Sources Of Prior Knowledge Research Articles

Tracking Down the Intuitiveness of Gesture Interaction in the Truck Domain

Touchless hand gesture control potentially leads to a safer, more comfortable and more intuitive Human Vehicle Interaction (HVI) if relevant ergonomic requirements are met. To achieve intuitive interaction and thus to favor user acceptance, the gesture interface should be conform to user expectation and enable the users to apply their prior knowledge. This particularly concerns the gestures used for input. The conducted experiment investigates which gestures subjects tend to use for various functions of a truck and how these gestures are affected by the subjects’ prior knowledge. In total, 17 potential functions were considered for this purpose. Within the experiment, 74 subjects performed gestures for each of these functions while being recorded on video. The video data shows a variety of gestures differing in hand pose, execution space, and palm orientation. Nevertheless, several interindividual similarities in gesturing can be observed, which made it possible to analyze the gestures in terms of the prior knowledge applied. The results show that gestures differ according to the sources of prior knowledge like culture and instincts. Depending on the function, the gestures observed within the experiment are based on gestures of quasi-direct manipulation, emblematic gestures, instinctive gestures, standardized gestures and gestures expressing the users’ mental model. However, the applicability of these gestures is limited by capabilities of gesture recognition and is depending on how the user interface will be designed.

Relevance of different prior knowledge sources for inferring gene interaction networks.

When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F-scores. Furthermore, their integration with genomic data leads to a significant increase in F-scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets.

Risk-based Process Development of Biosimilars as Part of the Quality by Design Paradigm

In the last few years, several quality by design (QbD) studies demonstrated the benefit of systematic approaches for biopharmaceutical development. However, only very few studies identified biosimilars as a special case of product development. The targeted quality profile of biosimilars is strictly defined by the originator's product characteristic. Moreover, the major source of prior knowledge is the experience with the originator product itself. Processing this information in biosimilar development has a major effect on risk management and process development strategies. The main objective of this contribution is to demonstrate how risk management can facilitate the implementation of QbD in early-stage product development with special emphasis on fitting the reported approaches to biosimilars. Risk assessments were highlighted as important tools to integrate prior knowledge in biosimilar development. The risk assessment process as suggested by the International Conference on Harmonization (ICH Q9) was reviewed and three elements were identified to play a key role in targeted risk assessment approaches: proper understanding of target linkage, risk assessment tool compliance, and criticality threshold value. Adjusting these steps to biosimilar applications helped to address some unique challenges of these products such as a strictly defined quality profile or a lack of process knowledge. This contribution demonstrates the need for tailored risk management approaches for the risk-based development of biosimilars and provides novel tools for the integration of additional knowledge available for these products. The pharmaceutical industry is facing challenges such as profit loss and price competition. Companies are forced to rationalize business models and to cut costs in development as well as manufacturing. These trends recently hinder the implementation of any concepts that do not offer certain financial benefit or promise a long return of investment. Quality by design (QbD) is a concept that is currently struggling for more acceptance from the side of the pharmaceutical industry. To achieve this, the major goals of QbD have to be revisited and QbD tools have to be subsequently developed. This contribution offers an example as to how implement risk management in early-stage biosimilar development as part of the QbD concept. The main goal was to go beyond the conventional QbD workflow and to adjust risk management to the challenges of biosimilar products. Accordingly, instead of using methods like failure mode and effects analysis, recommendations of the ICH Q9 guideline were reviewed and put into practice by creating tailored risk assessment tools. The novelty of this contribution is to report those tailored tools ready-to-use for early bioprocess development of biosimilars along QbD principles.

