The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This study was to evaluate the construct validity of WHOQOL-BREF & Disabilities module for physical disabilities (PD) and intellectual disabilities (ID) people, using Item Response Theory (IRT) Graded Response Model (GRM) analysis. As one of 14 centres for the field study of the WHOQOL Disabilities module, a stratified representative sample of Guangzhou general disabled people was approached for...
Emotion is an important indicator of depressive conditions. Emotion recognition based on physiological signals such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) has gained significant attraction in healthcare domain research. Sharing of physiological signal data related to emotional response between different healthcare systems has the potential to benefit both laboratory-based...
The EU-funded eNanoMapper project proposes a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs) based on open standards, ontologies and an interoperable design to enable a more effective, integrated approach to European research in nanotechnology. eNanoMapper's goal is to support the collaborative safety assessment for ENMs by creating a modular, extensible...
Nanotoxicity modeling can reveal the relationship between nanomaterial properties and unintended adverse effects. Traditional modeling usually builds a prediction model on the whole dataset. It does not examine the subsets of the data and their quality for model building. In this paper, we introduce a prediction cube approach to nanotoxicity modeling. Prediction cube is a new type of data cube for...
Persistency of genomic data brings many research challenges. Among them, alternatives to the commonly used relational database systems should be evaluated, to deal with large volumes of data, in particular writing operations and specific modelling issues. In this paper, we present a NoSQL approach to treat genomic data (Cassandra database system), and evaluate both persistency and I/O operations in...
Sequence based machine learning approaches for 1-D and 2-D protein structure prediction tasks have long been limited by relatively small datasets, namely proteins with experimentally determined structure. Recent advances in machine learning provide a means of using unlabeled data and, as a result, this opens up access to a much larger sequence space in the context of protein structure prediction....
Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. An important and unsolved problem in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from time-course gene expression data. The conventional one-stage model inference algorithm determines...
In today's healthcare environment, nurses play an integral role in determining patient outcomes. This role becomes especially clear in intensive care units such as the Neonatal Intensive Care Unit (NICU). In the NICU, critically ill infants rely almost completely on the care of these nurses for survival. Given the importance of their role, and the volatile conditions of the infants, it is imperative...
This paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning...
Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the widespread use of microarray experiments and clinical studies, a large numbers of candidate cancer genes are produced and extracting informative genes out of them is essential...
High accuracy and reproducibility provided by selected or multiple reaction monitoring (SRM/MRM) experiments suggest that it is likely to become the platform of choice for identifying and verifying candidate biomarkers. Although several methods have been developed to quantify and discover the significant changes in SRM/MRM expression, we show normalization continues to be an essential step in the...
Many scientific experiments are designed as computational workflows in bioinformatics. However, the amount of data generated increases at every phase of each execution, hindering the identification of the source and the transformation of data. Therefore, it has become necessary to create new tools to store data provenance, mainly which resources and parameters were used to generate the results, among...
Biomedical research depends upon increasingly high throughput instruments and sophisticated data analytics. In spite of the significant overhead of handling research data, there is little support for researchers to manage and organize data for purposes of exploration, analysis, and ultimately publication. Shared file systems with metadata coded into directory hierarchies and spreadsheets are the common...
In oncology, the risk of Serious Adverse Events (SAEs) is an important factor in the decision for a certain treatment, because SAEs can be life threatening and/or they can reduce the quality of life to a large extent. Prediction models can be made, based on data available from clinical trials. Building such prediction models requires great effort and expertise. On the one hand, data mining expertise...
This paper presents a heterogeneous computing solution for an optimized genetic selection analysis tool, GenSel. GenSel can be used to efficiently infer the effects of genetic markers on a desired trait or to determine the genomic estimated breeding values (GEBV) of genotyped individuals. To predict which genetic markers are informational, GenSel performs Bayesian inference using Gibbs sampling, a...
Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. Surgeons and patients have often many options to consider for undergoing the procedure. The ability to visualise the potential outcomes of the surgery and make decisions on their surgical options is, therefore, very important...
A major challenge to devising robust brain-computer interfaces (BCIs) based on electroencephalogram (EEG) data is the immanent non-stationary characteristics of EEG signals. Statistical properties of the signals may shift during inter-or-intra session transfers that often leads to deteriorated BCI performance. The shift in the input data distribution from training to testing phase is called a covariate...
In order to separate the chromatogram peaks and spectra from the High Performance Liquid Chromatography with Diode Array Detector (HPLC-DAD) data set, a separation model of Generalized Reference Curve Measurement and its solution by multitarget Bare Bones Particle Swarm Optimization (GRCMmBBPSO) is proposed in this paper. Firstly, parameters are constructed which will generate Reference Curves (RCs)...
Computer aid technology is widely applied in decision-making and outcome assessment of healthcare delivery, in which modeling knowledge and expert experience is technically important. However, the conventional rule-based models are incapable of capturing the underlying knowledge because they are incapable of simulating the complexity of human brains and highly rely on feature representation of problem...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.