Paul J. Taylor

Bio: Paul J. Taylor is an academic researcher from Lancaster University. The author has contributed to research in topics: Geographic profiling & Poison control. The author has an hindex of 37, co-authored 144 publications receiving 8984 citations. Previous affiliations of Paul J. Taylor include University of Liverpool & Fylde College, Lancaster University.

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

Abstract: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

The experience of work‐related stress across occupations

Abstract: Purpose – To compare the experience of occupational stress across a large and diverse set of occupations. Three stress related variables (psychological well‐being, physical health and job satisfaction) are discussed and comparisons are made between 26 different occupations on each of these measures. The relationship between physical and psychological stress and job satisfaction at an occupational level is also explored.Design/methodology/approach – The measurement tool used is a short stress evaluation tool which provides information on a number of work related stressors and stress outcomes. Out of the full ASSET database 26 occupations were selected for inclusion in this paper.Findings – Six occupations are reporting worse than average scores on each of the factors – physical health, psychological well‐being and job satisfaction (ambulance workers, teachers, social services, customer services – call centres, prison officers and police). Differences across and within occupational groups, for example, teac...

Large‐scale mapping of human protein–protein interactions by mass spectrometry

Abstract: Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24,540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

Work environments, stress and productivity: An examination using ASSET

Abstract: In this study (N = 16,001), the predictors of productivity (i.e., work performance)were investigated with A Shortened Stress Evaluation Tool (E. B. Faragher, C. L. Cooper, & S. Cartwright, 2004), which incorporates individual work stressors, stress outcomes (physical and psychological well-being), and commitment (both to and from an organization). Psychological well-being, commitment from the organization to the employee, and resources were found to be predictive. Physical health, individual work stressors (with the exception of resources), and commitment from the employee to the organization were not identified as important. The indings are discussed with reference to both previous and future research. The large sample size and broad range of occupations included suggest the findings are generalizable to other employee groupings. Implications for both stress and management theory are discussed.

Mass spectrometry-based proteomics

Abstract: Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

An automated method for finding molecular complexes in large protein interaction networks.

Abstract: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE .

The psychological meaning of words: LIWC and computerized text analysis methods

Abstract: We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.

Global analysis of protein localization in budding yeast

Abstract: A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Here we describe the construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins. We classify these proteins, representing 75% of the yeast proteome, into 22 distinct subcellular localization categories, and provide localization information for 70% of previously unlocalized proteins. Analysis of this high-resolution, high-coverage localization data set in the context of transcriptional, genetic, and protein-protein interaction data helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.

Network Medicine: A Network-Based Approach to Human Disease

Abstract: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.