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Electronic noses and disease diagnostics.

Rapid developments in sensor technology have facilitated the production of devices--known as electronic noses--that can detect and discriminate the production profiles of volatile compounds from microbial infections in situ. Such qualitative and semi-quantitative approaches could have a significant role in the early diagnosis and detection of microbial diseases. Using artificial intelligence and web-based knowledge systems, electronic noses might also have a valuable role in monitoring disease epidemiology.

Biosensing Techniques↗

Relationship between EEG dimensional complexity and neuropsychological findings in Alzheimer's disease.

The aim of the present study was to examine the region-specific correlations between electroencephalography (EEG) dimensional complexity (DC), a measure of non-linear dynamics, and neuropsychological performance in 25 right-handed patients with Alzheimer's disease (AD). Electroencephalography recording sites were according to the international 10-20 system. Neuropsychological tests included Wechelor Adult Intelligence Scale-Revised (WAIS-R) (Full-scale Intelligence Quotient (FIQ), Verbal Intelligence Quotient (VIQ), Performance Intelligent Quotient (PIQ)); Mini-Mental State Examination; Raven's Coloured Progressive Matrices (RCPM); Rey Auditory-Verbal Learning Test (RAVLT); seven word pairs-revised Miyake Paired-Associate Word Learning Test; Benton Visual Retention Test; and Rey Complex Figure Test. A partial correlational analysis was carried out, controlling for age and sex (P< 0.005). The FIQ, VIQ and RCPM scores were found to be significantly correlated with DC at the F3, C3 and T3 electrodes. Significant correlations were also found between RAVLT scores and DC at the C3, P3 and T5 electrodes. The study on AD indicated region-specific correlations between DC and neuropsychological performance: one between the DC value in the left frontal, central and mid-temporal areas and intellectual function; and another between the DC value in the left central, parietal and post-temporal areas and verbal memory. Dimensional complexity would therefore seem to be a useful indicator for the assessment of neuropsychological deficits in AD.

Aged↗

Outcome of medulloblastoma in children: long-term complications and quality of life.

To study the outcomes in long-term survivors of paediatric medulloblastoma (MB), we followed 51 consecutive children who were treated between 1980 and 2000 in a single institution. In 18 of 26 survivors (mean follow-up time 12.2 years), tumour control, neurological, endocrine, and neurocognitive complications and their impact on behavioural and psychological adjustment, and health-related quality of life (QoL) were comprehensively assessed using qualitative and quantitative measures. Endocrine deficits occurred in 61 %, neurological complications in 72 %, and significant school problems in 72 %. All patients had significant deficits in neurocognitive functioning: attention and processing speed was impaired in 79 %, learning and memory in 88 %, language in 56 %, visual perception in 50 %, and executive functions in 64 %. In comparison with healthy controls, social functioning was rated by the patients as the QoL dimension most affected. Parents' ratings were considerably lower than those of the patients. No MB survivor > 18 years of age (n = 12) had a boy- or girlfriend. Because of their treatment, including craniospinal radiotherapy, MB long-time survivors are not only at great risk for neurological, endocrine, and neurocognitive complications, but also of social isolation thereby decreasing self-rated QoL substantially.

Activities of Daily Living↗

Use of artificial neural networks in prostate cancer.

Artificial neural networks (ANNs) are a type of artificial intelligence software inspired by biological neuronal systems that can be used for nonlinear statistical modeling. In recent years, these applications have played an increasing role in predictive and classification modeling in medical research. We review the basic concepts behind ANNs and examine the role of this technology in selected applications in prostate cancer research.

Humans↗

CaGE: cardiac gene expression knowledgebase.

UNLABELLED: CaGE is a Cardiac Gene Expression knowledgebase we have developed to facilitate the analysis of genes important to human cardiac function. CaGE integrates the functionality of the LocusLink database with data from several human cardiac expression libraries, phenotypic data from OMIM and data from large-scale microarray gene expression studies to create a knowledgebase of gene expression in human cardiac tissue. The knowledgebase is fully searchable via the web using several intuitive query interfaces. Results can be displayed in several concise easy to navigate formats. AVAILABILITY: CaGE is located at http://www.cage.wbmei.jhu.edu

Artificial Intelligence↗

Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network.

MOTIVATION: There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature. RESULTS: We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed.edu/IRIDESCENT/GO_relationships.htm

Abstracting and Indexing↗

GAPSCORE: finding gene and protein names one word at a time.

MOTIVATION: New high-throughput technologies have accelerated the accumulation of knowledge about genes and proteins. However, much knowledge is still stored as written natural language text. Therefore, we have developed a new method, GAPSCORE, to identify gene and protein names in text. GAPSCORE scores words based on a statistical model of gene names that quantifies their appearance, morphology and context. RESULTS: We evaluated GAPSCORE against the Yapex data set and achieved an F-score of 82.5% (83.3% recall, 81.5% precision) for partial matches and 57.6% (58.5% recall, 56.7% precision) for exact matches. Since the method is statistical, users can choose score cutoffs that adjust the performance according to their needs. AVAILABILITY: GAPSCORE is available at http://bionlp.stanford.edu/gapscore/

Abstracting and Indexing↗

An entity tagger for recognizing acquired genomic variations in cancer literature.