Analysis of a Parkinson dataset: comparison between KDVS and the standard pipeline

Background High-throughput (HT) data analysis can be used to characterize Parkinson's disease (PD), the second most widespread neurodegenerative disease in the world, at the functional level and to identify possible candidate biomarkers and relevant functions involved in the disease early stage. Materials and Methods To this aim, we compare the results of two different pipelines in the analysis of the same early PD dataset (GSE6613) [1]: •the Standard pipeline in which prior knowledge is used a posteriori, after the gene signature identification; •the Knowledge Driven Variable Selection (KDVS) [2] in which prior knowledge is used a priori to structure the data matrix before variable selection. In both pipelines variable selection is performed by L1L2FS [6], an embedded variable selection method based on regularization that incorporates feature selection within the classification step. Gene Ontology (GO) [11] is the source of prior knowledge for both . Results Each pipeline identified a gene signature and a list of GO terms. The results were compared via two different procedures, namely Literature characterization and Benchmark analysis. Both pipelines were able to identify relevant gene signatures and GO terms concerning PD. The results obtained by KDVS cover much more certified knowledge on PD with respect to the results from the Standard procedure.

Use of the Crystallography Open Database as a source of prior knowledge for molecular modelling

Use of the Crystallography Open Database as a source of prior knowledge for molecular modelling

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

BackgroundBayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.ResultsWe introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.Conclusionour new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

An Exploratory Study on the Relationship between Country Image and the Evaluation of Fashion Products Influenced by the Ethnic Dress of Asians

Our study was designed to provide some insights on positive country image and the mechanisms of influence that Asian fashion industries can draw upon for future benefit. We focused on a country image and fashion products with Asian ethnic influence. Asian traditional costume elements reflected in contemporary fashion products may be important representative tools for national cultural identity. This study qualitatively investigated how and to what extent country image and additional information influence the evaluation of fashion products influenced by Chinese, Japanese, and Korean traditional dress. We conducted four focus-group interviews (FGI) with 30 students from a fashion program at a university in the United States. We analyzed the evaluation of fashion products influenced by Chinese, Japanese, and Korean traditional dress, and their associations with country image. Our qualitative analysis offered more developed empirical evidence for the various paths through which country image affects the evaluation of fashion products influenced by the ethnic dress of Asians. The results of our study showed that the more direct the source of prior knowledge, the larger the influence on product evaluation. This study showed that certain cues in the evaluation of fashion products with Asian ethnic influence induce changes in the affective state (that stem from the psychological and social nature of fashion products) and illustrate the necessity of considering the affective processes involved in the appropriate use of the country image. In the fashion industry, extrinsic properties such as the country image significantly influence the attitude and purchasing decisions of consumers. The significance of this study lies in its verification of the relationship between the country image and additional information. Modular and situational-contextual influences are also revealed as important factors that deserve more attention, as well as considerations regarding the dimensions of the country image described in attitude theory.

Accessing and Integrating Data and Knowledge for Biomedical Research

To review the issues that have arisen with the advent of translational research in terms of integration of data and knowledge, and survey current efforts to address these issues. Using examples form the biomedical literature, we identified new trends in biomedical research and their impact on bioinformatics. We analyzed the requirements for effective knowledge repositories and studied issues in the integration of biomedical knowledge. New diagnostic and therapeutic approaches based on gene expression patterns have brought about new issues in the statistical analysis of data, and new workflows are needed are needed to support translational research. Interoperable data repositories based on standard annotations, infrastructures and services are needed to support the pooling and meta-analysis of data, as well as their comparison to earlier experiments. High-quality, integrated ontologies and knowledge bases serve as a source of prior knowledge used in combination with traditional data mining techniques and contribute to the development of more effective data analysis strategies. As biomedical research evolves from traditional clinical and biological investigations towards omics sciences and translational research, specific needs have emerged, including integrating data collected in research studies with patient clinical data, linking omics knowledge with medical knowledge, modeling the molecular basis of diseases, and developing tools that support in-depth analysis of research data. As such, translational research illustrates the need to bridge the gap between bioinformatics and medical informatics, and opens new avenues for biomedical informatics research.

A Mixing Severity Model Incorporating Three Sources of Data for Operational Risk Quantification

To meet the new Solvency II Directive for operational risk capital assessment, an insurance company's internal model should make use of internal data and relevant external information. One of the unresolved challenges with operational risk quantification is combining different information sources appropriately (e.g. internal data, consortium data and publicly reported losses). This paper develops a systematic approach that apart from internal data, incorporates two sources of prior knowledge into internal loss distribution modelling. The standard statistical model resembles the idea with credibility theory and Bayesian methodology, in the sense that the sources of prior knowledge are weighted more when internal data is scarce than when internal data is abundant.

