Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Derivation of Context-free Stochastic L-Grammar Rules for Promoter Sequence Modeling Using Support Vector Machine

Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modeling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognized by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L- grammar rules are analyzed and compared with natural promoter sequences.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

The mitochondrial genome of the stingless bee Melipona bicolor (Hymenoptera, Apidae, Meliponini): sequence, gene organization and a unique tRNA translocation event conserved across the tribe Meliponini

At present a complete mtDNA sequence has been reported for only two hymenopterans, the Old World honey bee, Apis mellifera and the sawfly Perga condei. Among the bee group, the tribe Meliponini (stingless bees) has some distinction due to its Pantropical distribution, great number of species and large importance as main pollinators in several ecosystems, including the Brazilian rain forest. However few molecular studies have been conducted on this group of bees and few sequence data from mitochondrial genomes have been described. In this project, we PCR amplified and sequenced 78% of the mitochondrial genome of the stingless bee Melipona bicolor (Apidae, Meliponini). The sequenced region contains all of the 13 mitochondrial protein-coding genes, 18 of 22 tRNA genes, and both rRNA genes (one of them was partially sequenced). We also report the genome organization (gene content and order), gene translation, genetic code, and other molecular features, such as base frequencies, codon usage, gene initiation and termination. We compare these characteristics of M. bicolor to those of the mitochondrial genome of A. mellifera and other insects. A highly biased A+T content is a typical characteristic of the A. mellifera mitochondrial genome and it was even more extreme in that of M. bicolor. Length and compositional differences between M. bicolor and A. mellifera genes were detected and the gene order was compared. Eleven tRNA gene translocations were observed between these two species. This latter finding was surprising, considering the taxonomic proximity of these two bee tribes. The tRNA Lys gene translocation was investigated within Meliponini and showed high conservation across the Pantropical range of the tribe.

Qualitative Analysis of the Interdisciplinary Interaction between Data Analysis Specialists and Novice Clinical Researchers

Background: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time. Methods/Principal Findings: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of ""what if'' situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal. Conclusion/Significance: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

The taxonomic status of the endangered thin-spined porcupine, Chaetomys subspinosus (Olfers, 1818), based on molecular and karyologic data

Background: The thin-spined porcupine, also known as the bristle-spined rat, Chaetomys subspinosus (Olfers, 1818), the only member of its genus, figures among Brazilian endangered species. In addition to being threatened, it is poorly known, and even its taxonomic status at the family level has long been controversial. The genus Chaetomys was originally regarded as a porcupine in the family Erethizontidae, but some authors classified it as a spiny-rat in the family Echimyidae. Although the dispute seems to be settled in favor of the erethizontid advocates, further discussion of its affinities should be based on a phylogenetic framework. In the present study, we used nucleotide-sequence data from the complete mitochondrial cytochrome b gene and karyotypic information to address this issue. Our molecular analyses included one individual of Chaetomys subspinosus from the state of Bahia in northeastern Brazil, and other hystricognaths. Results: All topologies recovered in our molecular phylogenetic analyses strongly supported Chaetomys subspinosus as a sister clade of the erethizontids. Cytogenetically, Chaetomys subspinosus showed 2n = 52 and FN = 76. Although the sexual pair could not be identified, we assumed that the X chromosome is biarmed. The karyotype included 13 large to medium metacentric and submetacentric chromosome pairs, one small subtelocentric pair, and 12 small acrocentric pairs. The subtelocentric pair 14 had a terminal secondary constriction in the short arm, corresponding to the nucleolar organizer region (Ag-NOR), similar to the erethizontid Sphiggurus villosus, 2n = 42 and FN = 76, and different from the echimyids, in which the secondary constriction is interstitial. Conclusion: Both molecular phylogenies and karyotypical evidence indicated that Chaetomys is closely related to the Erethizontidae rather than to the Echimyidae, although in a basal position relative to the rest of the Erethizontidae. The high levels of molecular and morphological divergence suggest that Chaetomys belongs to an early radiation of the Erethizontidae that may have occurred in the Early Miocene, and should be assigned to its own subfamily, the Chaetomyinae.

