1,721,190 research outputs found
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Capturing Hidden Signals From High-Dimensional Data and Applications to Genomics
Full text linkThe analysis of high-dimensional data, albeit challenging owing to various computational and statistical aspects, often provides opportunities to uncover hidden signals by leveraging inherent structure in the data. In the context of genomics, where molecular markers are probed at ever-increasing resolution and throughput, large sets of features that follow specific patterns, in conjunction with large sample sizes, allow us to implement richer and more sophisticated models than before in attempt to extract signal that is not immediately evident from the data. Particularly, genomic markers are often affected by multiple genetic and environmental factors, they may differ in their regulation and presentation in different tissues, cell types, conditions, or over time, and some markers may affect multiple biological processes; unveiling those signals is likely to be pivotal in advancing our understanding of complex biology and disease. This dissertation introduces novel computational methodologies and theory that address several key challenges faced in the analysis of high-dimensional genomic data coming from heterogeneous sources ("bulk" genomics) with a particular focus on DNA methylation data. Through a range of simulations and the analysis of multiple data sets, we demonstrate that our proposed methods provide opportunities to conduct powerful and statistically sound population-level studies at an unprecedented resolution and scale
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Computational Methods for the Imputation and Prediction of Digital Health Data
Full text linkAdvances in both technology and medicine have enabled monumental progress toward the realization of precision medicine. In particular, machine learning algorithms -- powered by electronic health records, genomic information, wearable sensors, and medical images -- are positioned to become an integral part of the clinical workflow. While a tremendous amount of biomedical data is being generated and collected on a daily basis, plenty of data are still not routinely captured due to invasiveness, inconvenience, or cost. In this dissertation, we first describe the development and validation of a machine learning model that uses pre-operative data readily available in the electronic health record to predict post-operative in-hospital mortality. We then present multiple novel computational methods for accurately imputing unobserved health data using several different types of observed data, including physiological waveforms, genomics, and videos
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Spatiotemporal Modeling of Microbial Communities
Full text linkMicrobial communities can undergo rapid changes, that can both cause and indicate host disease, renderinglongitudinal microbiome studies key for understanding microbiome-associated disorders. However, moststandard statistical methods, based on random samples, are not applicable for addressing the methodologicaland statistical challenges associated with repeated, structured observations of a complex ecosystem.Therefore, to elucidate how and why our microbiome varies in time, and whether these trajectories areconsistent across humans, we developed new methods for modeling the temporal and spatial dynamics ofmicrobial communities. We developed a method to identify ‘time-dependent’ microbes (Shenhav et al.,PLoS Computational Biology 2019) and showed that their temporal patterns differentiate between thedeveloping microbial communities of infants and those of adults. We also developed models to deconvolutethe dynamics of microbial community formation. Using these methods, we found significant differencesbetween vaginally- and cesarean-delivered infants in terms of initial colonization and succession of theirgut microbial community (Shenhav et al., Nature Methods 2019) as well as the trajectories of thesecommunities in the first years of life (Martino*, Shenhav* et al., Nature Biotechnology). These models,designed to identify and predict time-dependent patterns, will help researchers better understand thetemporal nature of the human microbiome from the time of its formation at birth and throughout life
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Leveraging replicable sources of variability to increase power and interpretability in analyses of genomic datasets
Full text linkMany types of genomic datasets—including RNA sequencing (RNAseq) and DNA methylation—are influenced by innumerable sources of variability. Frequently, analyses of such variability focus on local effects due to genetics, often overlooking the components of variability related to context-level, individual-level, or environmental effects. Here, we leverage the idea that sources of variability are often conserved across genomic datasets to propose two approaches to partition variability: first into distinct biological and technical components, and second into orthogonal context-specific and context-shared genetic components. Using our methods, we perform more powerful and interpretable genomic association studies (such as transcriptome- or epigenome-wide association studies), and we uncover that heritability is more context-specific at the level of single-cell RNAseq, whereas it is more context-shared at the level of bulk (tissue) RNAseq. Subsequently, we perform an analysis of medical records to elucidate the informativeness and impacts of multiple genomics data types on phenotype imputation tasks. We show that risk scores derived from one’s methylation are more informative than risk scores derived from one’s genotypes in imputation tasks. The work presented here shows lasting impact on the design of multiple classes of genomic association studies as well as studies of the utility of genomic biomarkers in electronic medical records
Promoting Harmonious Relations and Equitable Well-Being: Peace Psychology and “Intractable” Conflicts
No full textThe chapter explores Bar-Tal’s legacy in relation to key concepts, perspectives, and findings that comprise the growing field of peace psychology, specifically the promotion of sustainable peace through the indivisible constructs of harmonious relations and equitable wellbeing. Analyzed through a peace psychology lens, Bar-Tal’s work highlights both the barriers to and bridges for achieving sustainable peace. Central concepts from his work, such as fear, insecurity, and an ethos of conflict, demonstrate key obstacles to fostering harmonious intergroup relations based on social justice. Bar-Tal’s work also identifies processes that can overcome these barriers, which is consistent with peace psychology’s emphasis on the development of constructive responses to violence and conflict. For example, the chapter outlines how confidence-building mechanisms, mutually respectful identities, and reconciliation processes, may help foster an ethos of peace that can be embedded in the structure of societies through peace education. The chapter concludes with implications and suggestions for future research, with a focus on the role of young people in settings of prolonged intergroup division and generational approaches to peacebuilding, as conceptualized through a peace psychology lens
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Computational approaches for metagenomic analysis of the microbiome
