“…There is a need for more precise and personalized approaches involving omics technologies, data science, and advanced analytics. An improved understanding of muscle involvement in spastic CP is needed. …”
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“…This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. …”
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“…This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. …”
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“…A whole new field of prognostic and early diagnostic digital biomarkers driven by data science and artificial intelligence can break the current cycle of high healthcare costs and low health quality that is being driven by today's chronic disease detection and treatment approaches. …”
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“…In recent years, with the aid of computer modeling and data science, several in silico methods for the cytotoxicity prediction of nanomaterials have been developed. …”
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“…We evaluate our data augmentation on the 2018 Data Science Bowl dataset consisting of various cell assays, lighting conditions, and imaging modalities. …”
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“…We consider these rules and associated pathophysiological parameters for guiding data-science and artificial intelligence mining of multi-omics and big-data for improving the precision of diagnostic and therapeutic approaches to sepsis. …”
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“…In the past few years, the applications of artificial intelligence and data science have increased rapidly in medicine especially in imaging applications. …”
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“…Activists enact alternative epistemological approaches to data science that center care, memory, and justice. Activists also face significant information challenges that increase monitoring labor and add emotional burden to reading about violent deaths. …”
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“…Due to this surge of popularity, analyzing the data stored on blockchains poses a new critical challenge in data science. To assist data scientists in various analytic tasks for a blockchain, in this tutorial, we provide a systematic and comprehensive overview of the fundamental elements of blockchain network models. …”
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“…In this research, we explore the application of Theory-Guided Data Science in studying protein–ligand binding. A hybrid model is introduced by integrating Graph Convolutional Network (data-driven model) with the GBNSR6 implicit solvent (physics-based model). …”
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“…As one of the key subjects of multi-center governance of environmental concerns, public perception is crucial in forming and implementing environmental policy. Based on data science research theory and the original theory of public perception, this study proposes a research framework to analyze environmental policy through network text analysis. …”
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“…This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar. …”
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“…It outlines opportunities for the technology and data science sectors specifically to engage given the access that these sectors have to information that would illuminate and frame the nuances and impacts of health inequity.…”
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“…The ever-increasing application of Artificial intelligence (AI) to clinical imaging is challenging radiology to become a discipline with competence in data science, which plays an important role in modern oncology. …”
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“…Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. …”
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“…Newer control and data science techniques also largely benefit from structured information endpoints available at the edge of the network. …”
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“…These structure–activity–stability correlations can be represented using data science tools like principal component analysis (PCA) in addition to common data visualization approaches. …”
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“…Throughout, potential pitfalls and implications for gerontologic data science development are identified.…”
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“…Kernel methods have played a major role in the last two decades in the modeling and visualization of complex problems in data science. The choice of kernel function remains an open research area and the reasons why some kernels perform better than others are not yet understood. …”
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