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Biomedical subjects

R B Altman

Publications and source records attributed to R B Altman.

63 records · Page 4Linked to original sources
PubMed1986

New strategies for the determination of macromolecular structure in solution.

Non-crystallographic approaches to the determination of protein structure must solve the problem of insufficient and low information content experimental data. Most successful methods augment experimentation with theoretical constraints (for example, potential energy functions or optimization error metrics). We believe it is important to separate the contributions of experimentation and theory in the construction of protein structure. The PROTEAN system defines protein topology on the basis of experimental data alone. Its performance on three data sets, derived from the lac-repressor headpiece of E. coli, sperm whale myoglobin, and domain 1 of bacteriophage T4 lysozyme, indicates that there may be families of related conformations that are consistent with the experimental data. These conformations provide insight into the strengths and weaknesses in the data sets. They also provide a set of structures with which to begin theoretical refinements. We outline here a strategy which maintains a clear distinction between refinements based on theory and those based on experiment, and thus allows a careful analysis of the properties of such refinement methods.

Expert Systems↗
PubMed1973

Radiography.

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Adenocarcinoma↗
PubMedDate not supplied

Automated diagnosis of data-model conflicts using metadata.

The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit.

Algorithms↗
PubMedDate not supplied

The interactions between clinical informatics and bioinformatics: a case study.

For the past decade, Stanford Medical Informatics has combined clinical informatics and bioinformatics research and training in an explicit way. The interest in applying informatics techniques to both clinical problems and problems in basic science can be traced to the Dendral project in the 1960s. Having bioinformatics and clinical informatics in the same academic unit is still somewhat unusual and can lead to clashes of clinical and basic science cultures. Nevertheless, the benefits of this organization have recently become clear, as the landscape of academic medicine in the next decades has begun to emerge. The author provides examples of technology transfer between clinical informatics and bioinformatics that illustrate how they complement each other.

Academic Medical Centers↗