A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&...
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A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing
The importance of optimizing the ability to penetrate blood-brain barrier of potential drug candidates is now widely recognized. Accurate computational prediction of such a properly will significantly enhance the spee...
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The importance of optimizing the ability to penetrate blood-brain barrier of potential drug candidates is now widely recognized. Accurate computational prediction of such a properly will significantly enhance the speed of the blood-brain barrier penetration analysis and reduce the cost of drug discovery. In this paper, we present some results of our predictive model built on the stochastic discrimination, a pattern classification method that has been shown to be a useful in the literature.
Tandem mass spectrometry (MS/MS) combined with protein database searching has been widely used in protein identification. A validation procedure is generally required to reduce the number of false positives. Advanced ...
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Tandem mass spectrometry (MS/MS) combined with protein database searching has been widely used in protein identification. A validation procedure is generally required to reduce the number of false positives. Advanced tools using statistical and machine learning approaches may provide faster and more accurate validation than manual inspection and empirical filtering criteria. In this study, we use two feature selection algorithms based on random forest and support vector machine to identify peptide properties that can be used to improve validation models. We demonstrate that an improved model based on an optimized set of features reduces the number of false positives by 58% relative to the model which used only search engine scores, at the same sensitivity score of 0.8. In addition, we develop classification models based on the physicochemical properties and protein sequence environment of these peptides without using search engine scores. The performance of the best model based on the support vector machine algorithm is at 0.8 AUC, 0.78 accuracy, and 0.7 specificity, suggesting a reasonably accurate classification. The identified properties important to fragmentation and ionization can be either used in independent validation tools or incorporated into peptide sequencing and database search algorithms to improve existing software programs.
This paper reports on the results of experiments regarding a biomedical data set describing blood-brain barrier penetration ability of molecules. In this data set 415 cases represent organic compounds with known stead...
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This paper reports on the results of experiments regarding a biomedical data set describing blood-brain barrier penetration ability of molecules. In this data set 415 cases represent organic compounds with known stead...
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This paper reports on the results of experiments regarding a biomedical data set describing blood-brain barrier penetration ability of molecules. In this data set 415 cases represent organic compounds with known steady-state concentrations of a drug in the brain and blood. In our experiments we used two different discretization algorithms, based on agglomerative and divisive approaches of cluster analysis, respectively, and two different approaches to missing attribute values: deletion of cases with missing attribute values and deletion of attributes with missing values. Using ten-fold cross validation we concluded that the best strategy is based on a divisive approach of cluster analysis and deleting cases affected by missing attribute values. Moreover, prediction accuracy of this strategy is comparable with the other successful approaches reported in this area.
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