FGO: a novel ontology for identification of ligand functional group.

Small molecules play crucial role in the modulation of biological functions by interacting with specific macromolecules. Hence small molecule interactions are captured by a variety of experimental methods to estimate and propose correlations between molecular structures to their biological activities. The tremendous expanse in publicly available small molecules is also driving new efforts to better understand interactions involving small molecules particularly in area of drug docking and pharmacogenomics. We have studied and designed a functional group identification system with the associated ontology for it. The functional group identification system can detect the functional group components from given ligand structure with specific coordinate information. Functional group ontology (FGO) proposed by us is a structured classification of chemical functional group which acts as an important source of prior knowledge that may be automatically integrated to support identification, categorization and predictive data analysis tasks. We have used a new annotation method which can be used to construct the original structure from given ontological expression using exact coordinate information. Here, we also discuss about ontology-driven similarity measure of functional groups and uses of such novel ontology for pharmacophore searching and de-novo ligand designing.

Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge

There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited amount of independent experimental conditions and noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. In the present paper, we consider a Bayesian approach to systematically integrate expression data with multiple sources of prior knowledge. Each source is encoded via a separate energy function, from which a prior distribution over network structures in the form of a Gibbs distribution is constructed. The hyperparameters associated with the different sources of prior knowledge, which measure the influence of the respective prior relative to the data, are sampled from the posterior distribution with MCMC. We have evaluated the proposed scheme on the yeast cell cycle and the Raf signalling pathway. Our findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

Automated segmentation of the left ventricle in cardiac MRI

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation.

Prior knowledge for learning networks in non-probabilistic settings

Current learning methods for general causal networks are basically data-driven. Exploration of the search space is made by resorting to some quality measure of prospective solutions. This measure is usually based on statistical assumptions. We discuss the interest of adopting a different point of view closer to machine learning techniques. Our main point is the convenience of using prior knowledge when it is available. We identify several sources of prior knowledge and define their role in the learning process. Their relation to measures of quality used in the learning of possibilistic networks are explained and some preliminary steps for adapting previous algorithms under these new assumptions are presented.

Constructing partial prior specifications for models of complex physical systems [Read before The Royal Statistical Society at ameeting on 'Elicitation' on Wednesday, april 16th, 1997, the President, Professor A. F. M. Smithin the Chair

Many large scale problems, particularly in the physical sciences, are solved using complex, high dimensional models whose outputs, for a given set of inputs, are expensive and time consuming to evaluate. The complexity of such problems forces us to focus attention on those limited aspects of uncertainty which are directly relevant to the tasks for which the model will be used. We discuss methods for constructing the relevant partial prior specifications for these uncertainties, based on the prior covariance structure. Our approach combines two sources of prior knowledge. First, we elicit both qualitative and quantitative prior information based on expert prior judgments, using computer-based elicitation tools for organizing the complex collection of assessments in a systematic way. Secondly, we test and refine these judgments using detailed experiments based on versions of the model which are cheaper to evaluate. Although the approach is quite general, we illustrate it in the context of matching hydrocarbon reservoir history

Learning Optimised Control Rules with Genetic Algorithms

The paper describes evolutionary optimisation of control rules extracted from recordings of manual control. Human control skill was exploited as a source of prior knowledge, needed to establish the structure of control rules. Machine learning was employed to induce control rules from behavioural traces. Subsequently, numerical parameters of the induced rules were tuned by a genetic algorithm. Induced and optimised rules enabled faster execution of the control task and exhibited some noticeable differences in comparison with human control. The task considered in this study was swing control on a physical model of a container crane.

Principles in the use of experiential learning as a source of prior knowledge

The importance of experience and experiential learning is investigated in this report. An attempt is made to chart this concept and attention is paid to the manner in which experience can be measured and be used within the system of higher education. Since distance education attracts many mature adults with experiential learning, an indication is given of the direction that developments should take within Dutch higher education, and in the Open University in particular, in order to make an optimal use of this experience.