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You searched for +publisher:"University of Manchester" +contributor:("STEVENS, ROBERT RD"). Showing records 1 – 3 of 3 total matches.

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University of Manchester

1. Florez Vargas, Oscar Roberto. DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY.

Degree: 2016, University of Manchester

The idea that the same experimental findings can be reproduced by a variety of independent approaches is one of the cornerstones of science’s claim to objective truth. However, in recent years, it has become clear that science is plagued by findings that cannot be reproduced and, consequently, invalidating research studies and undermining public trust in the research enterprise. The observed lack of reproducibility may be a result, among other things, of the lack of transparency or completeness in reporting. In particular, omissions in reporting the technical nature of the experimental method make it difficult to verify the findings of experimental research in biomedicine. In this context, the assessment of scientific reports could help to overcome – at least in part – the ongoing reproducibility crisis.In addressing this issue, this Thesis undertakes the challenge of developing strategies for the evaluation of reporting biomedical experimental methods in scientific manuscripts. Considering the complexity of experimental design – often involving different technologies and models, we characterise the problem in methods reporting through domain-specific checklists. Then, by using checklists as a decision making tool, supported by miniRECH – a spreadsheet-based approach that can be used by authors, editors and peer-reviewers – a reasonable level of consensus on reporting assessments was achieved regardless of the domain-specific expertise of referees. In addition, by using a text-mining system as a screening tool, a framework to guide an automated assessment of the reporting of bio-experiments was created. The usefulness of these strategies was demonstrated in some domain-specific scientific areas as well as in mouse models across biomedical research.In conclusion, we suggested that the strategies developed in this work could be implemented through the publication process as barriers to prevent incomplete reporting from entering the scientific literature, as well as promoters of completeness in reporting to improve the general value of the scientific evidence. Advisors/Committee Members: STEVENS, ROBERT RD, Brass, Andrew, Stevens, Robert.

Subjects/Keywords: Reproducibility; Checklists; Text Mining; Biomedicine

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Florez Vargas, O. R. (2016). DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324

Chicago Manual of Style (16th Edition):

Florez Vargas, Oscar Roberto. “DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY.” 2016. Doctoral Dissertation, University of Manchester. Accessed March 06, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324.

MLA Handbook (7th Edition):

Florez Vargas, Oscar Roberto. “DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY.” 2016. Web. 06 Mar 2021.

Vancouver:

Florez Vargas OR. DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2021 Mar 06]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324.

Council of Science Editors:

Florez Vargas OR. DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324


University of Manchester

2. Sechidis, Konstantinos. Hypothesis Testing and Feature Selection in Semi-Supervised Data.

Degree: 2015, University of Manchester

A characteristic of most real world problems is that collecting unlabelled examples is easier and cheaper than collecting labelled ones. As a result, learning from partially labelled data is a crucial and demanding area of machine learning, and extending techniques from fully to partially supervised scenarios is a challenging problem. Our work focuses on two types of partially labelled data that can occur in binary problems: semi-supervised data, where the labelled set contains both positive and negative examples, and positive-unlabelled data, a more restricted version of partial supervision where the labelled set consists of only positive examples. In both settings, it is very important to explore a large number of features in order to derive useful and interpretable information about our classification task, and select a subset of features that contains most of the useful information.In this thesis, we address three fundamental and tightly coupled questions concerning feature selection in partially labelled data; all three relate to the highly controversial issue of when does additional unlabelled data improve performance in partially labelled learning environments and when does not. The first question is what are the properties of statistical hypothesis testing in such data? Second, given the widespread criticism of significance testing, what can we do in terms of effect size estimation, that is, quantification of how strong the dependency between feature X and the partially observed label Y? Finally, in the context of feature selection, how well can features be ranked by estimated measures, when the population values are unknown? The answers to these questions provide a comprehensive picture of feature selection in partially labelled data. Interesting applications include for estimation of mutual information quantities, structure learning in Bayesian networks, and investigation of how human-provided prior knowledge can overcome the restrictions of partial labelling.One direct contribution of our work is to enable valid statistical hypothesis testing and estimation in positive-unlabelled data. Focusing on a generalised likelihood ratio test and on estimating mutual information, we provide five key contributions. (1) We prove that assuming all unlabelled examples are negative cases is sufficient for independence testing, but not for power analysis activities. (2) We suggest a new methodology that compensates this and enables power analysis, allowing sample size determination for observing an effect with a desired power by incorporating user’s prior knowledge over the prevalence of positive examples. (3) We show a new capability, supervision determination, which can determine a-priori the number of labelled examples the user must collect before being able to observe a desired statistical effect. (4) We derive an estimator of the mutual information in positive-unlabelled data, and its asymptotic distribution. (5) Finally, we show how to rank features with and without prior knowledge. Also we derive extensions of… Advisors/Committee Members: STEVENS, ROBERT RD, Brown, Gavin, Stevens, Robert.

Subjects/Keywords: Machine Learning; Information Theory; Feature Selection; Hypothesis Testing; Semi Supervised; Positive Unlabelled; Mutual Information

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sechidis, K. (2015). Hypothesis Testing and Feature Selection in Semi-Supervised Data. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415

Chicago Manual of Style (16th Edition):

Sechidis, Konstantinos. “Hypothesis Testing and Feature Selection in Semi-Supervised Data.” 2015. Doctoral Dissertation, University of Manchester. Accessed March 06, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415.

MLA Handbook (7th Edition):

Sechidis, Konstantinos. “Hypothesis Testing and Feature Selection in Semi-Supervised Data.” 2015. Web. 06 Mar 2021.

Vancouver:

Sechidis K. Hypothesis Testing and Feature Selection in Semi-Supervised Data. [Internet] [Doctoral dissertation]. University of Manchester; 2015. [cited 2021 Mar 06]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415.

Council of Science Editors:

Sechidis K. Hypothesis Testing and Feature Selection in Semi-Supervised Data. [Doctoral Dissertation]. University of Manchester; 2015. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:277415


University of Manchester

3. Turner, Emily. Predictive Variable Selection for Subgroup Identification.

Degree: 2017, University of Manchester

The problem of exploratory subgroup identification can be broken down into three steps. The first step is to identify predictive features, the second is to identify the interesting regions on those features, and the third is to estimate the properties of the subgroup region, such as subgroup size and the predicted recovery outcome for individuals belonging to this subgroup. While most work in this field analyses the full subgroup identification procedure, we provide an in-depth examination of the first step, predictive feature identification. A feature is defined as predictive if it interacts with a treatment to affect the recovery outcome. We compare three prominent methods for exploratory subgroup identification: Vir- tual Twins (Foster et al. 2011), SIDES (Subgroup Identification based on Differential Effect Search, Lipkovich et al. 2011) and GUIDE (Generalised, Unbiased Interaction Detection and Estimation, Loh et al. 2015). First, we provide a theoretical interpretation of the problem of predictive variable selection and connect it with the three methods. We believe that bringing different approaches under a common analytical framework facilitates a clearer comparison of each. We show that Virtual Twins and SIDES select interesting features in a theoretically similar way, so that the essential difference between the two is in the way in which this selection mechanism is implemented in their respective subgroup identification procedures. Second, we undertake an experimental analysis of the three. In order to do this, we apply each method to return a predictive variable importance measure (PVIMs), which we use to rank features in order of their predictiveness. We then evaluate and compare how well each method performs at this task. Although each of Virtual Twins, SIDES and GUIDE either output a PVIM or require minor adaptations to do so, their strengths and weaknesses as PVIMs had not been explored prior to this work. We argue that a variable ranking approach is a particularly good solution to the problem of subgroup identification. Because clinical trials often lack the power to identify predictive features with statistical significance, predictive variable scoring and ranking may be more appropriate than a full subgroup identification procedure. PVIMs enable a clinician to visualise the relative importance of each feature in a straightforward manner and to use clinical expertise to scrutinise the findings of the algorithm. Our conclusions are that Virtual Twins performs best in terms of predictive feature selection, outperforming SIDES and GUIDE on every type of data set. However, it appears to have weaknesses in distinguishing between predictive and prognostic biomarkers. Finally, we note that there is a need to provide common data sets on which new methods can be evaluated. We show that there is a tendency towards testing new subgroup identification methods on data sets that demonstrate the strengths of the algorithm and hide its weaknesses. Advisors/Committee Members: STEVENS, ROBERT RD, Brown, Gavin, Stevens, Robert.

Subjects/Keywords: Subgroup identification; Interaction detection; Recursive partitioning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Turner, E. (2017). Predictive Variable Selection for Subgroup Identification. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697

Chicago Manual of Style (16th Edition):

Turner, Emily. “Predictive Variable Selection for Subgroup Identification.” 2017. Doctoral Dissertation, University of Manchester. Accessed March 06, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697.

MLA Handbook (7th Edition):

Turner, Emily. “Predictive Variable Selection for Subgroup Identification.” 2017. Web. 06 Mar 2021.

Vancouver:

Turner E. Predictive Variable Selection for Subgroup Identification. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2021 Mar 06]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697.

Council of Science Editors:

Turner E. Predictive Variable Selection for Subgroup Identification. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312697

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