Addressing the unmet need for visualizing Conditional Random Fields in Biological Data

Background: The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the complex web of interacting factors inherent to a problem might be easy to define and also intractable to compute upon. Discussion: We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. Conclusions: In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects.Comment: BioVis 2014 conferenc

J Biomed Inform

We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.CC999999/ImCDC/Intramural CDC HHS/United States2019-11-20T00:00:00Z28729030PMC6864736694

Statistics challenges and opportunities for the twenty first century

The field of statistics has made remarkable contribution to society over several decades its impact is felt across gradually all branches of science, medicine, industries and planning in the government department. The main objectives of this research is to identify forthcoming challenges and enormous opportunities for the professions. I have tried to concentrate not only to scientific research but also cover important related topics such as statistics educations infrastructure science, industry, Pharmaceuticals, Fashion Technology, Information technology, the international perspective and a long term objectives. A substantial proportion of the research is devoted to describing the unique role of statistics as a tool in gaining knowledge, with the goal of making the research more accessible to the statistical community. This was done largely because the role of statistical science is often poorly understood by the rest of the scientific community. Much of the intellectual excitement of the core of the subject comes from the development and use of sophisticated mathematical and computational tools. For this reason there is a potential confusion about how statistics relates to other field , and so a portion of this research dealt with separating the identity of the others

CHILDREN IN THE UNITED STATES OPIOID EPIDEMIC

This dissertation examines the needs of children growing up in families where a parent or caregiver is struggling with opioid-related problems. Because of their parents’ illness, these children may be at increased risk for exposure to adverse or traumatic experiences. Indeed, a growing number of children are coming into contact with America’s child welfare systems because of parents’ opioid-related problems. These adverse childhood experiences may then increase children’s risk for adult substance use disorder, creating a two-generational health problem. However, there are few research studies and even fewer policy initiatives focused on meeting the unique needs of these families. This dissertation seeks to expand knowledge about children in the opioid epidemic with three aims: 1. Identify the number of families where an adult with an opioid use disorder lives with a child, and explore these adults’ access to treatment (Chapter 2). 2. Assess how childhood trauma influences the risk of heroin use at different ages in adults who have injected drugs (Chapters 3 & 4). 3. Test if Florida’s opioid prescribing reforms – designed to prevent overdose deaths – also helped reduce children’s contact with the child welfare system (Chapters 5 & 6). I address these aims using a combination of public surveys on drug use, administrative records on contact with the child welfare system, and primary data collection from adults who injected drugs in Baltimore. I show that: 1. Around 820,00 U.S. adults with an opioid use disorder live with at least one child, but fewer than a third report receiving any substance use treatment in the past year. 2. Among adults who have injected drugs, a history of very high levels of childhood adversity is associated with elevated risk for sustained heroin use into late adulthood. 3. Florida’s opioid prescribing reforms reduced drug overdose deaths, but did not have the added benefit of reducing children’s contact with the child welfare system. Findings suggest that existing strategies to address the opioid epidemic are not adequately meeting the unique needs of children, and specific, evidence-informed policies and programs are needed to address the unique needs of families struggling with opioid-related problems

Making augmented human intelligence in medicine practical: A case study of treating major depressive disorder

