Download or read book Computational Phenotypes written by Sergio Balari and published by Oxford University Press, USA. This book was released on 2013 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written accessibly for both biologists and linguists, argues that language is not as exceptional a human trait as some linguists believe it to be. It is rather, according to the authors, just the human version of a fairly common and conservative organic system, the Central Computational Complex.

Download or read book Computational Phenotypes written by Sergio Balari and published by Oxford University Press. This book was released on 2013 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book about language as a species-typical trait of humans. It argues that language is not so exceptional after all, as according to the authors it is just the human version of a rather common and conservative organic system that they refer to as the Central Computational Complex.

Download or read book Computational Psychiatry written by A. David Redish and published by MIT Press. This book was released on 2016-12-09 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Psychiatrists and neuroscientists discuss the potential of computational approaches to address problems in psychiatry including diagnosis, treatment, and integration with neurobiology. Modern psychiatry is at a crossroads, as it attempts to balance neurological analysis with psychological assessment. Computational neuroscience offers a new lens through which to view such thorny issues as diagnosis, treatment, and integration with neurobiology. In this volume, psychiatrists and theoretical and computational neuroscientists consider the potential of computational approaches to psychiatric issues. This unique collaboration yields surprising results, innovative synergies, and novel open questions. The contributors consider mechanisms of psychiatric disorders, the use of computation and imaging to model psychiatric disorders, ways that computation can inform psychiatric nosology, and specific applications of the computational approach. Contributors Susanne E. Ahmari, Huda Akil, Deanna M. Barch, Matthew Botvinick, Michael Breakspear, Cameron S. Carter, Matthew V. Chafee, Sophie Denève, Daniel Durstewitz, Michael B. First, Shelly B. Flagel, Michael J. Frank, Karl J. Friston, Joshua A. Gordon, Katia M. Harlé, Crane Huang, Quentin J. M. Huys, Peter W. Kalivas, John H. Krystal, Zeb Kurth-Nelson, Angus W. MacDonald III, Tiago V. Maia, Robert C. Malenka, Sanjay J. Mathew, Christoph Mathys, P. Read Montague, Rosalyn Moran, Theoden I. Netoff, Yael Niv, John P. O'Doherty, Wolfgang M. Pauli, Martin P. Paulus, Frederike Petzschner, Daniel S. Pine, A. David Redish, Kerry Ressler, Katharina Schmack, Jordan W. Smoller, Klaas Enno Stephan, Anita Thapar, Heike Tost, Nelson Totah, Jennifer L. Zick

Download or read book Computational Phenotyping Based on Clinical Data and Electronic Health Records for Neurodevelopmental Disorders written by Arezoo Movaghar and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid increase in the generation of digital clinical and medical data created a tremendous interest in using machine learning in medical research. Advanced computational methods have considerable promise for improving the accuracy and efficiency of medical practices and patients' outcomes. In my work, I demonstrate the application of machine learning in improving various stages of patient care through automated population screening, health risk evaluation and informed intervention. First, I developed a fast, easy and cost effective method to screen for carriers of the FMR1 premutation using machine learning models by analyzing five-minute speech samples. The resultant method is fully automated, does not rely on any manual coding and is able to process hundreds of speech samples in a few seconds. Without using any genetic information, the algorithm is able to identify individuals with the FMR1 premutation with a high degree of accuracy. Next, leveraging the electronic health records from the Marshfield Clinic, we created the first population-based FMR1-informed biobank to examine patterns of health problems in individuals with the premutation. We applied machine learning on diagnostic codes to discriminate premutation carriers from the general population. Then we examined individual clinical phenotypes to identify primary phenotypes associated with the FMR1 premutation. Our population-based, unbiased, double-blinded approach enabled us to not only confirm the known phenotypes associated with the premutation, we also discovered new phenotypes that have never been identified as characteristic of these individuals. Knowledge of the clinical risk associated with this genetic variant is critical for premutation carriers, families and clinicians, and has important implications for public health. Finally, I developed a new method to screen "expressed emotion", which is a measure of a family's emotional climate and a key component in predicting relapse in patients with schizophrenia or other disabilities. Our approach replaces the time-consuming, cumbersome and costly process of evaluating expressed emotion manually with a fully automatic framework, which relies on natural language processing and machine learning methods. The ability to rapidly screen expressed emotion in the clinic setting can enable timely psychoeducational intervention for families, leading to lower rates of relapse and more effective treatment in patients.

Download or read book Phenotypes and Genotypes written by Florian Frommlet and published by Springer. This book was released on 2016-02-12 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text presents a comprehensive guide to genetic association, a new and rapidly expanding field that aims to elucidate how our genetic code (genotypes) influences the traits we possess (phenotypes). The book provides a detailed review of methods of gene mapping used in association with experimental crosses, as well as genome-wide association studies. Emphasis is placed on model selection procedures for analyzing data from large-scale genome scans based on specifically designed modifications of the Bayesian information criterion. Features: presents a thorough introduction to the theoretical background to studies of genetic association (both genetic and statistical); reviews the latest advances in the field; illustrates the properties of methods for mapping quantitative trait loci using computer simulations and the analysis of real data; discusses open challenges; includes an extensive statistical appendix as a reference for those who are not totally familiar with the fundamentals of statistics.

Download or read book Computational Phenotyping and Phenome wide Association Studies written by Pedro Luis Teixeira (Jr.) and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning and Validating Clinically Meaningful Phenotypes from Electronic Health Data written by Jessica Lowell Henderson and published by . This book was released on 2018 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-growing adoption of electronic health records (EHR) to record patients' health journeys has resulted in vast amounts of heterogeneous, complex, and unwieldy information [Hripcsak and Albers, 2013]. Distilling this raw data into clinical insights presents great opportunities and challenges for the research and medical communities. One approach to this distillation is called computational phenotyping. Computational phenotyping is the process of extracting clinically relevant and interesting characteristics from a set of clinical documentation, such as that which is recorded in electronic health records (EHRs). Clinicians can use computational phenotyping, which can be viewed as a form of dimensionality reduction where a set of phenotypes form a latent space, to reason about populations, identify patients for randomized case-control studies, and extrapolate patient disease trajectories. In recent years, high-throughput computational approaches have made strides in extracting potentially clinically interesting phenotypes from data contained in EHR systems. Tensor factorization methods have shown particular promise in deriving phenotypes. However, phenotyping methods via tensor factorization have the following weaknesses: 1) the extracted phenotypes can lack diversity, which makes them more difficult for clinicians to reason about and utilize in practice, 2) many of the tensor factorization methods are unsupervised and do not utilize side information that may be available about the population or about the relationships between the clinical characteristics in the data (e.g., diagnoses and medications), and 3) validating the clinical relevance of the extracted phenotypes requires domain training and expertise. This dissertation addresses all three of these limitations. First, we present tensor factorization methods that discover sparse and concise phenotypes in unsupervised, supervised, and semi-supervised settings. Second, via two tools we built, we show how to leverage domain expertise in the form of publicly available medical articles to evaluate the clinical validity of the discovered phenotypes. Third, we combine tensor factorization and the phenotype validation tools to guide the discovery process to more clinically relevant phenotypes.

Download or read book Systems Genetics written by Florian Markowetz and published by Cambridge University Press. This book was released on 2015-07-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.

Download or read book Computational Systems Bioinformatics written by Peter Markstein and published by Imperial College Press. This book was released on 2008 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume contains 29 papers covering many of the latest developments in the fast-growing field of bioinformatics. The contributions span a wide range of topics, including computational genomics and genetics, protein function and computational proteomics, the transcriptome, structural bioinformatics, microarray data analysis, motif identification, biological pathways and systems, and biomedical applications.The papers not only cover theoretical aspects of bioinformatics but also delve into the application of new methods, with input from computation, engineering and biology disciplines. This multidisciplinary approach to bioinformatics gives these proceedings a unique viewpoint of the field.

Download or read book Experimental and Computational Approaches for Genetic Dissection of Complex Phenotypes written by Hani Goodarzi and published by . This book was released on 2010 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning Phenotypes from Electronic Health Records Using Robust Temporal Tensor Factorization written by Kejing Yin and published by . This book was released on 2021 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the widespread adoption of electronic health records (EHR), a large volume of EHR data has been accumulated, providing researchers and clinicians with valuable opportunities to accelerate clinical research and to improve the quality of care by advanced analysis of the EHR data. One approach to transforming the raw EHR to actionable insights is computational phenotyping -- the process of discovering meaningful combinations of clinical items, e.g. diagnosis and medications, from the raw EHR data for characterizing health conditions with minimum human supervision. Many data-driven approaches have been proposed to tackle the problem, among which non-negative tensor factorization (NTF) has been shown effective for high-throughput discovery of phenotypes from structural EHR data. Although great efforts have been made, several open challenges limit the robustness of existing NTF-based computational phenotyping models. (1) The correspondence information between different modalities (e.g., between diagnosis and medication) is often not recorded in EHR data, and existing models rely on unrealistic assumptions to construct input tensors for phenotyping which introduces inevitable errors. (2) EHR data are often recorded over time, presenting serious temporal irregularity: patients have different lengths of stay and the time gap between clinical visits can vary significantly. Existing models are limited in considering the temporal irregularity and temporal dependency, which limits their generalizability and robustness. (3) Heavy missingness is unavoidable in the raw EHR data due to recording mistakes or operational reasons. Existing models mostly do not take the missing data into account and assume that the data are fully observed, which can greatly compromise their robustness. In this thesis research study, we propose a series of robust tensor factorization models to address these challenges. First, we propose a hidden interaction tensor factorization (HITF) model to discover the inter-modal correspondence jointly with the learning of latent phenotypes. It is further extended to the multi-modal setting by the collective hidden interaction tensor factorization (cHITF) framework. Second, we propose a collective non-negative tensor factorization (CNTF) model to extract phenotypes from temporally irregular EHR data and separate phenotypes that appear at different stages of the disease progression. Third, we propose a temporally dependent PARAFAC2 factorization (TedPar) model to further capture the temporal dependency between phenotypes by capturing the transitions between them over time. Forth, we propose a logistic PARAFAC2 factorization (LogPar) model to jointly complete the one-class missing data in the binary irregular tensor and learn phenotypes from it. Finally, we propose context-aware time series imputation (CATSI) to capture the overall health condition of patients and use it to guide the imputation of clinical time series. We empirically validate the proposed models using a number of real-world, largescale, and de-identified EHR datasets. The empirical evaluation results show that the proposed models are significantly more robust than the existing ones. Evaluated by the clinician, HITF and cHITF discovers more clinically meaningful inter-modal correspondence, CNTF learns phenotypes that better separate early and later stages of disease progression, TedPar captures meaningful phenotype transition patterns, and LogPar also derives clinically meaningful phenotypes. Quantitatively, LogPar and CATSI show significant improvement than baselines in tensor completion and time series imputation, respectively. Besides, HITF, cHITF, CNTF, and LogPar all significantly outperform baseline models in terms of downstream prediction tasks.

