim of this survey is to provide insight into the sequential algorithms that have been proposed to compute exact “regularities” in strings; that is, covers (or quasiperiods), seeds, repetitions, runs (or maximal periodicities), and repeats. After outlining and evaluating the algorithms that have been proposed for their computation, I suggest possibly productive future directions of research
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets
QuadStack: An Efficient Representation and Direct Rendering of Layered Datasets
We introduce QuadStack, a novel algorithm for volumetric data compression and direct rendering. Our algorithm exploits the data redundancy often found in layered datasets which are common in science and engineering fields such as geology, biology, mechanical engineering, medicine, etc. QuadStack first compresses the volumetric data into vertical stacks which are then compressed into a quadtree that identifies and represents the layered structures at the internal nodes. The associated data (color, material, density, etc.) and shape of these layer structures are decoupled and encoded independently, leading to high compression rates (4× to 54× of the original voxel model memory footprint in our experiments). We also introduce an algorithm for value retrieving from the QuadStack representation and we show that the access has logarithmic complexity. Because of the fast access, QuadStack is suitable for efficient data representation and direct rendering. We show that our GPU implementation performs comparably in speed with the state-of-the-art algorithms (18-79 MRays/s in our implementation), while maintaining a significantly smaller memory footprint
A computational framework for unsupervised analysis of everyday human activities
In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner.
A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity.
Exploiting such representations, we propose a computational framework to discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify
a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments.Ph.D.Committee Chair: Aaron Bobick; Committee Member: Charles Isbell; Committee Member: David Hogg; Committee Member: Irfan Essa; Committee Member: James Reh
Network dynamics in the neural control of birdsong
Sequences of stereotyped actions are central to the everyday lives of humans and animals, from the kingfisher's dive to the performance of a piano concerto. Lashley asked how neural circuits managed this feat nearly 6 decades ago, and to this day it remains a fundamental question in neuroscience. Toward answering this question, vocal performance in the songbird was used as a model to study the performance of learned, stereotyped motor sequences. The first component of this work considers the song motor cortical zone HVC in the zebra finch, an area that sends precise timing signals to both the descending motor pathway, responsible for stereotyped vocal performance in the adult, and the basal ganglia, which is responsible for both motor variability and song learning. Despite intense interest in HVC, previous research has exclusively focused on describing the activity of small numbers of neurons recorded serially as the bird sings. To better understand HVC network dynamics, both single units and local field potentials were sampled across multiple electrodes simultaneously in awake behaving zebra finches. The local field potential and spiking data reveal a stereotyped spatio-temporal pattern of inhibition operating on a 30 ms time-scale that coordinates the neural sequences in principal cells underlying song. The second component addresses the resilience of the song circuit through cutting the motor cortical zone HVC in half along one axis. Despite this large-scale perturbation, the finch quickly recovers and sings a near-perfect song within a single day. These first two studies suggest that HVC is functionally organized to robustly generate neural dynamics that enable vocal performance. The final component concerns a statistical study of the complex, flexible songs of the domesticated canary. This study revealed that canary song is characterized by specific long-range correlations up to 7 seconds long-a time-scale more typical of human music than animal vocalizations. Thus, the neural sequences underlying birdsong must be capable of generating more structure and complexity than previously thought
Music Synchronization, Audio Matching, Pattern Detection, and User Interfaces for a Digital Music Library System
Over the last two decades, growing efforts to digitize our cultural heritage could be observed. Most of these digitization initiatives pursuit either one or both of the following goals: to conserve the documents - especially those threatened by decay - and to provide remote access on a grand scale. For music documents these trends are observable as well, and by now several digital music libraries are in existence. An important characteristic of these music libraries is an inherent multimodality resulting from the large variety of available digital music representations, such as scanned score, symbolic score, audio recordings, and videos. In addition, for each piece of music there exists not only one document of each type, but many. Considering and exploiting this multimodality and multiplicity, the DFG-funded digital library initiative PROBADO MUSIC aimed at developing a novel user-friendly interface for content-based retrieval, document access, navigation, and browsing in large music collections. The implementation of such a front end requires the multimodal linking and indexing of the music documents during preprocessing. As the considered music collections can be very large, the automated or at least semi-automated calculation of these structures would be recommendable. The field of music information retrieval (MIR) is particularly concerned with the development of suitable procedures, and it was the goal of PROBADO MUSIC to include existing and newly developed MIR techniques to realize the envisioned digital music library system. In this context, the present thesis discusses the following three MIR tasks: music synchronization, audio matching, and pattern detection. We are going to identify particular issues in these fields and provide algorithmic solutions as well as prototypical implementations. In Music synchronization, for each position in one representation of a piece of music the corresponding position in another representation is calculated. This thesis focuses on the task of aligning scanned score pages of orchestral music with audio recordings. Here, a previously unconsidered piece of information is the textual specification of transposing instruments provided in the score. Our evaluations show that the neglect of such information can result in a measurable loss of synchronization accuracy. Therefore, we propose an OCR-based approach for detecting and interpreting the transposition information in orchestral scores. For a given audio snippet, audio matching methods automatically calculate all musically similar excerpts within a collection of audio recordings. In this context, subsequence dynamic time warping (SSDTW) is a well-established approach as it allows for local and global tempo variations between the query and the retrieved matches. Moving to real-life digital music libraries with larger audio collections, however, the quadratic runtime of SSDTW results in untenable response times. To improve on the response time, this thesis introduces a novel index-based approach to SSDTW-based audio matching. We combine the idea of inverted file lists introduced by Kurth and Müller (Efficient index-based audio matching, 2008) with the shingling techniques often used in the audio identification scenario. In pattern detection, all repeating patterns within one piece of music are determined. Usually, pattern detection operates on symbolic score documents and is often used in the context of computer-aided motivic analysis. Envisioned as a new feature of the PROBADO MUSIC system, this thesis proposes a string-based approach to pattern detection and a novel interactive front end for result visualization and analysis
Behaviour modelling with data obtained from the Internet and contributions to cluster validation
[EN]This PhD thesis makes contributions in modelling behaviours found in different types of data acquired from the Internet and in the field of clustering evaluation. Two different types of Internet data were processed, on the one hand, internet traffic with the objective of attack detection and on the other hand, web surfing activity with the objective of web personalization, both data being of sequential nature. To this aim, machine learning techniques were applied, mostly unsupervised techniques. Moreover, contributions were made in cluster evaluation, in order to make easier the selection of the best partition in clustering problems.
With regard to network attack detection, first, gureKDDCup database was generated which adds payload data to KDDCup99 connection attributes because it is essential to detect non-flood attacks. Then, by modelling this data a network Intrusion Detection System (nIDS) was proposed where context-independent payload processing was done obtaining satisfying detection rates.
In the web mining context web surfing activity was modelled for web personalization. In this context, generic and non-invasive systems to extract knowledge were proposed just using the information stored in webserver log files. Contributions were done in two senses: in problem detection and in link suggestion. In the first application a meaningful list of navigation attributes was proposed for each user session to group and detect different navigation profiles. In the latter, a general and non-invasive link suggestion system was proposed which was evaluated with satisfactory results in a link prediction context.
With regard to the analysis of Cluster Validity Indices (CVI), the most extensive CVI comparison found up to a moment was carried out using a partition similarity measure based evaluation methodology. Moreover, we analysed the behaviour of CVIs in a real web mining application with elevated number of clusters in which they tend to be unstable. We proposed a procedure which automatically selects the best partition analysing the slope of different CVI values.[EU]Doktorego-tesi honek internetetik eskuratutako datu mota ezberdinetan aurkitutako portaeren modelugintzan eta multzokatzeen ebaluazioan egiten ditu bere ekarpenak. Zehazki, bi mota ezberdinetako interneteko datuak prozesatu dira: batetik, interneteko trafikoa, erasoak hautemateko helburuarekin; eta bestetik, web nabigazioen jarduera, weba pertsonalizatzeko helburuarekin; bi datu motak izaera sekuentzialekoak direlarik. Helburu hauek lortzeko, ikasketa automatikoko teknikak aplikatu dira, nagusiki gainbegiratu-gabeko teknikak. Testuinguru honetan, multzokatzeen partizio onenaren aukeraketak dakartzan arazoak gutxitzeko multzokatzeen ebaluazioan ere ekarpenak egin dira.
Sareko erasoen hautemateari dagokionez, lehenik gureKDDCup datubasea eratu da KDDCup99-ko konexio atributuei payload-ak (sareko paketeen datu eremuak) gehituz, izan ere, ez-flood erasoak (pakete gutxi erabiltzen dituzten erasoak) hautemateko ezinbestekoak baitira. Ondoren, datu hauek modelatuz testuinguruarekiko independenteak diren payload prozesaketak oinarri dituen sareko erasoak hautemateko sistema (network Intrusion Detection System (nIDS)) bat proposatu da maila oneko eraso hautemate-tasak lortuz.
