Discovery and Expansion of Gene Modules by Seeking Isolated Groups in a Random Graph Process

BACKGROUND: A central problem in systems biology research is the identification and extension of biological modules-groups of genes or proteins participating in a common cellular process or physical complex. As a result, there is a persistent need for practical, principled methods to infer the modular organization of genes from genome-scale data. RESULTS: We introduce a novel approach for the identification of modules based on the persistence of isolated gene groups within an evolving graph process. First, the underlying genomic data is summarized in the form of ranked gene-gene relationships, thereby accommodating studies that quantify the relevant biological relationship directly or indirectly. Then, the observed gene-gene relationship ranks are viewed as the outcome of a random graph process and candidate modules are given by the identifiable subgraphs that arise during this process. An isolation index is computed for each module, which quantifies the statistical significance of its survival time. CONCLUSIONS: The Miso (module isolation) method predicts gene modules from genomic data and the associated isolation index provides a module-specific measure of confidence. Improving on existing alternative, such as graph clustering and the global pruning of dendrograms, this index offers two intuitively appealing features: (1) the score is module-specific; and (2) different choices of threshold correlate logically with the resulting performance, i.e. a stringent cutoff yields high quality predictions, but low sensitivity. Through the analysis of yeast phenotype data, the Miso method is shown to outperform existing alternatives, in terms of the specificity and sensitivity of its predictions

The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

Identifying Prototypical Components in Behaviour Using Clustering Algorithms

Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Marker-Assisted Selection for Biotic Stress Resistance in Peanut

Peanut is the second-most important legume grown worldwide. Cultivated peanut is a disomic tetraploid, 2n—4x—40, with limited genetic diversity due to a genetic bottleneck in formation of the polyploid from ancestors A. duranensis and A. ipaensis. Consequently, resistance_to biotic stresses is limited in the cultigen; however, wild species possess strong resistances. Transfer o f these resistances is hindered by differences o f ploidy, but production o f synthetic amphidiploids, coupled with use o f molecular markers, enables efficient gene transfer. Marker maps have been made from interspecific crosses, and SSR-based maps from cultivated parents have been developed recently. At least 410 resistance gene analogues have been identified. The first markers for biotic stress tolerance were for root-knot nematode resistance and introgressed from one A. cardenasii chromosome. These and improved markers have been used for marker-assisted backcrossing, contributing to release of three cultivars. Additional QTLs have been identified since. Early and late leafspots cause significant yield losses worldwide, and resistance depends on multiple genes. Using interspecific populations, five resistance QTLs for early leafspot were identified using greenhouse inoculations, and five QTLs for late leafspot were identified using detached leaf assays. Using cultivated species populations, 28 QTLs were identified for LLS resistance; all but one were minor QTLs; the major QTL was donated by an interspecific introgression line parent. Rust often occurs alongside leafspots, and rust resistance was characterized as one major QTL, plus several smaller QTLs. Marker-assisted backcrossing o f this major QTL has been performed into different populations. QTLs for resistance to other biotic stresses have been identified, namely to groundnut rosette virus, Sclerotinia blight, afiatoxin contamination, aphids, and tomato spotted wilt virus. Marker-assisted breeding is still in early stages, and development o f more rapid and inexpensive markers from transcriptome and genome sequencing is expected to accelerate progress

High cerebrospinal fluid levels of interleukin-10 attained by AAV in dogs

Intrathecal (IT) gene transfer using adeno-associated virus (AAV) may be clinically promising as a treatment for chronic pain if it can produce sufficiently high levels of a transgene product in the cerebrospinal fluid (CSF). While this strategy was developed in rodents, no studies investigating CSF levels of an analgesic or anti-allodynic protein delivered by IT AAV have been performed in large animals. Interleukin-10 (IL-10) is an anti-allodynic cytokine, for which target therapeutic levels have been established in rats. The present study tested IT AAV8 encoding either human IL-10 (hIL-10) or enhanced green fluorescent protein (EGFP) in a dog model of IT drug delivery. AAV8/hIL-10 at a dose of 3.5×10(12) genome copies induced high hIL-10 levels in the CSF, exceeding the target concentration previously found to be anti-allodynic in rodents by >1000-fold. AAV8/EGFP targeted the primary sensory and motor neurons and the meninges. hIL-10, a xenogeneic protein in dogs, induced anti-hIL-10 antibodies detectable in the dogs’ CSF and serum. The high hIL-10 levels demonstrate the efficacy of AAV for delivery of secreted transgenes into the IT space of large animals suggesting a strong case for further development towards clinical testing