Pattern recognition programs development

Because protein ROA spectra contain bands characteristic of loops and turns in addition to bands characteristic of secondary structure, they should provide information on the overall three-dimensional solution structure. We are developing a pattern recognition program, based on principal component analysis (PCA), to identify protein folds from ROA spectral band patterns (Blanch etal., 2002b). The method is similar to one developed for the determination of the structure of proteins from VCD (Pancoska etal., 1991) and UVCD (Venyaminov and Yang, 1996) spectra, but is expected to provide enhanced discrimination between different structural types since protein ROA spectra contain many more structure-sensitive bands than do either VCD or UVCD. From the ROA spectral data, the PCA program calculates a set of subspectra that serve as basis functions, the algebraic combination of which with appropriate expansion coefficients can be used to reconstruct any member of the... 

Software Availability. The SIMCA software is available in two forms, both developed by Wold (25) 1) an interactive, Fortran version which runs on Control Data Corporation (CDC) machines. The second set of programs are an interactive microcomputer version, SIMCA-3B, are available from Principal Data Components, 2505 Shepard Blvd., Columbia, MO 65201. The SIMCA-3B pattern recognition programs includes the CPLS-2 program used for PLS analysis and are available for CP/M (Digital Research, Pacific Grove, CA) and MS-DOS (Microsoft Corporation, Bellueve, WA) for 8088 or 80 86 based microcomputers. 

An important aspect of our AI application is the attention paid to including well-established Fortran programs and database search methods into the decision structure of an expert system network. Only certain AI software tools (such as TIMM) effectively handle this critical aspect for the analytical instrumentation field at this time (57-60)> The ability to combine symbolic and numeric processing appears to be a major factor in development of multilevel expert systems for practical instrumentation use. Therefore, the expert systems in the EXMAT linked network access factor values and the decisions from EXMATH, an expert system with chemometric/Fortran routines which are appropriate to the nature of the instrumental data and the information needed by the analyst. Pattern recognition and correlation methods are basic capabilities in this field. 

EXMATH - An Expert System for Pattern Recognition. A prototype expert system for pattern recognition and data analysis, EXMATH, has been developed to embed a chemometrician s expertise into an accessible form for researchers. The selected library of subroutines developed over the past ten years comprise a portion of the EXMATH program to permit an integrated expert systems approach (Figures 5 and 6). 

The SIMCA approach can be applied in all of the four levels of pattern recognition. We focus on its use to describe complex mixtures graphically, and on its utility in quality control. This approach was selected for the tasks of developing a quality control program and evaluating similarities in samples of various types. Principal components analysis has proven to be well suited for evaluating data from capillary gas chromatographic (GC) analyses (6-8). 

ARTHUR Infometrix, 2200 Sixth Ave. 833, Seattle, Wash. 98121, USA cca 7000. This package of Fortran programs has been developed at the Department of Chemistry of the University of Washington, Seattle. For many years it was the most widely used program for PCA and other pattern recognition applications in chemistry. ARTHUR has been written for mainframe computers, but also PC-versions are used (e.g. TNO CIVO Institute, P.O.Box 360, NL-3700 AJ Zeist, The Netherlands). An overview of ARTHUR is given in Wolff and Parsons (ref. 14). 

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