Web meatzaritzaren testuinguruan, weba pertsonalizatzeko helburuarekin web nabigazioen jarduera modelatu da. Honetarako, web zerbizarietako lorratz fitxategietan metatutako informazioa soilik erabiliz ezagutza erabilgarria erauziko duen sistema orokor eta ez-inbasiboak proposatu dira. Ekarpenak bi zentzutan eginaz: arazoen hautematean eta esteken iradokitzean. Lehen aplikazioan sesioen nabigazioa adierazteko atributu esanguratsuen zerrenda bat proposatu da, gero nabigazioak multzokatu eta nabigazio profil ezberdinak hautemateko. Bigarren aplikazioan, estekak iradokitzeko sistema orokor eta ez-inbasibo bat proposatu da, eta berau, estekak aurresateko testuinguruan ebaluatu da emaitza onak lortuz.
Multzokatzeak balioztatzeko indizeen (Cluster Validity Indices (CVI)) azterketari dagokionez, gaurdaino aurkitu den CVI-en konparaketa zabalena burutu da partizioen antzekotasun neurrian oinarritutako ebaluazio metodologia erabiliz. Gainera, CVI-en portaera aztertu da egiazko web meatzaritza aplikazio batean normalean baino multzo kopuru handiagoak dituena, non CVI-ek ezegonkorrak izateko joera baitute. Arazo honi aurre eginaz, CVI ezberdinek partizio ezberdinetarako lortzen dituzten balioen maldak aztertuz automatikoki partiziorik onena hautatzen duen prozedura proposatu da.[ES]Esta tesis doctoral hace contribuciones en el modelado de comportamientos encontrados en diferentes tipos de datos adquiridos desde internet y en el campo de la evaluación del clustering. Dos tipos de datos de internet han sido procesados: en primer lugar el tráfico de internet con el objetivo de detectar ataques; y en segundo lugar la actividad generada por los usuarios web con el objetivo de personalizar la web; siendo los dos tipos de datos de naturaleza secuencial. Para este fin, se han aplicado técnicas de aprendizaje automático, principalmente técnicas no-supervisadas. Además, se han hecho aportaciones en la evaluación de particiones de clusters para facilitar la selección de la mejor partición de clusters.
Respecto a la detección de ataques en la red, primero, se generó la base de datos gureKDDCup que añade el payload (la parte de contenido de los paquetes de la red) a los atributos de la conexión de KDDCup99 porque el payload es esencial para la detección de ataques no-flood (ataques que utilizan pocos paquetes). Después, se propuso un sistema de detección de intrusos (network Intrusion Detection System (IDS)) modelando los datos de gureKDDCup donde se propusieron varios preprocesos del payload independientes del contexto obteniendo resultados satisfactorios.
En el contexto de la minerı́a web, se ha modelado la actividad de la navegación web para la personalización web. En este contexto se propondrán sistemas genéricos y no-invasivos para la extracción del conocimiento, utilizando únicamente la información almacenada en los ficheros log de los servidores web. Se han hecho aportaciones en dos sentidos: en la detección de problemas y en la sugerencia de links. En la primera aplicación, se propuso una lista de atributos significativos para representar las sesiones de navegación web para después agruparlos y detectar diferentes perfiles de navegación. En la segunda aplicación, se propuso un sistema general y no-invasivo para sugerir links y se evaluó en el contexto de predicción de links con resultados satisfactorios.
Respecto al análisis de ı́ndices de validación de clusters (Cluster Validity Indices (CVI)), se ha realizado la más amplia comparación encontrada hasta el momento que utiliza la metodologı́a de evaluación basada en medidas de similitud de particiones. Además, se ha analizado el comportamiento de los CVIs en una aplicación real de minerı́a web con un número elevado de clusters, contexto en el que los CVIs tienden a ser inestables, ası́ que se propuso un procedimiento para la selección automática de la mejor partición en base a la pendiente de los valores de diferentes CVIs.Grant of the Basque Government (ref.: BFI08.226); Grant of Ministry of Economy and Competitiveness of the Spanish Government (ref.: BES-2011-045989); Research stay grant of Spanish Ministry of Economy and Competitiveness (ref.: EEBB-I-14-08862); University of the Basque Country UPV/EHU (BAILab, grant UFI11/45); Department of Education, Universities and Research of the Basque Government (grant IT-395-10); Ministry of Economy and Competitiveness of the Spanish Government and by the European Regional Development Fund - ERDF (eGovernAbility, grant TIN2014-52665-C2-1-R)
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