Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution

• URL: http://arxiv.org/abs/2102.02878v3
• Date: Wed, 17 Feb 2021 22:58:58 GMT
• Title: Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution
• Authors: Dimitris Floros (1), Mulugu V. Brahmajothi (2), Alexandros-Stavros Iliopoulos (3), Nikos Pitsianis (1 and 4), Xiaobai Sun (4) ((1) Aristotle University of Thessaloniki, (2) Duke University Medical Center, (3) Massachusetts Institute of Technology, (4) Duke University)
• Abstract summary: We introduce a novel data platform, named ROSALIND, to overcome the challenges, foster healthy and vital collaborations and advance scientific inquiries. We follow the principles etched in the platform name - ROSALIND stands for resource organisms with self-governed accessibility, linkability, integrability, neutrality, and dependability. 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.
• Score: 48.7576911714538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 researcher's 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.

Related papers

• Simplicity within biological complexity [0.0]
  We survey the literature and argue for the development of a comprehensive framework for embedding of multi-scale molecular network data. Network embedding methods map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation.
  arXiv  Detail & Related papers  (2024-05-15T13:32:45Z)
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark [55.898771405172155]
  Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. We provide a systematic overview of the important and recent developments of research on federated learning.
  arXiv  Detail & Related papers  (2023-11-12T06:32:30Z)
• Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets [17.774341783844026]
  This work proposes Multimodal Integration of Oncology Data System (MINDS) MINDS is a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability.
  arXiv  Detail & Related papers  (2023-09-30T15:44:39Z)
• Ontologies for increasing the FAIRness of plant research data [0.0]
  Onologies provide concepts for a particular domain as well as relationships between concepts. By tagging with data terms data becomes both human machine interpretable, allowing increased reuse and interoperability. We outline the most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments.
  arXiv  Detail & Related papers  (2023-08-25T13:08:26Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges [0.0]
  Traditionally, neuromarketing studies have relied on a single biosignal to obtain feedback from presented stimuli. Thanks to new devices and technological advances, recent trends indicate a shift towards the fusion of diverse biosignals. This paper conducts a comprehensive analysis of the objectives, biosignals, and data processing techniques employed in neuromarketing research.
  arXiv  Detail & Related papers  (2022-08-30T10:20:18Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• An Empirical Meta-analysis of the Life Sciences (Linked?) Open Data on the Web [1.2964393302157287]
  We study the Life Sciences Linked Open Data (LSLOD) cloud. We extract schemas from more than 80 publicly available biomedical linked data graphs. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources.
  arXiv  Detail & Related papers  (2020-06-07T14:26:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.