Download or read book Phenotypes and Endophenotypes written by and published by . This book was released on 2009 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Leveraging Data Science for Global Health written by Leo Anthony Celi and published by Springer Nature. This book was released on 2020-07-31 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

Download or read book Computational Methods for Transcriptome based Cellular Phenotyping written by Matthew Nathan Bernstein and published by . This book was released on 2019 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the basic chemical mechanisms of cellular biology are now well-known, we are still a long way from understanding how phenotypes emerge from these basic mechanisms. Within the last decade, RNA-sequencing (RNA-seq) has become a ubiquitous technology for measuring the transcriptome, which provides a snapshot of gene expression across the entire genome. An improvement in our ability to predict how phenotypes emerge from the complex patterns of gene expression, a task we refer to as transcriptome-based cellular phenotyping (TBCP), would lead to considerable medical and technological advancements. Machine learning promises to be an apt approach for TBCP due to its ability to overcome noise inherent in RNA-seq data and because it does not require a priori knowledge regarding the rules and patterns that lead from gene expression to phenotype. Furthermore, there exist large, public databases of RNA-seq data that promise to be a valuable source of training data for developing machine learning algorithms to perform TBCP. Unfortunately, this opportunity is impeded by a number of challenges inherent in these databases including poorly structured metadata and data heterogeneity. In this thesis, I present three projects that push the state-of-the-art in the ability to leverage the trove of publicly available gene expression data for TBCP. In the first project, we address the problem of poorly structured metadata that exist in public genomics databases. We specifically focus on the Sequence Read Archive (SRA), which is the premiere repository of raw RNA-seq data curated by the National Institutes of Health; however, our work generalizes to other databases. Existing approaches treat metadata normalization as a named entity recognition problem where the goal is to tag metadata with terms from controlled vocabularies when that term is mentioned in the metadata. We reframe this problem as an inference task, in which we tag the metadata with only those terms that describe the underlying biology of the described sample rather than with all mentioned terms. By doing so, we achieve much higher precision than that achieved by existing methods, and maintain a competitive recall. In the second project, we leverage the normalized metadata produced by the first project in order to train predictive models of phenotype from RNA-seq derived gene expression data. We specifically focus on the cell type prediction task: given an RNA-seq sample, we wish to predict the cell type from which the sample was derived. Cell type prediction is an important step in many transcriptomic analyses, including that of annotating cell types in single-cell RNA-seq datasets. This work represents the first effort towards a cell type prediction task that utilizes the full potential of publicly available RNA-seq data. Finally, in the third project, we build on the second project in order to address the task of cell type prediction on sparse single-cell RNA-seq data (scRNA-seq) produced by novel droplet-based technologies. These droplet-based scRNA-seq technologies are enabling the sequencing of higher numbers of cells at the cost of a lower read-depth per cell. Such low read-depths result in fewer genes with detected expression per cell. We explore the effects of applying cell type classifiers trained on dense, bulk RNA-seq data to sparse scRNA-seq data and propose a novel probabilistic generative model for adapting the bulk-trained classifiers to sparse input data.

Download or read book Computational Learning Strategies for Assessing Modular Influences on Biological Phenotypes written by Maria Angels De Luis Balaguer and published by . This book was released on 2013 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Research in Computational Molecular Biology written by Terry Speed and published by Springer. This book was released on 2007-05-18 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2007, held in Oakland, CA, USA in April 2007. The 37 revised full papers address all current issues in algorithmic, theoretical, and experimental bioinformatics.