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  1. 2181
    “…From iIS312, we then built three stage-specific models through transcriptomics data integration, and showed that epimastigotes present the most active metabolism among the stages (see S1–S4 GEMs). …”
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    Genome-scale metabolic models highlight stage-specific differences in essential metabolic pathways in Trypanosoma cruzi
  2. 2182
    “…In this Mini Review, we give an overview of the current high-throughput methodologies, including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and multi-parametric phenotyping suitable for systems immunology as well as on the key steps of data integration and biological interpretation. Additionally, we review recent studies in which multi-omics technologies have been used to characterize mechanisms of response and to identify powerful biomarkers of response to checkpoint inhibitors, CAR-T cell therapy, dendritic cell-based and peptide-based cancer vaccines. …”
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    Towards a Systems Immunology Approach to Unravel Responses to Cancer Immunotherapy
  3. 2183
    por Ma, Yuanyuan, Zhao, Junmin, Ma, Yingjun
    Publicado 2020
    “…BACKGROUND: With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. …”
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    MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis
  4. 2184
    “…Multidimensional data integration to reveal links between these datasets and the use of chronic kidney disease (CKD) rodent models to test the effects of intestinal microbiome on toxin accumulation and disease severity. …”
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    Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents
  5. 2185
    “…We developed a novel method for extracellular flux analysis of PBMCs, where we combined brightfield imaging with metabolic flux analysis and data integration in R. Multiple buffy coat donors were used to demonstrate assay linearity with low levels of variation. …”
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    Novel standardized method for extracellular flux analysis of oxidative and glycolytic metabolism in peripheral blood mononuclear cells
  6. 2186
    por Müller, Tamara T., Lio, Pietro
    Publicado 2020
    “…Our algorithm enriches the fundamental work of Masheyekhi and Gras in data integration, personal medicine, usability, visualization, and interactivity. …”
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    PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases
  7. 2187
    “…The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. …”
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    The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health
  8. 2188
    “…Thus, a retrospective study was conducted through the integration of many administrative health databases of the FVG as the source of information. From data integration, we estimated that more than two-thirds of AAV patients showed at least one hospitalization in their medical history, most frequently caused by the disease itself or superimposed infections. …”
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    Healthcare and economic burden of ANCA-associated vasculitis in Italy: an integrated analysis from clinical and administrative databases
  9. 2189
    “…After sample cleaning, data integration, and batch effect removal, we used 22 publicly available datasets to draw out the tumor immune microenvironment using the ssGSEA algorithm. …”
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    Application of Immune Infiltration Signature and Machine Learning Model in the Differential Diagnosis and Prognosis of Bone-Related Malignancies
  10. 2190
    “…A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. …”
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    Understanding gender differences in professional European football through machine learning interpretability and match actions data
  11. 2191
    “…In this work we explore omics data from Breast, Kidney and Lung cancers at different levels as signalling pathways, functions and miRNAs, as part of the CAMDA 2019 Hi-Res Cancer Data Integration Challenge. Our goal is to find common functional patterns which give rise to the generic microenvironment in these cancers and contribute to a better understanding of cancer pathogenesis and a possible clinical translation down further studies. …”
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    Common pathways and functional profiles reveal underlying patterns in Breast, Kidney and Lung cancers
  12. 2192
    “…RESULTS: We highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository. …”
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    PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial
  13. 2193
    “…INTRODUCTION: The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies. …”
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    On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples
  14. 2194
    “…We hope that this model will serve as an improved basis for future data integration, useful for research and drug developments within diabetes.…”
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    An Updated Organ-Based Multi-Level Model for Glucose Homeostasis: Organ Distributions, Timing, and Impact of Blood Flow
  15. 2195
    “…For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138 663 (including 115 357 tumor) copy number variation (CNV) profiles. …”
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    The Progenetix oncogenomic resource in 2021
  16. 2196
    “…This study emphasises the power of joint inference schemes based on genomic and epidemiological data integration to inform the transmission dynamics of disease epidemics, ultimately aimed at better disease control.…”
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    Retrospective Characterization of the 2006–2007 Swine Vesicular Disease Epidemic in Northern Italy by Whole Genome Sequence Analysis
  17. 2197
    “…Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.…”
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    Towards realizing the vision of precision medicine: AI based prediction of clinical drug response
  18. 2198
    “…A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. …”
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    A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group
  19. 2199
    “…To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open‐source and containerized for easy use. …”
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    KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species
  20. 2200
    por Mei, Suyu, Zhang, Kun
    Publicado 2021
    “…Large-scale empirical studies including both cross validation and independent test show that the proposed drug target profiles-based machine learning framework outperforms existing data integration-based methods. The proposed statistical metrics show that two drugs easily interact in the cases that they target common genes; or their target genes connect via short paths in protein–protein interaction networks; or their target genes are located at signaling pathways that have cross-talks. …”
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    A machine learning framework for predicting drug–drug interactions
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