GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

<p>Abstract</p> <p>Background</p> <p>Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task.</p> <p>Results</p> <p>GRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in <it>Saccharomyces cerevisiae</it>. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in <it>Saccharomyces cerevisiae</it>.</p> <p>Conclusion</p> <p>GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in <it>Saccharomyces cerevisiae</it>. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at <url>http://rosalind.infj.ulst.ac.uk/GRIP/</url>.</p

Proceed with Care:Reimagining Home IoT Through a Care Perspective

As the internet is increasingly embedded in the everyday things in our homes, we notice a need for greater focus on the role care plays in those relationships—and therefore an opportunity to realize un- seen potential in reimagining home Internet of Things (IoT). In this paper we report on our inquiry of home dwellers’ relationships to caring for their everyday things and homes (referred to as thing- care). Findings from our design ethnography reveal four thematic qualities of their relationships to thingcare: Care Spectacle, Care Liminality, Ontological Binding, and Care Condition. Using these themes as touchstones, we co-speculated to produce four specula- tive IoT concepts to explore what care as a design ethic might look like for IoT and reflect on nascent opportunities and challenges for domestic IoT design. We conclude by considering structures of power and privilege embedded within care practices that critically open new design imaginaries for IoT

Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning

Slip and skid compensation is crucial for mobile robots' navigation in outdoor environments and uneven terrains. In addition to the general slipping and skidding hazards for mobile robots in outdoor environments, slip and skid cause uncertainty for the trajectory tracking system and put the validity of stability analysis at risk. Despite research in this field, having a real-world feasible online slip and skid compensation is still challenging due to the complexity of wheel-terrain interaction in outdoor environments. This paper presents a novel trajectory tracking technique with real-world feasible online slip and skid compensation at the vehicle-level for skid-steering mobile robots in outdoor environments. The sliding mode control technique is utilized to design a robust trajectory tracking system to be able to consider the parameter uncertainty of this type of robot. Two previously developed deep learning models [1], [2] are integrated into the control feedback loop to estimate the robot's slipping and undesired skidding and feed the compensator in a real-time manner. The main advantages of the proposed technique are (1) considering two slip-related parameters rather than the conventional three slip parameters at the wheel-level, and (2) having an online real-world feasible slip and skid compensator to be able to reduce the tracking errors in unforeseen environments. The experimental results show that the proposed controller with the slip and skid compensator improves the performance of the trajectory tracking system by more than 27%

Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

The key issues pertaining to collection of epidemic disease data for our analysis purposes are that it is a labour intensive, time consuming and expensive process resulting in availability of sparse sample data which we use to develop prediction models. To address this sparse data issue, we present novel Incremental Transductive methods to circumvent the data collection process by applying previously acquired data to provide consistent, confidence-based labelling alternatives to field survey research. We investigated various reasoning approaches for semisupervised machine learning including Bayesian models for labelling data. The results show that using the proposed methods, we can label instances of data with a class of vector density at a high level of confidence. By applying the Liberal and Strict Training Approaches, we provide a labelling and classification alternative to standalone algorithms. The methods in this paper are components in the process of reducing the proliferation of the Schistosomiasis disease and its effects.Comment: 8 pages, 5 figures, Dragon 4 Symposiu

A Longitudinal Mixed Logit Model for Estimation of Push and Pull Effects in Residential Location Choice

We develop a random effects discrete choice model for the analysis of households' choice of neighbourhood over time. The model is parameterised in a way that exploits longitudinal data to separate the influence of neighbourhood characteristics on the decision to move out of the current area ("push" effects) and on the choice of one destination over another ("pull" effects). Random effects are included to allow for unobserved heterogeneity between households in their propensity to move, and in the importance placed on area characteristics. The model also includes area-level random effects. The combination of a large choice set, large sample size and repeated observations mean that existing estimation approaches are often infeasible. We therefore propose an effcient MCMC algorithm for the analysis of large-scale datasets. The model is applied in an analysis of residential choice in England using data from the British Household Panel Survey linked to neighbourhood-level census data. We consider how effects of area deprivation and distance from the current area depend on household characteristics and life course transitions in the previous year. We find substantial differences between households in the effects of deprivation on out-mobility and selection of destination, with evidence of severely constrained choices among less-advantaged households