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Register a new userChallenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution
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Dimitris Floros Mr
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Authors
Dimitris Floros
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Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of many interactive neural and humoral biomarkers in order to establish predictive, diagnostic, and therapeutic targets. We encounter obstacles at every stage of the biomarker discovery process, from sample acquisition, bio-marker extraction to multi-aspect, multi-way interaction analysis, due to the study complexity and lack of support for complex data problem solutions. We introduce a novel data platform, named ROSALIND, to overcome the challenges, foster healthy and vital collaborations and advance scientific inquiries. Main methods: ROSALIND is a researcher-centered, study-specific data platform. It provides vital support of individual creativity and effort in collaborative research. We follow the principles etched in the platform name - ROSALIND stands for resource organisms with self-governed accessibility, linkability, integrability, neutrality, and dependability. We translate, encode and implement the principles in the platform with novel use of advanced concepts and techniques to ensure and protect data integrity and research integrity. From a researchers vantage point, ROSALIND embodies nuance utilities and advanced functionalities in one system, beyond conventional storage, archive and data management. Key findings: The deployment of ROSALIND in our GWI study in recent 12 months has accelerated the pace of data experiment and analysis, removed numerous error sources, and increased research quality and productivity. Significance: ROSALIND seems the first to address data integrity and research integrity in tandem with digital measures and means. It also promises a new type of distributed research networks with individualized data platforms connected in various self-organized collaboration configurations.
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Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimers Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed learning-to-rank approach PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results: Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions: The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.
Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.
Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: 1) interfaces, 2) data formatting, and 3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.
Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain largely unanswered. We investigate these questions by applying machine learning techniques to a large scale Lyme disease patient registry, MyLymeData, developed by the nonprofit LymeDisease.org. We apply various machine learning methods in order to measure the effect of individual features in predicting participants answers to the Global Rating of Change (GROC) survey questions that assess the self-reported degree to which their condition improved, worsened, or remained unchanged following antibiotic treatment. We use basic linear regression, support vector machines, neural networks, entropy-based decision tree models, and $k$-nearest neighbors approaches. We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC. After we identify the key features, we separate them from the dataset and demonstrate the effectiveness of these features at identifying GROC. In doing so, we highlight possible directions for future study both mathematically and clinically.
We report on a Collaborative Workshop Physics instructional strategy to deliver the first IE calculus-based physics course at Khalifa University, UAE. To these authors knowledge, this is the first such course on the Arabian Peninsula using PER-based instruction. A brief history of general university and STEM teaching in the UAE is given. We present this secondary implementation (SI) as a case study of a novel context and use it to determine if PER-based instruction can be successfully implemented far from the cultural context of the primary developer and, if so, how might such SIs differ from SIs within the US. With these questions in view, a pre-reform baseline of MPEX, FCI, course exam and English language proficiency data are used to design a hybrid implementation of Cooperative Group Problem Solving. We find that for students with high English proficiency, normalized gain on FCI improves from <g> = 0.16+/-0.10 pre- to <g> = 0.47+/-0.08 post-reform, indicating successful SI. We also find that <g> is strongly modulated by language proficiency and discuss likely causes. Regardless of language skill, problem-solving skill is also improved and course DFW rates drop from 50% to 24%. In particular, we find evidence in post-reform student interviews that prior classroom experiences, and not broader cultural expectations about education, are the more significant cause of expectations at odds with the classroom norms of well-functioning PER-based instruction. This result is evidence that PER-based innovations can be implemented across great changes in cultural context, provided that the method is thoughtfully adapted in anticipation of context and culture-specific student expectations. This case study should be valuable for future reforms at other institutions, both in the Gulf Region and developing world, facing similar challenges involving SI of PER-based instruction outside the US.
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