ABSTRACT
Background: The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop. Methods: To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset. Results: At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively. Conclusions: This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.
ABSTRACT
Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM), the second data challenge battling avian flu was kicked off in April 2006 to identify new drugs for the potential variants of the influenza A virus. Mobilizing thousands of CPUs on the Grid, the six-week-long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a worldwide Grid infrastructure to efficiently deploy large-scale virtual screening to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.
Subject(s)
Drug Design , Drug Evaluation, Preclinical/methods , Enzyme Inhibitors/chemistry , Influenza A virus/enzymology , Internet , Neuraminidase/chemistry , Sequence Analysis, Protein/methods , Binding Sites , Protein Binding , Protein Interaction Mapping/methodsABSTRACT
DNA is a universal language encrypted with biological instruction for life. In higher organisms, the genetic information is preserved predominantly in an organized exon/intron structure. When a gene is expressed, the exons are spliced together to form the transcript for protein synthesis. We have developed a complexity reduction algorithm for sequence analysis (CRASA) that enables direct alignment of cDNA sequences to the genome. This method features a progressive data structure in hierarchical orders to facilitate a fast and efficient search mechanism. CRASA implementation was tested with already annotated genomic sequences in two benchmark data sets and compared with 15 annotation programs (10 ab initio and 5 homology-based approaches) against the EST database. By the use of layered noise filters, the complexity of CRASA-matched data was reduced exponentially. The results from the benchmark tests showed that CRASA annotation excelled in both the sensitivity and specificity categories. When CRASA was applied to the analysis of human Chromosomes 21 and 22, an additional 83 potential genes were identified. With its large-scale processing capability, CRASA can be used as a robust tool for genome annotation with high accuracy by matching the EST sequences precisely to the genomic sequences.