Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: How to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.

A model of price discovery and market design : theory and empirical evidence

An essential function of derivative markets is price discovery. A model is proposed to incorporate a comprehensive dynamic interaction between price size coordinates of orders and trades. An example of application of the model and its effect on price discovery is discussed.

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning.

Industrial lab-on-a-chip: design, applications and scale-up for drug discovery and delivery

Microfluidics is an emerging and promising interdisciplinary technology which offers powerful platforms for precise production of novel functional materials (e.g., emulsion droplets, microcapsules, and nanoparticles as drug delivery vehicles- and drug molecules) as well as high-throughput analyses (e.g., bioassays, detection, and diagnostics). In particular, multiphase microfluidics is a rapidly growing technology and has beneficial applications in various fields including biomedicals, chemicals, and foods. In this review, we first describe the fundamentals and latest developments in multiphase microfluidics for producing biocompatible materials that are precisely controlled in size, shape, internal morphology and composition. We next describe some microfluidic applications that synthesize drug molecules, handle biological substances and biological units, and imitate biological organs. We also highlight and discuss design, applications and scale up of droplet- and flow-based microfluidic devices used for drug discovery and delivery.

Price discovery and asset pricing

This paper tests the hypothesis that price discovery influences asset pricing. Our innovations are twofold. First, we estimate time-varying price discovery for a large number (21) of Islamic stock portfolios. Second, we test using a predictive regression model whether or not price discovery predicts stock excess returns. We find from both in-sample and out-of-sample tests that all 21 portfolio excess returns are predictable. We show that a mean-variance investor by tracking price discovery is able to devise profitable trading strategies.

Revolt and discovery : towards an understanding of practice as research through Kristevan aesthetics

This paper will examine Kristeva’s conceptions of revolution and revolt to demonstrate the significance of her work for practitioners and researchers working in the emerging field of creative arts practice as research, a field of research that is burgeoning in the UK, Australia, Canada and Scandinavia. I will argue that Kristeva’ thought elaborates the aesthetic underpinnings of discovery and provides a rationale for the methodologies used in artistic research.

In her later work on interpretation, Kristeva places a greater emphasis on the need for analysis or theory, since the art and culture of revolt produce unfamiliar or mutant meanings that are difficult for audiences to grasp in terms of their potency for engendering social change and individual empowerment. However, she places the responsibility for this analysis and interpretation on the art critic. But what if (as is the case with the advent of artistic practice as research), the maker and the “critic” become one and the same? Can this shift in the status of artistic practice within the knowledge economy, be understood in terms of Kristeva account of the sense and nonsense of revolt? I will address these questions by revisiting aspects of Kristeva thinking on experience-in practice and examining her more recent and extended elaboration of revolutionary practice. The paper will explore how her thinking can provide practitioners with a framework for understanding creative arts research as the production of new knowledge. If as Kristeva argues, that art and literature are amongst the few means of revolt and renewal, it seems appropriate to turn to her thinking in order to articulate a rationale and argument for claiming that practice as research can operate as a driver of change and innovation in contemporary culture.

The first part of this task will involve tracing what Kristeva sees as three forms of revolt made possible through aesthetic experience. This will involve a closer examination of the notions of transgression and art as experience. Following on from this discussion, I will discuss how Kristeva’s work constitutes both an implicit and explicit critique of science allowing us to conceive of artistic research as an alternative and performative production of knowledge. Finally in this paper, I will apply and illustrate these ideas through an analysis of a selection of a number of research projects successfully completed by artistic researchers in Australia. I hope to show that artistic practice as a mode of enquiry, reveals the inextricable and necessary relationship between practice and theory, interpretation and making, art and life. I suggest that it is this interrelationship, that underpins what Kristeva describes as creative and revolutionary practice. In the context of creative arts practice as research, Kriteva’s account of experience–in-practice indicates that interpretation and analysis must fall to the practitioner-researcher himself or herself - rather than to another person who has been external to the procedures of making - to trace the significant experiential, subjective and emergent processes involved in the production of the work that allows it to reveal the new. This is necessary if the generative and revolutionary impact of artistic research is to be fully understood in the wider research arena.

User perceptions and acceptance of benevolent worms - a matter of fear?

Worms and other forms of malware have been considered by IT Security firms and large companies for many years as one of the leading threats to the integrity of their data and security. However, several researchers over recent years have been working on creating worms which, instead of causing harm to machines which they infect, or the networks on which the machines reside, actually aid the network and systems administrators. Several uses of these worms have been proposed by these researchers, including, but not limited to, rapid remote patching of machines, network and system administration through use of their unique discovery and propagation methods, actively hunting, and defending against, other forms of malware such as "malevolent" worms, viruses, spyware, as well as increasing reliable communication of nodes in distributed computing. However, there has been no hint of commercial adoption of these worms, which one researcher has described as being due to a fear factor'. This paper concentrates on assessing and delivering the findings of user attitudes towards these worms in an attempt to find out how users feel about these worms, and to try and define and overcome the factors which might contribute to the fear factor'.

Interacting amino acid preferences of 3D pattern pairs at the binding sites of transient and obligate protein complexes

To assess the physico-chemical characteristics of protein-protein interactions, protein sequences and overall structural folds have been analyzed previously. To highlight this, discovery and examination of amino acid patterns at the binding sites defined by structural proximity in 3-dimensional (3D) space are essential. In this paper, we investigate the interacting preferences of 3D pattern pairs discovered separately in transient and obligate protein complexes. These 3D pattern pairs are not necessarily sequence-consecutive, but each residue in two groups of amino acids from two proteins in a complex is within certain °A threshold to most residues in the other group. We develop an algorithm called AA-pairs by which every pair of interacting proteins is represented as a bipartite graph, and it discovers all maximal quasi-bicliques from every bipartite graph to form our 3D pattern pairs. From 112 and 2533 highly conserved 3D pattern pairs discovered in the transient and obligate complexes respectively, we observe that Ala and Leu is the highest occuring amino acid in interacting 3D patterns of transient (20.91%) and obligate (33.82%) complexes respectively. From the study on the dipeptide composition on each side of interacting 3D pattern pairs, dipeptides Ala-Ala and Ala-Leu are popular in 3D patterns of both transient and obligate complexes. The interactions between amino acids with large hydrophobicity difference are present more in the transient than in the obligate complexes. On contrary, in obligate complexes, interactions between hydrophobic residues account for the top 5 most occuring amino acid pairings.

Exploiting affinity propagation for energy-efficient information discovery in sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. Emerging WSN applications require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group "similar" sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.