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Richard S. Judson

Bio: Richard S. Judson is an academic researcher from United States Environmental Protection Agency. The author has contributed to research in topics: High-Throughput Screening Assays & Population. The author has an hindex of 69, co-authored 220 publications receiving 22147 citations. Previous affiliations of Richard S. Judson include Sandia National Laboratories & University of Houston.


Papers
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Journal ArticleDOI
10 Feb 2000-Nature
TL;DR: Examination of large-scale yeast two-hybrid screens reveals interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes.
Abstract: Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.

4,877 citations

Journal ArticleDOI
TL;DR: This work simulates an apparatus that learns to excite specified rotational states in a diatomic molecule and uses a learning procedure to direct the production of pulses based on fitness'' information provided by a laboratory measurement device.
Abstract: We simulate a method to teach a laser pulse sequences to excite specified molecular states. We use a learning procedure to direct the production of pulses based on ``fitness'' information provided by a laboratory measurement device. Over a series of pulses the algorithm learns an optimal sequence. The experimental apparatus, which consists of a laser, a sample of molecules and a measurement device, acts as an analog computer that solves Schr\"odinger's equation n/Iexactly, in real time. We simulate an apparatus that learns to excite specified rotational states in a diatomic molecule.

1,426 citations

Journal ArticleDOI
TL;DR: The results indicate that the unique interactions of multiple SNPs within a haplotype ultimately can affect biologic and therapeutic phenotype and that individual SNPs may have poor predictive power as pharmacogenetic loci.
Abstract: The human β2-adrenergic receptor gene has multiple single-nucleotide polymorphisms (SNPs), but the relevance of chromosomally phased SNPs (haplotypes) is not known. The phylogeny and the in vitro and in vivo consequences of variations in the 5′ upstream and ORF were delineated in a multiethnic reference population and an asthmatic cohort. Thirteen SNPs were found organized into 12 haplotypes out of the theoretically possible 8,192 combinations. Deep divergence in the distribution of some haplotypes was noted in Caucasian, African-American, Asian, and Hispanic-Latino ethnic groups with >20-fold differences among the frequencies of the four major haplotypes. The relevance of the five most common β2-adrenergic receptor haplotype pairs was determined in vivo by assessing the bronchodilator response to β agonist in asthmatics. Mean responses by haplotype pair varied by >2-fold, and response was significantly related to the haplotype pair (P = 0.007) but not to individual SNPs. Expression vectors representing two of the haplotypes differing at eight of the SNP loci and associated with divergent in vivo responsiveness to agonist were used to transfect HEK293 cells. β2-adrenergic receptor mRNA levels and receptor density in cells transfected with the haplotype associated with the greater physiologic response were ≈50% greater than those transfected with the lower response haplotype. The results indicate that the unique interactions of multiple SNPs within a haplotype ultimately can affect biologic and therapeutic phenotype and that individual SNPs may have poor predictive power as pharmacogenetic loci.

997 citations

Journal ArticleDOI
20 Jul 2001-Science
TL;DR: Pairs of SNPs exhibited variability in the degree of linkage disequilibrium that was a function of their location within a gene, distance from each other, population distribution, and population frequency.
Abstract: Variation within genes has important implications for all biological traits. We identified 3899 single nucleotide polymorphisms (SNPs) that were present within 313 genes from 82 unrelated individuals of diverse ancestry, and we organized the SNPs into 4304 different haplotypes. Each gene had several variable SNPs and haplotypes that were present in all populations, as well as a number that were population-specific. Pairs of SNPs exhibited variability in the degree of linkage disequilibrium that was a function of their location within a gene, distance from each other, population distribution, and population frequency. Haplotypes generally had more information content (heterozygosity) than did individual SNPs. Our analysis of the pattern of variation strongly supports the recent expansion of the human population.

842 citations

Journal ArticleDOI
Leming Shi1, Gregory Campbell1, Wendell D. Jones, Fabien Campagne2  +198 moreInstitutions (55)
TL;DR: P predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans are generated.
Abstract: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

753 citations


Cited by
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Journal ArticleDOI
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

17,647 citations

Journal Article
Fumio Tajima1
30 Oct 1989-Genomics
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.

11,521 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

9,441 citations