Improving override rates for computerized prescribing alerts in ambulatory care.
(25/335)
Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. We developed a selective knowledge base of only clinically significant drug alerts and designated only critical-high severity alerts to be interruptive to clinician workflow (a tiered approach). Using this approach, we were able to achieve a 67% clinician accept rate for ambulatory computerized prescribing alerts. (+info)
Novel knowledge-based mean force potential at the profile level.
(26/335)
BACKGROUND: The development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials. RESULTS: The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Phi/Psi dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5-6%). The contact and Phi/Psi dihedral angle potential only get a slight improvement (1-2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading. CONCLUSION: The knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement. (+info)
Introduction to early in vitro identification of metabolites of new chemical entities in drug discovery and development.
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The introduction of combinatorial chemistry and robotics for high throughput screening has changed the way drugs are discovered today compared with 10-15 years ago when fewer compounds were tested in animal or organ models. The introduction of new analytical techniques, especially liquid chromatography/mass spectrometry (LC/MS) has made it possible to characterize the chemical properties, permeability, metabolic stability and metabolic fate of a large number of screening hits for further development in a funnel-like manner. The purpose of this contribution is to discuss principles and recent strategies for metabolite identification and to give an introduction to biotransformation studies. Metabolites are experimentally generated with the use of animal and human recombinant expressed enzymes, and different liver and other tissue fractions like microsomes and slices. For separation and identification of structurally diverse metabolites, LC/MS and tandem mass spectrometry (LC/MS/MS) techniques are commonly used. The LC/MS and LC/MS/MS techniques are rapid, sensitive, easy to automate and robust, and therefore, they are the methods of choice for these studies. The outcome of the metabolite identification studies is detection of metabolites that could be pharmacologically active and contribute to the efficacy of a new chemical entity (NCE), and elimination of compounds that form reactive intermediates and/or toxic metabolites that could cause adverse effects of NCE. If such information is available at an early stage during the drug discovery process, the chemical structure of the compound may be modified to reduce the risk of idiosyncratic and/or adverse drug reactions during clinical development. (+info)
Improving the human readability of Arden Syntax medical logic modules using a concept-oriented terminology and object-oriented programming expressions.
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Medical logic modules are a procedural representation for sharing task-specific knowledge for decision support systems. Based on the premise that clinicians may perceive object-oriented expressions as easier to read than procedural rules in Arden Syntax-based medical logic modules, we developed a method for improving the readability of medical logic modules. Two approaches were applied: exploiting the concept-oriented features of the Medical Entities Dictionary and building an executable Java program to replace Arden Syntax procedural expressions. The usability evaluation showed that 66% of participants successfully mapped all Arden Syntax rules to Java methods. These findings suggest that these approaches can play an essential role in the creation of human readable medical logic modules and can potentially increase the number of clinical experts who are able to participate in the creation of medical logic modules. Although our approaches are broadly applicable, we specifically discuss the relevance to concept-oriented nursing terminologies and automated processing of task-specific nursing knowledge. (+info)
GEDA: new knowledge base of gene expression in drug addiction.
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Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and nonredundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain. (+info)
Peroxisome targeting signal 1: is it really a simple tripeptide?
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Originally, the peroxisomal targeting signal 1 (PTS1) was defined as a tripeptide at the C-terminus of proteins prone to be imported into the peroxisomal matrix. The corresponding receptor PEX5 initiates the translocation of proteins by identifying potential substrates via their C-termini and trapping PTS1s through remodeling of its TPR domain. Thorough studies on the interaction between PEX5 and PTS1 as well as sequence-analytic tools revealed the influence of amino acid residues further upstream of the ultimate tripeptide. Altogether, PTS1s should be defined as dodecamer sequences at the C-terminal ends of proteins. These sequences accommodate physical contacts with both the surface and the binding cavity of PEX5 and ensure accessibility of the extreme C-terminus. Knowledge-based approaches in applied Bioinformatics provide reliable tools to accurately predict the peroxisomal location of proteins not yet determined experimentally. (+info)
TimeTree: a public knowledge-base of divergence times among organisms.
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Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. AVAILABILITY: TimeTree is available at http://www.timetree.net (+info)
PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing.
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Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. It has the potential to substantially facilitate biomedical research by extracting, linking, and organizing massive amounts of information that occur in biomedical journal articles as well as in textual fields of biological databases. Until recently, much of the work in biological NLP and text mining has revolved around recognizing the occurrence of biomolecular entities in articles, and in extracting particular relationships among the entities. Now, researchers have recognized a need to link the extracted information to ontologies or knowledge bases, which is a more difficult task. One such knowledge base is Gene Ontology annotations (GOA), which significantly increases semantic computations over the function, cellular components and processes of genes. For multicellular organisms, these annotations can be refined with phenotypic context, such as the cell type, tissue, and organ because establishing phenotypic contexts in which a gene is expressed is a crucial step for understanding the development and the molecular underpinning of the pathophysiology of diseases. In this paper, we propose a system, PhenoGO, which automatically augments annotations in GOA with additional context. PhenoGO utilizes an existing NLP system, called BioMedLEE, an existing knowledge-based phenotype organizer system (PhenOS) in conjunction with MeSH indexing and established biomedical ontologies. More specifically, PhenoGO adds phenotypic contextual information to existing associations between gene products and GO terms as specified in GOA. The system also maps the context to identifiers that are associated with different biomedical ontologies, including the UMLS, Cell Ontology, Mouse Anatomy, NCBI taxonomy, GO, and Mammalian Phenotype Ontology. In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and recall of 92%, demonstrating that the PhenoGO NLP system can accurately encode a large number of anatomical and cellular ontologies to GO annotations. The PhenoGO Database may be accessed at the following URL: http://www.phenoGO.org (+info)