Full text linkThe microbiome is a community of microorganisms living in our bodies and throughout the environment. The genomic data researchers can extract from microbiomes, known as metagenomic data, can be used to predict traits about a host or environment. By identifying microbiome biomarkers associated with disease or health, researchers can develop better therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Here, we evaluate methods to remove background noise due to technical variables unrelated to the phenotype of interest, such as sequencing protocol, and thereby improving our ability to find accurate biomarkers of human disease. Also crucial in understanding host health is elucidating the sources of their microbiomes, as it allows researchers to understand the dynamics behind how microbial communities form and how they respond to changing environments. In this work, we introduce a method to use metagenomic variants obtained from hundreds of species in microbiome data to perform source tracking, which is a method of estimating colonization sources for a sample of interest. These analyses shed light on phenomena like the colonization of the early infant gut microbiome, or spatial patterns in the ocean microbiomes around the world. Lastly, we analyze metagenomic data to understand how genetic diversity changes along the human gut on the species, strain and gene level. In sum, this work leverages the genomic information contained in our microbiomes to find universal patterns in microbiomes, allowing us to better understand the relationship between microbiome and phenotypes, the colonization sources of microbiomes, and also the colonization dynamics on the species and strain level
Dynamics of emotions in protracted intergroup conflict as microfoundations for violent action : insights for conflict transformation from the Palestinian territories
Full text linkLiving within prolonged intergroup conflict has detrimental psychosocial and societal
consequences, especially for members of low-power groups. Experiencing repression creates intense
emotions and raises serious dilemmas about handling resistance to achieve social change. In recent
years, novel approaches that focus on microlevel factors, particularly emotions, have been suggested
as useful predictors to understand how and why violent conflicts persist. Details of the exact
dynamics between emotions and collective action, such as how emotional mechanisms predict
violent action under different types of conflict escalation, remain an open question. Despite the
theoretical and practical importance of the subject, limited data is available from a low-power group
perspective. In this dissertation research, I investigate how emotional mechanisms predict how –
mainly violent – collective action is moderated by different types of conflict escalation. These
insights inform and support conflict transformation from a psychological perspective.
The research is based on extensive longitudinal mixed methods fieldwork in Israel and the
Palestinian Territories over three years. To contextually comprehend the complex issues, I first
‘mapped the space' between emotions and action, using explorative participatory-observation. Then,
to investigate the exact mechanisms of these interrelations, particularly how emotions predict violent
action under different conflict escalation settings, I surveyed two samples of West Bank Palestinians
(N = 200, 450) before and during different escalations using a longitudinal design. Escalation
contexts included the US embassy's highly publicized move to Jerusalem which led to widespread
unrest in Palestine, the so-called 'Gaza Marches of Return', and a full lockdown of Ramallah by the
Israeli army. Particular focus was placed on negative high-agency emotions such as anger,
humiliation, and hate, as well as on the distinction between individual- versus group emotions.
Finally, using activist narratives, I outlined how – in the light of these escalatory interrelations –
constructive social change from violence to nonviolent action is possible.
Results confirmed an oppressive conflict reality for low-power group members, in which
years of standstill alternate with acute phases of conflict escalation. The participatory data showed
how people employ agentic coping patterns similar to established interpersonal conflict styles.
Situational context such as conflict escalation substantially affects how and which specific emotional
dynamics predict violent responses. For example, in conditions of low conflict salience, anger was
associated with citizens' support for violent action while after conflict aggravation feelings of
humiliation elicited support for violent resistance. Furthermore, distinctive profiles of individual- versus group emotions shape an agentic response. For mainly indirectly experienced conflict
escalations, group emotions predicted violent collective action, while for closely experienced conflict
events, individual emotions were associated with violent engagement. The qualitative narratives of
formerly violent activists showed change pathways including emotional, cognitive, and behavioral
aspects. For most participants, the change sequence was triggered by an unforeseen respectful
intergroup encounter. This encounter elicited empathy towards the outgroup and reduced negative
emotions, resulting in the cognitive reappraisal of their situation concerning the conflict context.
Despite experiencing difficult conflict events and against the mechanisms outlined above, emotional
and behavioral change from radical violent to nonviolent activism was possible.
The data collected during different surges of conflict escalation in the Occupied Palestinian
Territories shows how emotional mechanisms contribute to violence. Understanding psychological
microfoundations, namely emotional dynamics, provides novel inroads for individual conflict
transformation. The research contributes to current approaches of integrating political science with
social psychology and adds more profound insights into the causes of violence, which is notoriously
difficult to study. The gained insights hold the potential to positively influence detrimental
intergroup behaviour in the Middle East and beyond
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Identifying genomic regulatory patterns underlying complex phenotypes from heterogeneous data
Full text linkLarge-scale transcriptomic datasets provide valuable opportunities to better understand the regulation of gene expression and its role in human health. However, these studies can be confounded by issues such as cell type heterogeneity. Furthermore, these datasets are growing extremely large with complex study designs, such as gene expression measured across a multitude of tissues, that must be accurately and efficiently modeled. Finally, better understanding of the mechanisms that influence gene regulation are required to integrate novel associations with biological understanding. In this dissertation, we introduce methods that address these issues in the analysis of tissue-level gene expression data. We present a method to accurately estimate cell type composition from these data by integrating single-cell information, as well as a scalable approach to model multi-tissue expression datasets and identify expression quantitative trait loci. We also present analyses of bulk expression data that support a hypothesized mechanism of gene regulation that occurs in the general female population through non-random X chromosome inactivation. The work presented in this dissertation allows researchers to perform efficient and accurate analyses of gene expression data and provides additional insight into the mechanisms that underlie associations between genetics, transcriptomics, and complex phenotypes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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