Report 77-11 Structure Elucidation Based on Computer S Stanford -- KSL Analysis of High and Low Resolution Mass Spectral Data. A tremendous effort has been directed toward development of advanced instrumentation for mass spectrometrlc analysis. Advancements include everincreasing sensitivities and resolving powers, new ionization techniques, metastable ion probes of ion decomposition and structure and computer systems for rapid acquisition and reduction of data. We sometimes lose sight of the fact that these developments are designed to provide information about chemical and biochemic,a1 structures at greater TeTTE--aTrin greater detail than previously available. The ultimate goal in most research in mass spectrometry is to provide powerful tools for molecular structure elucidation, either directly, by exploitation of existing techniques, or indirectly by development of new techniques. Concurrently, several computer-based techniques designed to assist chemists in the analysis and interpretation of mass spectral data have been developed. Reprinted with permission from Smith, Dennis H. and Carhart, Raymond E. in "High Performance Mass Spectrometry: Chemical Applications," Michael Gross, Ed., in ACS SYMPOSIUM SERIES, No. 70; American Chemical Society: Washington, D.C., 1978, pp.325-347. Library search procedures (2) and their extensions (I) or attern recoviitioz programs (4) may provide clues to t e identity of the structure or be used to determine the structure uniquely. A computer program for analysis of spectra based on classspecific fragmentation rules, is available (5). There are several reasons or this lag: There is no formal theory.

In this paper we examine the selection.

Since the domain expert often knows nothing about programming, the interaction between the expert and the performance program usually requires the mediation of a human programmer.

Both tasks are concerned with the interpretation of large quantities of digitized signal data. The task of SU/X is to understand "continuous signal that is, signals which persist over time. The task of SU/P is to interpret protein x-ray crystallographic data. Some features of the design are: (1) incremental interpretation of data employing many different pattern-invoked sources of knowledge, (2) production rule representation of knowledge, includiR high level strategy knowledge, (3) "opportunistic" hypothesis formation using Dr,

It is generally accepted that problem solving systems require a wealth of domain specific knowledge for effective performance in complex domains. This report takes the view that all domain specific knowledge should be expressed in a knowledge base. With this in mind, the ideas and techniques from problem solving and knowledge base research are reviewed and outstanding problems are identified. Finally, a task domain is characterized in terms of objects, actions, and control/strategy knowledge and suggestions are made for creating a uniform knowledge base management system to be used for knowledge acquisition, DD "" 1473

Almost one-half of the total cost of drugs spent in treating hospitalized patients is spend on antibiotics (1,2), and a significant part of this therapy is associated with serious misuse (2,3,4,5). One problem involves incorrect selection of a therapeutic regimen [4], while another involves the incorrect decision to administer any antibiotic (2,4,5). For example, one recent study concluded that one out of every four people in the United States was given penicillin during a recent year, and nearly 907. of these preL,criptions were oahccessary (6).

A Glossary of Terms Used in Protein Crystallography.. - - - - A KNOWLEDGE-BASED SYSTEM FOR THE INTERPRETATION OF PROTEIN X-RAY CRYSTALLOGRAPHIC DATA ABSTRACT The broad goal of this project is to develop intelligent computational systems to infer the three-dimensional structures of proteins from x-ray crystallographic data. The computational systems under development use both formal and judgmental knowledge from experts to select appropriate procedures and to constrain the space of plausible protein structures. The hypothesis generating and testing procedures operate upon a variety of representations of the data, and work with several different descriptions of the structure being inferred. The system consists of a number of independent but cooperating knowledge sources which propose, augment and verify a sol.uticn to the problem as it is incrementally generated.

ABSTRACT A computer program that models an expert in a given domain is more likek to be accepted by experts in that domain, and by non-experts seeking its gavice. An explanation capability not only adds to the system's credibility, but also enables the non-expert user to learn from it. Furthermore, clear explanations allow an expert to check the system's "reasoning", possibly discovering the need for refinements and additions to the svstem's knowledge base. In a developing system, an explanation capability can be used as a debugging aid to verify that additions to the system are working as'hey should. The explanation facility in MYCIN is discussed as an illustration of how the various problems might be approached.

Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and AI gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.

One of the most difficult steps in the development of an expert system is the recruitment and exploitation of the domain wizards. Almost always it is necessary to establish teams of specialists to deal with the programming issues and the user interfaces as well as the incorporation of domain specific knowledge. Experts will communicate how they read a gel, or what is the canonical biological interpretation of DNA sequences conserved over phyletically diverse organisms. The computer scientist will rarely have an independent base of knowledge and experience for critical judgments about the wisdom thus received. Therein may lie the greatest hazards from the proliferation of expert systems; for much of that expertise is fallible.