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University of Texas MD Anderson Cancer Center
Statistical Issues in Proteomic Research
Jeffrey S. Morris Thu, 31 Jul 2008 11:03:24 -0700
Microproteomics: Analysis of protein diversity in small samples
Howard B. Gutstein Fri, 13 Jun 2008 14:38:53 -0700
Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The effects of miniaturization on these analyses are highlighted using neuroscience-related examples, as are statistical issues unique to the high-dimensional datasets generated by proteomic experiments.
Pinnacle: A Fast, Automatic Method for Detecting and Quantifying Protein Spots in 2-Dimensional Gel Electrophoresis Data
Jeffrey S. Morris Tue, 04 Dec 2007 09:44:53 -0800
Motivation: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust, and reproducible methods for detecting, matching, and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels, and finally quantifying each spot by calculating normalized spot volumes. This approach is time con-suming, error-prone, and frequently requires extensive manual edit-ing, which can unintentionally introduce bias into the results.Results: We introduce a new method for spot detection and quanti-fication called Pinnacle that is automatic, quick, sensitive and spe-cific, and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller CVs. More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences.Availability: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.
Laser capture sampling and analytical issues in proteomics
Howard Gutstein Tue, 04 Dec 2007 09:35:54 -0800
Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for the analysis of the highly complex datasets generated are also reviewed.
Statistical contributions to proteomic research
Jeffrey S. Morris Wed, 04 Apr 2007 12:55:09 -0700
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.
Wavelet-based functional mixed model analysis: Computational considerations
Richard C. Herrick Wed, 04 Apr 2007 12:48:45 -0700
Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in terms of model and data set parameters. We present examples and benchmarks demonstrating that it is practical to analyze very large data sets with readily available computing resources. This code is freely available on our website.
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Statistical Issues in Proteomic Research
Jeffrey S. Morris Thu, 31 Jul 2008 11:03:24 -0700
Microproteomics: Analysis of protein diversity in small samples
Howard B. Gutstein Fri, 13 Jun 2008 14:38:53 -0700
Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The effects of miniaturization on these analyses are highlighted using neuroscience-related examples, as are statistical issues unique to the high-dimensional datasets generated by proteomic experiments.
Pinnacle: A Fast, Automatic Method for Detecting and Quantifying Protein Spots in 2-Dimensional Gel Electrophoresis Data
Jeffrey S. Morris Tue, 04 Dec 2007 09:44:53 -0800
Motivation: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust, and reproducible methods for detecting, matching, and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels, and finally quantifying each spot by calculating normalized spot volumes. This approach is time con-suming, error-prone, and frequently requires extensive manual edit-ing, which can unintentionally introduce bias into the results.Results: We introduce a new method for spot detection and quanti-fication called Pinnacle that is automatic, quick, sensitive and spe-cific, and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller CVs. More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences.Availability: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.
Laser capture sampling and analytical issues in proteomics
Howard Gutstein Tue, 04 Dec 2007 09:35:54 -0800
Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for the analysis of the highly complex datasets generated are also reviewed.
Statistical contributions to proteomic research
Jeffrey S. Morris Wed, 04 Apr 2007 12:55:09 -0700
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.
Wavelet-based functional mixed model analysis: Computational considerations
Richard C. Herrick Wed, 04 Apr 2007 12:48:45 -0700
Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in terms of model and data set parameters. We present examples and benchmarks demonstrating that it is practical to analyze very large data sets with readily available computing resources. This code is freely available on our website.
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ABC's of Lung Cancer: Information about the condition and its treatment.Altruis Biomedical Network: Lung Cancer: Provides information about the causes, symptoms, diagnosis and treatment of this disease.
Cancer Bulletin: Lung Cancer: Health summary which outlines causes and effects, incidence, treatment and outcomes.
Cancer Treatment Centers of America: Lung Cancer: At Cancer Treatment Centers of America, we use many tools to help you fight lung cancer on all fronts. To learn more about our treatment options, visit our site today.
CancerBACUP: Lung Cancer Information Centre: Information about lung cancer, including causes, symptoms, diagnosis, treatments, side effects and further resources.
eMedicine Health: Lung Cancer: Learn about lung cancer - causes, signs, symptoms and treatment of the leading cause of cancer deaths in the U.S. and the world on eMedicineHealth.com
Gail P. Ramos Lung Cancer Foundation: Dedicated to fighting lung cancer through research, education, and assistance programs.
Imperial Cancer Research Fund: This section of CancerHelp UK is about lung cancer (cancer of the bronchus or bronchial cancer).It includes symptoms and causes, tests for lung cancer, treatment, living with lung cancer, and current research.
Information, Stories and More: Provides access to resources for those dealing with lung cancer.
Louisiana Cancer and Lung Trust Fund Board: Cancer control and cardio-pulmonary diseases with statistics, screening and public health in Louisiana.
Lung Cancer Dissertation: Lung Cancer Dissertation by Will Roberts, Medical Student at Leicester University
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Lung Cancer Online: LungCancerOnline.org is a directory of information and resources for lung cancer patients and their families. The Lung Cancer Online Foundation provides funding for lung cancer research.
Lung Cancer Ranks Among Deadliest, Most Neglected Cancers: By Troy Goodman CNN.com Health Writer
Lung Cancer Resources Directory: Information and links about causes and prevention, molecular biology and specialist organizations.
Lung Cancer Screening: Learn about Lung Cancer Screening at Boca Raton Community Hospital
Lung Cancer Support Community: Lung Cancer Support Community, Message Board, Information, Treatment, Diagnosis, Support for survivors and caregivers.
M. D. Anderson Cancer Center: Lung Cancer: Information on lung cancer symptoms, diagnosis and treatment, and lung cancer patient support.
Marcia Lemkin Foundation: The Marcia Lemkin Foundation to Conquer Lung Cancer is is dedicated to the memory of Marcia, a wife, a mother and friend.
Mayo Clinic: Lung Cancer: Lung cancer is a major cause of cancer deaths. Yet it's highly preventable.
Medicine World: Lung Cancer: Comprehensive information on lung cancer, basic information on small cell lung cancer, non-small cell lung cancer, what is my lung cancer risk? Your gateway to the world of lung cancer.
MedicineNet: Lung Cancer: Get the facts on lung cancer treatment, diagnosis, staging, causes (smoking), types (adenocarcinoma, squamous cell), prevention, symptoms, warning signs and survival rates.
Oncology for Primary Care Physicians: Lung Cancer: Prevention, early detection, diagnosis, management, and case studies of lung cancer for physicians in general practice.
RadiologyInfo: Lung Cancer: Current and accurate information for patients about Lung Cancer. Learn about treatment options, how the therapy is performed, what happens during therapy, common side effects and risks.
Rhode Island Cancer Council: Lung Cancer: Cancer fact sheet describing lung cancer and how it can be detected.
Tobacco.org: Lung Cancer: Updated collection of news articles about tobacco use, lung cancer, research, and treatment.
Understanding Lung Cancer: Understanding Lung Cancer - A guide for people with lung cancer, their families and friends.
What You Need to Know about Lung Cancer: Information about detection, symptoms, diagnosis, and treatment of lung cancer. NIH Publication No. 07-1553
Yahoo Health: Lung Cancer: Information including symptoms, diagnosis, staging, treatment and clinical trials.
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