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Multivariate Analysis

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A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reserve.

Chemosphere
The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growi...

Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS.

Scientific reports
We use shotgun proteomics to identify biomarkers of diagnostic and prognostic value in individuals diagnosed with amyotrophic lateral sclerosis. Matched cerebrospinal and plasma fluids were subjected to abundant protein depletion and analyzed by nano...

Individualized prediction of dispositional worry using white matter connectivity.

Psychological medicine
BACKGROUND: Excessive worry is a defining feature of generalized anxiety disorder and is present in a wide range of other psychiatric conditions. Therefore, individualized predictions of worry propensity could be highly relevant in clinical practice,...

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach.

Psychological medicine
BACKGROUND: The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition method...

Experience With Hypothermic Machine Perfusion in Expanded Criteria Donors: Functional Outcomes.

Transplantation proceedings
UNLABELLED: Hypothermic machine perfusion (HMP) decreases delayed graft function (DGF) and improves 1-year graft survival in expanded criteria donors (ECDs). Time of HMP could be associated with incidence of DGF.

A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.

Journal of neuroscience methods
BACKGROUND: Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging.

Unsupervised versus Supervised Identification of Prognostic Factors in Patients with Localized Retroperitoneal Sarcoma: A Data Clustering and Mahalanobis Distance Approach.

BioMed research international
The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS) patients by univariate and multivariate statistical techniques. A phase I-II study on localized RPS treated with high-dose ifosfamide and radiotherapy f...

Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning.

Analytica chimica acta
Large amounts of data are generally produced from mass spectrometry imaging (MSI) experiments in obtaining the molecular and spatial information of biological samples. Traditionally, MS images are constructed using manually selected ions, and it is v...

A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.

Journal of cancer research and clinical oncology
PURPOSE: To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores.