PubMed Health⌕ Search

Biomedical subjects

J E Solomon

Publications and source records attributed to J E Solomon.

14 recordsLinked to original sources
PubMed2001

Parallel cascade identification and its application to protein family prediction.

Parallel cascade identification is a method for modeling dynamic systems with possibly high order nonlinearities and lengthy memory, given only input/output data for the system gathered in an experiment. While the method was originally proposed for nonlinear system identification, two recent papers have illustrated its utility for protein family prediction. One strength of this approach is the capability of training effective parallel cascade classifiers from very little training data. Indeed, when the amount of training exemplars is limited, and when distinctions between a small number of categories suffice, parallel cascade identification can outperform some state-of-the-art techniques. Moreover, the unusual approach taken by this method enables it to be effectively combined with other techniques to significantly improve accuracy. In this paper, parallel cascade identification is first reviewed, and its use in a variety of different fields is surveyed. Then protein family prediction via this method is considered in detail, and some particularly useful applications are pointed out.

Computational Biology↗
PubMed2000

Parallel cascade identification as a means for automatically classifying protein sequences into structure/function groups.

Current methods for automatically classifying protein sequences into structure/function groups, based on their hydrophobicity profiles, have typically required large training sets. The most successful of these methods are based on hidden Markov models, but may require hundreds of exemplars for training in order to obtain consistent results. In this paper, we describe a new approach, based on nonlinear system identification, which appears to require little training data to achieve highly promising results.

Animals↗
PubMed2000

Automatic classification of protein sequences into structure/function groups via parallel cascade identification: a feasibility study.

A recent paper introduced the approach of using nonlinear system identification as a means for automatically classifying protein sequences into their structure/function families. The particular technique utilized, known as parallel cascade identification (PCI), could train classifiers on a very limited set of exemplars from the protein families to be distinguished and still achieve impressively good two-way classifications. For the nonlinear system classifiers to have numerical inputs, each amino acid in the protein was mapped into a corresponding hydrophobicity value, and the resulting hydrophobicity profile was used in place of the primary amino acid sequence. While the ensuing classification accuracy was gratifying, the use of (Rose scale) hydrophobicity values had some disadvantages. These included representing multiple amino acids by the same value, weighting some amino acids more heavily than others, and covering a narrow numerical range, resulting in a poor input for system identification. This paper introduces binary and multilevel sequence codes to represent amino acids, for use in protein classification. The new binary and multilevel sequences, which are still able to encode information such as hydrophobicity, polarity, and charge, avoid the above disadvantages and increase classification accuracy. Indeed, over a much larger test set than in the original study, parallel cascade models using numerical profiles constructed with the new codes achieved slightly higher two-way classification rates than did hidden Markov models (HMMs) using the primary amino acid sequences, and combining PCI and HMM approaches increased accuracy.

Algorithms↗
PubMed1995

Exploration of compact protein conformations using the guided replication Monte Carlo method.

We have studied the use of a new Monte Carlo (MC) chain generation algorithm, introduced by T. Garel and H. Orland [(1990) Journal of Physics A, Vol. 23, pp. L621-L626], for examining the thermodynamics of protein folding transitions and for generating candidate C(alpha) backbone structures as starting points for a de novo protein structure paradigm. This algorithm, termed the guided replication Monte Carlo method, allows a rational approach to the introduction of known "native" folded characteristics as constraints in the chain generation process . We have shown this algorithm to be computationally very efficient in generating large ensembles of candidate C(alpha) chains on the face centered cubic lattice, and illustrate its use by calculating a number of thermodynamic quantities related to protein folding characteristics. In particular, we have used this static MC algorithm to compare such temperature-dependent quantities as the ensemble mean energy, ensemble mean free energy, the heat capacity, and the mean-square radius of gyration. We also demonstrate the use of several simple "guide fields" for introducing protein-specific constraints into the ensemble generation process. Several extensions to our current model are suggested, and applications of the method to other folding related problems are discussed.

Algorithms↗
PubMed1993

A robust, high-sensitivity algorithm for automated detection of proteins in two-dimensional electrophoresis gels.

The automated interpretation of two-dimensional gel electrophoresis images used in protein separation and analysis presents a formidable problem in the detection and characterization of ill-defined spatial objects. We describe in this paper a hierarchical algorithm that provides a robust, high-sensitivity solution to this problem, which can be easily adapted to a variety of experimental situations. The software implementation of this algorithm functions as part of a complete package designed for general protein gel analysis applications.

Algorithms↗
PubMed1992

Special report on taxation. IRS issues stricter guidelines for audits of tax-exempt hospitals.

The new audit guidelines serve as yet another reminder to tax-exempt hospitals that great care must be taken in structuring and documenting business arrangements with physicians and executives so as to withstand scrutiny by the IRS with regard to exempt status. Since increased census and utilization, and enhancement of the hospital's financial position, are no longer acceptable justifications for such activities as physician recruitment incentives (being suggestive of payment for referrals), it is important that hospitals make an effort to ensure that board minutes, recruitment policies, internal memoranda, and other documentation set forth the reasons--other than the benefits to the institution's bottom line--for having entered into such transactions. Hospitals must establish and document a community need for each physician recruited. Hospitals that actively recruit should be armed with studies evaluating recruiting needs in each clinical area, based on objective criteria, taking into consideration managed care contracting needs and the provision of services to the poor and needy. Finally, hospitals should re-examine all joint ventures and other business relationships with physicians to determine whether such arrangements resulted from arm's length negotiation, involve fair market value for goods and services, and conform, insofar as possible, with the Medicare fraud and abuse safe harbor regulations. Under GCM 39862 and the new guidelines, "aggressive" arrangements may not only create exposure under fraud and abuse laws, but could jeopardize the provider's tax-exempt status as well.

Community-Institutional Relations↗
PubMed1989

The Ethics in Patient Referrals Act of 1989: an analysis of the bill and the issue.

It is inherent in the practice of medicine for physicians to refer their patients to hospitals and ancillary health care providers of all types. Suppose, however, that the referring physician owns a financial interest in the provider to which he or she refers patients? Is this practice unethical? Should it be illegal? Many people think it should--including Rep. Fortney H. "Pete" Stark of California. This article examines Rep. Stark's pending legislation to prohibit such arrangements under the Medicare program.

Crime↗