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Background: Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates. To this end, the serum glycome has been proposed as a potential source of biomarkers for different types of cancers.High-throughput hydrophilic interaction liquid chromatography (HILIC) technology for glycan analysis allows for the detailed quantification of the glycan content in human serum. However, the experimental data from this analysis is compositional by nature. Compositional data are subject to a constant-sum constraint, which restricts the sample space to a simplex. Statistical analysis of glycan chromatography datasets should account for their unusual mathematical properties.As the volume of glycan HILIC data being produced increases, there is a considerable need for a framework to support appropriate statistical analysis. Proposed here is a methodology for feature selection in compositional data. The principal objective is to provide a template for the analysis of glycan chromatography data that may be used to identify potential glycan biomarkers.
Results: A greedy search algorithm, based on the generalized Dirichlet distribution, is carried out over the feature space to search for the set of "grouping variables" that best discriminate between known group structures in the data, modelling the compositional variables using beta distributions. The algorithm is applied to two glycan chromatography datasets. Statistical classification methods are used to test the ability of the selected features to differentiate between known groups in the data. Two well-known methods are used for comparison: correlation-based feature selection (CFS) and recursive partitioning (rpart). CFS is a feature selection method, while recursive partitioning is a learning tree algorithm that has been used for feature selection in the past.
Conclusions: The proposed feature selection method performs well for both glycan chromatography datasets. It is computationally slower, but results in a lower misclassification rate and a higher sensitivity rate than both correlation-based feature selection and the classification tree method.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703279 | PMC |
http://dx.doi.org/10.1186/1471-2105-14-155 | DOI Listing |
Sci Rep
December 2024
Electrical Engineering Department, Shobra Faculty of Engineering, Benha University, Cairo, 11629, Egypt.
The accurate classification of seismic events into natural earthquakes (EQ) and quarry blasts (QB) is crucial for geological understanding, seismic hazard mitigation, and public safety. This paper proposes a machine-learning approach to discriminate seismic events, particularly differentiating between natural EQs and man-made QBs. The core of this study is to integrate different features into a unified dataset to train some linear and nonlinear supervised machine learning (ML) models.
View Article and Find Full Text PDFSci Rep
December 2024
College of Mechanical Engineering, Beihua University, Jilin City, Jilin, 132021, China.
To address the limitations of weak information extraction of rolling bearing fault features and the poor generalization performance of diagnostic methods, a novel method was proposed based on sparrow search algorithm (SSA)-Variational Mode Decomposition (VMD) and refined composite multi-scale dispersion entropy (RCMDE). Firstly, SSA optimized the key parameters of VMD to decompose the fault signal. The time-frequency domain comprehensive evaluation factor algorithm was then employed to select the sensitive intrinsic mode function (IMF) components for reconstruction.
View Article and Find Full Text PDFBackground: For the time being, tafamidis is the only approved treatment for wild-type transthyretin cardiac amyloidosis. However, benefits on all-cause death only emerge after ≈18 months. The current available staging systems are unable to specifically discriminate patients at high risk of death within 18 months from diagnosis, and the selection of patients who are expected to benefit from tafamidis is left to the clinical judgment of treating physicians, being often based primarily (and sometimes only) on age.
View Article and Find Full Text PDFInt J Cardiol
December 2024
Department of Pharmacy, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China. Electronic address:
Background: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead to varying degrees of bleeding, resulting in serious clinical events, including death. This study aims to develop accurate and scalable predictive tools for early identification of bleeding risks during the DAPT period post-CABG, comparing them with the PRECISE-DAPT score..
View Article and Find Full Text PDFJ Neural Eng
December 2024
West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.
Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
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