VTag is an application for identifying the type, genomic location and genomic state-change of acquired genomic aberrations described in text. The application uses a machine learning technique called conditional random fields. VTag was tested with 345 training and 200 evaluation documents pertaining to cancer genetics. Our experiments resulted in 0.8541 precision, 0.7870 recall and 0.8192 F-measure on the evaluation set.

Abstracting and Indexing↗

Extracting gene pathway relations using a hybrid grammar: the Arizona Relation Parser.

MOTIVATION: Text-mining research in the biomedical domain has been motivated by the rapid growth of new research findings. Improving the accessibility of findings has potential to speed hypothesis generation. RESULTS: We present the Arizona Relation Parser that differs from other parsers in its use of a broad coverage syntax-semantic hybrid grammar. While syntax grammars have generally been tested over more documents, semantic grammars have outperformed them in precision and recall. We combined access to syntax and semantic information from a single grammar. The parser was trained using 40 PubMed abstracts and then tested using 100 unseen abstracts, half for precision and half for recall. Expert evaluation showed that the parser extracted biologically relevant relations with 89% precision. Recall of expert identified relations with semantic filtering was 35 and 61% before semantic filtering. Such results approach the higher-performing semantic parsers. However, the AZ parser was tested over a greater variety of writing styles and semantic content. AVAILABILITY: Relations extracted from over 600 000 PubMed abstracts are available for retrieval and visualization at http://econport.arizona.edu:8080/NetVis/index.html.

Artificial Intelligence↗

DNAFSMiner: a web-based software toolbox to recognize two types of functional sites in DNA sequences.

UNLABELLED: DNAFSMiner (DNA Functional Sites Miner) is a web-based software toolbox to recognize functional sites in nucleic acid sequences. Currently in this toolbox, we provide two software: TIS Miner and Poly(A) Signal Miner. The TIS Miner can be used to predict translation initiation sites in vertebrate DNA/mRNA/cDNA sequences, and the Poly(A) Signal Miner can be used to predict polyadenylation [poly(A)] signals in human DNA sequences. The prediction results are better than those by literature methods on two benchmark applications. This good performance is mainly attributable to our unique learning method. DNAFSMiner is available free of charge for academic and non-profit organizations. AVAILABILITY: http://research.i2r.a-star.edu.sg/DNAFSMiner/ CONTACT: huiqing@i2r.a-star.edu.sg.

Algorithms↗

Automatic extension of Gene Ontology with flexible identification of candidate terms.

MOTIVATION: Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. RESULTS: We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. AVAILABILITY: http://autogo.biopathway.org SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Algorithms↗

Annotating proteins by mining protein interaction networks.

MOTIVATION: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. RESULTS: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. AVAILABILITY: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.

Amino Acid Sequence↗

Fixing healthcare from the inside: teaching residents to heal broken delivery processes as they heal sick patients.

There is a marked gap between the potential of medical science to treat illness and injury and the performance of the health care system in which hard-working, intelligent, well-trained people put that science to work. Care is available to too few, and costs and risks of injury are too high. This is avoidable. Experience in Boston, Pittsburgh, Salt Lake City, Seattle, and elsewhere indicates that quality can be raised-while risks and costs are dramatically reduced-by applying lessons from the highest-performing industrial organizations to designing, operating, and improving health care processes. What are these lessons? Improve sick processes with the same approaches used to treat patients. Specify "normal." When problems in quality, safety, efficiency, responsiveness, and the like occur, quickly determine exactly what is abnormal and determine what might be causing them. Develop a "treatment plan"-process changes that will eliminate or counteract the causal factors. Run the process (or a facsimile) with a modified, watching for gaps between actual and expected outcomes. When gaps occur, do a new work-up, diagnosis, treatment plan, and test. Lessons in designing, operating, and improving processes can be taught just as medical expertise is developed. Teach basic frameworks didactically; then, provide hands-on experience in applying those principles to real problems. Start with simple well-bounded situations that can be practiced frequently, with rapid feedback and close mentoring before advancing to more complex, less well bounded situations that occur less frequently and provide less immediate feedback between action and outcome. Incorporate development of process improvement skills into residency training so that deepening expertise within specialties is complemented by greater skill at integrating functional knowledge into well-integrated care processes.

Delivery of Health Care↗

Extracting and characterizing gene-drug relationships from the literature.

A fundamental task of pharmacogenetics is to collect and classify relationships between genes and drugs. Currently, this useful information has not been comprehensively aggregated in any database and remains scattered throughout the published literature. Although there are efforts to collect this information manually, they are limited by the size of the published literature on gene-drug relationships. Therefore, we investigated computational methods to extract and characterize pharmacogenetic relationships between genes and drugs from the literature. We first evaluated the effectiveness of the co-occurrence method in identifying related genes and drugs. We then used supervised machine learning algorithms to classify the relationships between genes and drugs from the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) into five categories that have been defined by active pharmacogenetic researchers as relevant to their work. The final co-occurrence algorithm was able to extract 78% of the related genes and drugs that were published in a review article from the literature. Our algorithm subsequently classified the relationships between genes and drugs from the PharmGKB into five categories with 74% accuracy. We have made the data available on a supplementary website at http://bionlp.stanford.edu/genedrug/ Gene-drug relationships can be accurately extracted from text and classified into categories. Although the relationships that we have identified do not capture the details and fine distinctions often made in the literature, these methods will help scientists to track the ever-growing literature and create information resources to support future discoveries.

Algorithms↗