Knowledge discovery from tree databases using balanced optimal search

This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.

A transcriptomic analysis of the kidney tissue of Tra catfish (pangasianodon hypophthalmus) reared in saline condition: De novo assembly, annotation, SNP discovery

Pangasianodon hypophthalmus is a commercially important freshwater fish used in inland aquaculture in the Mekong Delta, Vietnam. The current study using Ion Torrent technology generated EST resources from the kidney for Tra catfish reared at a salinity level of 9 ppt. We obtained 2,623,929 reads after trimming and processing with an average length of 104 bp. De novo assemblies were generated using CLC Genomic Workbench, Trinity and Velvet/Oases with the best overall contig performance resulting from the CLC assembly. De novo assembly using CLC yielded 29,940 contigs, and allowing identification of 5,710 putative genes when comppared with NCBI non-redundant database. A large number of single nucleotide polymorphisms (SNPs) were also detected. The sequence collection generated in our study represents the most comprehensive transcriptomic resource for P. hypophthalmus available to date.

Drug discovery approaches utilizing three-dimensional cell culture

Historically, two-dimensional (2D) cell culture has been the preferred method of producing disease models in vitro. Recently, there has been a move away from 2D culture in favor of generating three-dimensional (3D) multicellular structures, which are thought to be more representative of the in vivo environment. This transition has brought with it an influx of technologies capable of producing these structures in various ways. However, it is becoming evident that many of these technologies do not perform well in automated in vitro drug discovery units. We believe that this is a result of their incompatibility with high-throughput screening (HTS). In this study, we review a number of technologies, which are currently available for producing in vitro 3D disease models. We assess their amenability with high-content screening and HTS and highlight our own work in attempting to address many of the practical problems that are hampering the successful deployment of 3D cell systems in mainstream research.

Cointegration and causal relationships between energy consumption and output: Assessing the evidence from Australia

In this paper, we describe our investigation of the cointegration and causal relationships between energy consumption and economic output in Australia over a period of five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach used in an Australian study of this type to include energy, capital and labour as separate inputs of production. The empirical evidence points to a cointegration relationship between energy and output and implies that energy is an important variable in the cointegration space, as are conventional inputs capital and labour. We also find some evidence of bidirectional causality between GDP and energy use. Although the evidence of causality from energy use to GDP was relatively weak when using the thermal aggregate of energy use, once energy consumption was adjusted for energy quality, we found strong evidence of Granger causality from energy use to GDP in Australia over the investigated period. The results are robust, irrespective of the assumptions of linear trends in the cointegration models, and are applicable for different econometric approaches.

Next-generation sequencing: A frameshift in skeletal dysplasia gene discovery

In the last decade, huge breakthroughs in genetics - driven by new technology and different statistical approaches - have resulted in a plethora of new disease genes identified for both common and rare diseases. Massive parallel sequencing, commonly known as next-generation sequencing, is the latest advance in genetics, and has already facilitated the discovery of the molecular cause of many monogenic disorders. This article describes this new technology and reviews how this approach has been used successfully in patients with skeletal dysplasias. Moreover, this article illustrates how the study of rare diseases can inform understanding and therapeutic developments for common diseases such as osteoporosis. © International Osteoporosis Foundation and National Osteoporosis Foundation 2013.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.