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James R Green

Publications and source records attributed to James R Green.

7 recordsLinked to original sources
PubMed2006

On the advantages of multi-input single-output parallel cascade classifiers.

Parallel Cascade Identification (PCI) has been successfully applied to build dynamic nonlinear systems that address diverse challenges in the field of bioinformatics. PCI may be used to identify either single-input single-output (SISO) or multi-input single-output (MISO) models. Although SISO PCI models have typically sufficed, it has been suggested that MISO PCI systems could also be used to form bioinformatics classifiers, and indeed they were successfully applied in one study. This paper reports on the first systematic comparison of MISO and SISO PCI classifiers. Motivation for using the MISO structure is given. The construction of MISO parallel cascade models is also briefly reviewed. In order to compare the accuracy of SISO and MISO PCI classifiers, genetic algorithms are applied to optimize the model architecture on a number of equivalent single-input and multi-input biological training datasets. Through evaluation of both model structures on independent test datasets, we establish that MISO PCI is capable of building classifiers of equal accuracy to those resulting from SISO PCI models. Moreover, we discuss and illustrate the benefits of the MISO approach, including significant reduction in training and testing times, and the ability to adjust automatically the weighting of individual inputs according to information content.

Algorithms↗
PubMed2003

Indolophanetetrayne cobalt complexes via Nicholas reactions.

[reaction: see text] Indole N-substituted diyne tetracobalt complexes (4) undergo a Lewis acid mediated dimerization-cyclization reaction through the indole 3-position to afford indolophanetetrayne cobalt complexes (7). Substitution of the indole fragment of (4) with a 3-methyl function allows analogous formation of indolophanetetrayne complex (9), linked through the indole 2-position.

Journal Article↗
PubMed2003

Peroxynitrite and NO+ donors form colored nitrite adducts with sinapinic acid: potential applications.

Sinapinic acid (3,5-dimethoxy-4-hydroxycinnamic acid, SA) reacted with peroxynitrous acid at neutral pH with a second-order rate constant of 812 M(-1)s(-1), to yield a red product (lambda(max), 532 nm). The identical colored product could be formed with acidified decomposed peroxynitrous acid solutions or nitrite at slower rates (0.1M HCl, 8.32 M(-1)s(-1); 10% acetic acid, 0.0004 M(-1)s(-1)). The red compound is thought to be O-nitrososinapinic acid (3,5-dimethoxy-4-nitrosooxycinnamic acid) which can be formed by reaction with either peroxynitrous acid or nitrous acid. The extinction coefficient of O-nitrososinapinic acid (ONSA) was estimated to be 8419 M(-1)cm(-1) at 510 nm in 10% acetic acid and 90% acetonitrile. ONSA was also formed via NO(+) transfer from S-nitrosoglutathione (GSNO). ONSA in turn can S-nitrosate low molecular weight thiols and protein thiols. SA was also shown to act as a peroxynitrite sink as it effectively prevented the oxidation of dihydrorhodamine under physiological conditions. The fact that O-nitrososinapinic acid is stable and can be used to S-nitrosate thiol containing amino acids, peptides, and proteins makes it a potentially useful reagent in the study of S-nitrosothiol biochemistry and physiology. In addition, the relatively high extinction coefficient of O-nitrososinapinic acid means that it could be utilized as an analyte for the spectroscopic detection of peroxynitrite or NO(+)-donors in the submicromolar range.

Coumaric Acids↗
PubMed2003

Recognition of adenosine triphosphate binding sites using parallel cascade system identification.

Parallel cascade identification (PCI) is a method for approximating the behavior of a nonlinear system, from input/output training data, by constructing a parallel array of cascaded dynamic linear and static nonlinear elements. PCI has previously been shown to provide an effective means for classifying protein sequences into structure/function families. In the present study, PCI is used to distinguish proteins that are binding to adenosine triphosphate or guanine triphosphate molecules from those that are nonbinding. Classification accuracy of 87.1% using the hydrophobicity scale of Rose et al. (Hydrophobicity of amino acid residues in globular proteins. Science 229:834-838, 1985), and 88.8% using Korenberg's SARAH1 scale, are obtained, as measured by tenfold cross-validation testing. Nearest-neighbor and K-nearest-neighbor (KNN) classifiers are constructed, and the resulting accuracy is, respectively, 88.0% and 90.8% on the SARAH1-encoded test data set, as measured by the above testing protocol. Significantly improved classification accuracy is achieved by combining PCI and KNN classifiers using quadratic discriminant analysis: accuracy rises from 87.9% (PCI) and 87.4% (KNN) to 96.5% for the combination, as measured by twofold cross-validation testing on the SARAH1-encoded test data set.

Adenosine Triphosphate↗
PubMed2002

Parallel cascade recognition of exon and intron DNA sequences.

Many of the current procedures for detecting coding regions on human DNA sequences combine a number of individual techniques such as discriminant analysis and neural net methods. Recent papers have used techniques from nonlinear systems identification, in particular, parallel cascade identification (PCI), as one means for classifying protein sequences into their structure/function groups. In the present paper, PCI is used in a pilot study to distinguish exon (coding) from intron (noncoding; interspersed within genes) human DNA sequences. Only the first exon and first intron sequences with known boundaries in genomic DNA from the beta T-cell receptor locus were used for training. Then, the parallel cascade classifiers were able to achieve classification rates of about 89% on novel sequences in a test set, and averaged about 82% when results of a blind test were included. In testing over a much wider range of human nucleotide sequences, PCI classifiers averaged 83.6% correct classifications. These results indicate that parallel cascade classifiers may be useful components in future coding region detection programs.

Algorithms↗