In silico analysis of candidate genes involved in light sensing and signal transduction pathways in soybean

Several aspects of photoperception and light signal transduction have been elucidated by studies with model plants. However, the information available for economically important crops, such as Fabaceae species, is scarce. In order to incorporate the existing genomic tools into a strategy to advance soybean research, we have investigated publicly available expressed sequence tag ( EST) sequence databases in order to identify Glycine max sequences related to genes involved in light-regulated developmental control in model plants. Approximately 38,000 sequences from open-access databases were investigated, and all bona fide and putative photoreceptor gene families were found in soybean sequence databases. We have identified G. max orthologs for several families of transcriptional regulators and cytoplasmic proteins mediating photoreceptor-induced responses, although some important Arabidopsis phytochrome-signaling components are absent. Moreover, soybean and Arabidopsis gene-family homologs appear to have undergone a distinct expansion process in some cases. We propose a working model of light perception, signal transduction and response-eliciting in G. max, based on the identified key components from Arabidopsis. These results demonstrate the power of comparative genomics between model systems and crop species to elucidate several aspects of plant physiology and metabolism.

Geospatial clustering in data-rich environments: Features and issues

Geospatial clustering must be designed in such a way that it takes into account the special features of geoinformation and the peculiar nature of geographical environments in order to successfully derive geospatially interesting global concentrations and localized excesses. This paper examines families of geospaital clustering recently proposed in the data mining community and identifies several features and issues especially important to geospatial clustering in data-rich environments.

Complete genomic sequence of the Australian south-west genotype of Sindbis virus: Comparisons with other Sindbis strains and identification of a unique deletion in the 3 '-untranslated region

Our previous studies have shown that two distinct genotypes of Sindbis (SIN) virus occur in Australia. One of these, the Oriental/Australian type, circulates throughout most of the Australian continent, whereas the recently identified south-west (SW) genetic type appears to be restricted to a distinct geographic region located in the temperate south-west of Australia. We have now determined the complete nucleotide and translated amino acid sequences of a SW isolate of SIN virus (SW6562) and performed comparative analyses with other SIN viruses at the genomic level. The genome of SW6562 is 11,569 nucleotides in length, excluding the cap nucleotide and poly (A) tail. Overall this virus differs from the prototype SIN virus (strain AR339) by 23% in nucleotide sequence and 12.5% in amino acid sequence. Partial sequences of four regions of the genome of four SW isolates were determined and compared with the corresponding sequences from a number of SIN isolates from different regions of the World. These regions are the non-structural protein (nsP3), the E2 gene, the capsid gene, and the repeated sequence elements (RSE) of the 3'UTR. These comparisons revealed that the SW SIN viruses were more closely related to South African and European strains than to other Australian isolates of SIN virus. Thus the SW genotype of SIN virus may have been introduced into this region of Australia by viremic humans or migratory birds and subsequently evolved independently in the region. The sequence data also revealed that the SW genotype contains a unique deletion in the RSE of the 3'UTR region of the genome. Previous studies have shown that deletions in this region of the SIN genome can have significant effects on virus replication in mosquito and avian cells, which may explain the restricted distribution of this genotype of SIN virus.

The complete sequence of a 9000 bp fragment of the right arm of Saccharomyces cerevisiae chromosome VII contains four previously unknown open reading frames

We report the sequence of a 9000 bp fragment from the right arm of Saccharomyces cerevisiae chromosome VII. Analysis of the sequence revealed four complete previously unknown open reading frames, which were named G7587, G7589, G7591 and G7594 following standard rules for provisional nomenclature. Outstanding features of some of these proteins were the homology of the putative protein coded by G7589 with proteins involved in transcription regulation and the transmembrane domains predicted in the putative protein coded by G7591.

On cluster analysis of complex and heterogeneous data

3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.

Mining social individuals movements and friends based on mobile devices

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Modelação de um data warehouse para a Direcção Geral do Tesouro e Finanças e implementação de um data mart para o processo de gestão patrimonial.

Dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Estatística e Gestão da Informação