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Dimensionality reduction beyond neural subspaces with slice tensor component analysis
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that...
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Early Alzheimer’s Prediction Using Dimensionality Reduction Techniques
Single cell RNA sequencing (scRNA-seq) technology is capable of generating a large amount of data. These big data would be easier to handle, if they...
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Early Alzheimer’s Prediction Using Dimensionality Reduction Techniques
Single cell RNA sequencing (scRNA-seq) technology is capable of generating a large amount of data. These big data would be easier to handle, if they...
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Multifactor Dimensionality Reduction Analysis to Evaluate the Association of Dopamine Beta-Hydroxylase (DΒH) Polymorphisms with Susceptibility to Dementia (SADEM Study)
Dementia is a multifactorial disease in which environmental, lifestyle, and genetic factors intervene. Population studies have been used in looking...
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Dimensionality Reduction Methods in Machine Learning
The goal of machine learning algorithm is to understand the basic features of a complex system. If the dataset is large and the number of features is...
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A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and Nonlinear Dimensionality Reduction (tSNE or UMAP)
Flow cytometry has been used for the last two decades to identify which immune cell subsets diapedese from the periphery into the brain parenchyma...
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A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data
Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable...
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The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO)
Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the...
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A Belief Degree–Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection
Epistasis is a challenge in prediction, classification, and suspicion of human genetic diseases. Many technologies, methods, and tools have been...
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Detection of microsleep states from the EEG: a comparison of feature reduction methods
Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport...
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Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study demonstrating accuracy-preserving data reduction for prosthetic embedded systems
AbstractThis work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in...
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Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and...
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Optimizing feature subset for schizophrenia detection using multichannel EEG signals and rough set theory
Schizophrenia (SZ) is a mental disorder that causes lifelong disorders based on delusions, cognitive deficits, and hallucinations. By visual...
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Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology
Purpose of ReviewEpigenetic changes can be highly influenced by environmental factors and have in turn been proposed to influence chronic disease....
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A feature reduction and selection algorithm for improved obstructive sleep apnea classification process
Feature reduction and selection of the best features for classification is a crucial stage for data analysis to reduce training time and overfitting....
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Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning
BackgroundAmyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the...
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A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM
The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing...
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A Review of Machine Learning Algorithms for Biomedical Applications
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction...
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Revealing the role of necroptosis microenvironment: FCGBP + tumor-associated macrophages drive primary liver cancer differentiation towards cHCC-CCA or iCCA
Previous research has demonstrated that the conversion of hepatocellular carcinoma (HCC) to intrahepatic cholangiocarcinoma (iCCA) can be stimulated...