Individualized medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on highest reported efficacy in clinical trials, which is often not effective for all individuals. In this dissertation, we assert that treatment selection and management can be individualized when clinicians assessment of disease symptoms are augmented with a few analytically identified patient-specific measures (e.g., genomics, metabolomics) that are prognostic or predictive of treatment outcomes. Patient-derived biological, clinical and symptom measures are sufficiently complex, i.e., heterogeneous, noisy and high-dimensional. The question for research then becomes: “Which few among these large complex measures are sufficient to augment the clinician’s disease assessment and treatment logic to individualize treatment decisions?” This dissertation introduces, ALMOND — Analytics and Machine Learning Framework for Actionable Intelligence from Clinical and Omics Data. As a case study, this dissertation describes how ALMOND addresses the unmet need for individualized medicine in treating major depressive disorder — the leading cause of medical disabilities worldwide. The biggest challenge in individualizing treatment of depression is in the heterogeneity of how depressive symptoms manifest between individuals, and in their varied response to the same treatment. ALMOND comprises a systematic analytical workflow to individualize antidepressant treatment by addressing the challenge of heterogeneity of major depressive disorder. First, “right patients” are identified by stratifying patients using unsupervised learning, that serves as a foundation to associate their disease states with multiple pharmacological (drug-associated) measures. Second, “right drug” selection is shown to be feasible by demonstrating that psychiatrists’ depression severity assessments augmented with pharmacogenomic measures can accurately predict remission of depressive symptoms using supervised learning. Finally, probabilistic graphs provide early and easily interpretable prognoses at the “right time” to a psychiatrist by accounting for changes in routinely assessed depressive symptoms’ severity. By choosing antidepressants that have the highest-likelihood of the patient achieving remission, the chances of persisting depressive symptoms are reduced, which is often the leading medical conditions in those who commit suicide or develop chronic illnesses

Assessing and Mapping Rice Provisioning Ecosystem Services

Reisproduktion und damit verbundene Ökosystemleistungen sind abhängig von ökologischen und sozio-ökonomischen Faktoren. Auf wissenschaftlicher sowie auf politischer Ebene bestehen immer noch Wissenslücken zum Thema Entwicklung nachhaltiger Strategien für die Landwirtschaft und zur Verbesserung der Nahrungsmittelsicherheit. Landnutzer verstehen oftmals nicht die Probleme bezüglich Angebot und Nachfrage von Reisprodukten und Ökonomen können nicht alle landwirtschaftlichen Aspekte nachvollziehen. Diese Lücken haben zu einer Steigerung ökologischer Risiken (z.B. Dürre, Erosion und Verschmutzung) und zu Hungersnöten in Entwicklungsländern beigetragen. Darum ist es zwingend notwendig, einen integrativen Ansatz zu erarbeiten, welcher die Vorteile und das Wissen der verschiedenen Stakeholder, wie z.B. Landwirte, Politiker, Zwischenhändler und Konsumenten, integriert und eine ausbalancierte Bewertung ermöglicht.Rice production and related ecosystem services provision are strongly dependent on environmental characteristics and socio-economic factors. There are still various knowledge gaps among decision makers for the development of sustainable agriculture strategies and to improve food security. Farmers can often not clearly understand issues related to rice supply chains, while economists can often not clearly understand farming issues. These gaps have led to the increase of environmental risks (e.g. droughts, erosion and pollution), as well as famine threats in developing countries. Therefore, it is necessary to find out an integrated approach to balance the benefits and knowledge between stakeholders such as farmers, politicians, intermediate traders and consumers

The role of social supports on the financial toxicity of Cancer.

Although literature exists on the impact of cancer-related financial burden and its effects on quality of life, we do not have a full understanding of the nature of the financial burden on individuals and their families. Likewise, research on social support has concentrated on social, emotional, functional, and physical well-being but not on the financial well-being of individuals with cancer. The purpose of this study is to understand the role of social support in the relationship between financial toxicity and quality of life of individuals living with cancer. This research used a multiple method approach composed of two studies. Study 1 was a secondary analysis of data from a study exploring the lived experiences of 26 cancer survivors who self-reported financial hardship due to the costs of their cancer experience. Interview transcripts were analyzed using a theory-based direct approach to Qualitative Content Analysis. Study 2 recruited 126 participants who had a diagnosis of blood (Leukemia, Lymphoma or Myeloma) or breast cancer within the past 5 years. Moderator and mediator analyses were conducted to determine the specific path by which social support intervenes. Findings confirm the existence, use and need for social support that addresses financial toxicity. Social support plays a critical role in helping to protect patients from cancer-related financial toxicity. More needs to be done to address the financial needs of patients. Oncology social workers can play an essential role in assisting patients in averting financial toxicity

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature