Novel approaches for tinnitus subphenotyping: evidence synthesis, standardised assessment, and supervised and unsupervised machine learning applications

Clinical management of tinnitus is rather challenging and there is yet no cure for most tinnitus cases. It is speculated that tinnitus heterogeneity is hindering progress in scientific understanding and development of treatments. Phenotyping (i.e., assessment of observable characteristics) and subphenotyping (i.e., subgrouping based on differences in observable characteristics) are important for studying heterogeneous conditions like tinnitus. Identifying and defining clinically relevant tinnitus subphenotypes could help achieve transformational advances in the field. This dissertation reports the application of several advanced methodological approaches and has two main aims. The first aim is to contribute to an international standardisation of tinnitus assessment relevant for tinnitus phenotypic profiling and subphenotyping. The second aim is to further our understanding of tinnitus heterogeneity by investigating the presence of robust subphenotypes, consistent across multiple independent datasets. Two chapters focus on the first aim. Chapter 2 reviews the literature, summarises current knowledge on tinnitus subphenotypes and identifies research gaps. It also summarises methods used so far and presents a novel framework of variable concepts that have been used for tinnitus subphenotyping. Chapter 3 describes the development of a self-report questionnaire intended to be used as a standard for tinnitus phenotyping. This questionnaire was developed through an international collaboration with tinnitus researchers from many centres. The questionnaire is already translated into 9 languages (Albanian, Dutch, French, German, Greek, Italian, Polish, Spanish, and Swedish) and is being used by multiple research teams as a tool for standardised tinnitus assessment. The second aim is addressed in Chapters 4 and 5. Chapter 4 provides a detailed description of three tinnitus-specific datasets that were subsequently analysed in Chapter 5, and highlights commonalities and differences in the studied populations and the collected variables. Chapter 5 describes a novel data-driven approach for discovering tinnitus subphenotypes. This Chapter reports on a comprehensive unsupervised machine learning methodology applied to the three datasets. Findings indicate that this method was able to identify robust tinnitus subphenotypic patterns. Finally, Chapter 6 relates the overall findings to the wider context of the published literature and presents suggestions and recommendations for future research. Age, sex, hearing ability, problems with sounds, symptoms of depression, and mandible problems were highlighted as important variables for tinnitus subphenotyping and should be considered for assessment in future tinnitus studies. Overall, this work provides a basis for standardised tinnitus assessment in future studies and gives novel insights into the characteristics of tinnitus subphenotypes

Novel approaches for tinnitus subphenotyping: evidence synthesis, standardised assessment, and supervised and unsupervised machine learning applications

Clinical management of tinnitus is rather challenging and there is yet no cure for most tinnitus cases. It is speculated that tinnitus heterogeneity is hindering progress in scientific understanding and development of treatments. Phenotyping (i.e., assessment of observable characteristics) and subphenotyping (i.e., subgrouping based on differences in observable characteristics) are important for studying heterogeneous conditions like tinnitus. Identifying and defining clinically relevant tinnitus subphenotypes could help achieve transformational advances in the field. This dissertation reports the application of several advanced methodological approaches and has two main aims. The first aim is to contribute to an international standardisation of tinnitus assessment relevant for tinnitus phenotypic profiling and subphenotyping. The second aim is to further our understanding of tinnitus heterogeneity by investigating the presence of robust subphenotypes, consistent across multiple independent datasets. Two chapters focus on the first aim. Chapter 2 reviews the literature, summarises current knowledge on tinnitus subphenotypes and identifies research gaps. It also summarises methods used so far and presents a novel framework of variable concepts that have been used for tinnitus subphenotyping. Chapter 3 describes the development of a self-report questionnaire intended to be used as a standard for tinnitus phenotyping. This questionnaire was developed through an international collaboration with tinnitus researchers from many centres. The questionnaire is already translated into 9 languages (Albanian, Dutch, French, German, Greek, Italian, Polish, Spanish, and Swedish) and is being used by multiple research teams as a tool for standardised tinnitus assessment. The second aim is addressed in Chapters 4 and 5. Chapter 4 provides a detailed description of three tinnitus-specific datasets that were subsequently analysed in Chapter 5, and highlights commonalities and differences in the studied populations and the collected variables. Chapter 5 describes a novel data-driven approach for discovering tinnitus subphenotypes. This Chapter reports on a comprehensive unsupervised machine learning methodology applied to the three datasets. Findings indicate that this method was able to identify robust tinnitus subphenotypic patterns. Finally, Chapter 6 relates the overall findings to the wider context of the published literature and presents suggestions and recommendations for future research. Age, sex, hearing ability, problems with sounds, symptoms of depression, and mandible problems were highlighted as important variables for tinnitus subphenotyping and should be considered for assessment in future tinnitus studies. Overall, this work provides a basis for standardised tinnitus assessment in future studies and gives novel insights into the characteristics of tinnitus subphenotypes

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

Updating the Lambda modes of a nuclear power reactor

[EN] Starting from a steady state configuration of a nuclear power reactor some situations arise in which the reactor configuration is perturbed. The Lambda modes are eigenfunctions associated with a given configuration of the reactor, which have successfully been used to describe unstable events in BWRs. To compute several eigenvalues and its corresponding eigenfunctions for a nuclear reactor is quite expensive from the computational point of view. Krylov subspace methods are efficient methods to compute the dominant Lambda modes associated with a given configuration of the reactor, but if the Lambda modes have to be computed for different perturbed configurations of the reactor more efficient methods can be used. In this paper, different methods for the updating Lambda modes problem will be proposed and compared by computing the dominant Lambda modes of different configurations associated with a Boron injection transient in a typical BWR reactor. (C) 2010 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia under projects ENE2008-02669 and MTM2007-64477-AR07, the Generalitat Valenciana under project ACOMP/2009/058, and the Universidad Politecnica de Valencia under project PAID-05-09-4285.González Pintor, S.; Ginestar Peiro, D.; Verdú Martín, GJ. (2011). Updating the Lambda modes of a nuclear power reactor. Mathematical and Computer Modelling. 54(7):1796-1801. https://doi.org/10.1016/j.mcm.2010.12.013S1796180154

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig