... with advanced feature selection techniques. Here, we propose a novel backward variable selection method based on the hyperdimensional computing (HDC) paradigm, which takes inspiration from how the human brain works in the classification of concepts by encoding features into vectors in a high-dimensional space...

... is also presented along with examples and references. Conclusions This review provides a thorough taxonomy of methods for harmonising multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow. multimodal integration feature representation data...

... dataset is highly complex. In recent times, some computational methods have been developed for cancer omics data interpretation. However, various existing methods face challenges in accounting for diverse types of cancer omics data and struggle to effectively extract informative features...

.... To systematically mine reliable methylation prior knowledge from known DNA methylation-disease associations and develop robust computational methods for precision medicine applications, we propose MethPriorGCN. By integrating layer attention mechanisms and feature weighting mechanisms, MethPriorGCN not only...

...-unlabeled” as uninfected individuals must be PASC negative, but those with prior infection have unknown PASC status. Moreover, feature selection among many potentially informative characteristics can facilitate reaching a concise and easily interpretable PASC definition. Therefore, to characterize PASC...

...Nathaniel S O’Connell; Byron C Jaeger; Garrett S Bullock; Jaime Lynn Speiser random forest variable selection feature selection feature reduction regression Corresponding author. Division of Public Health Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston...

... evaluate HSSPPI on public benchmark datasets and the predicting results outperform the comparative models. This indicates the effectiveness of hierarchical protein modeling and also illustrates that HSSPPI has a strong feature extraction ability by considering spatial and sequential information...

... examinations for identifying SR. Methods and results Heart transplantation (HTx) recipients with endomyocardial biopsy and healthy controls were prospectively recruited for CMR assessment. CMR feature tracking was performed to evaluate the left ventricular (LV) global strain in all three directions...

... management of feature bindings ( Takacs et al. 2020 ; Prochnow et al. 2022a ; Prochnow et al. 2022b ). This is well in line with conceptions according to which enhanced alpha power is associated with the inhibition of information processing, particularly the suppression of processing task-irrelevant...

...Tian Bai; Kaile Bu; Chentao Gao; Huiling Chen Correspondence: [email protected] Graphical Abstract Figure. Principle and feature selection effects of QGRIME. 04 07 2024...

...Sara Monosilio; Silvia Prosperi; Domenico Filomena; Erika Lemme; Giuseppe Di Gioia; Ruggiero Mango; Lucrezia Netti; Giovanni Tonti; Gianni Pedrizzetti; Gianfranco Gualdi; Maria Rosaria Squeo; Antonio Pelliccia; Viviana Maestrini CMR Feature tracking Strain Myocardial deformation Athletes...

... web application. Graphical Abstract Graphical Abstract colorimetric analysis feature selection pH test strip Research on the application of machine learning to the field of chemistry has received a great deal of attention over the past decade. ^1–5 The colorimetric method determines...

... pose significant threats to coastal populations, causing destruction and loss of life. Precisely forecasting the frequency and arrival dates is still a challenge. This research presents a technique for feature extraction and integration using a random forest (RF) model with a cascaded convolutional...

..., when designing machine learning-based predictors for clathrin proteins, for instance, TargetCLP in this work, PSSM, with a length equal to the provided protein sequence, cannot be employed directly to represent the clathrin protein’s feature vector. Therefore, in this study, we employed an image-based...

... performance in cross-species experiments highlights its potential for broad applications in biomedical research and therapeutic development. AntiBinder is designed to determine whether an antibody can bind to an antigen. Its architecture involves several key steps: extracting features from both the antibody...

...Xiangfei Zhou; Hao Wu The scHiClassifier framework, depicted in Fig. 1 , comprises three key steps: data preparation, extraction of multiple feature sets, and construction of the fusion prediction model. In the first step, raw single-cell Hi-C data are preprocessed into contact matrices...

... of each feature on the disease outcome. To this end, we can evaluate the role of each feature using rigorous statistical inference for machine learning models by adopting a PermFIT procedure [ 31 ], as described briefly below. Based on the PermFIT, we derive a powerful framework to identify important...

... methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers. Results This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering. First, STMSGAL...

...-sensor autonomous driving and robotics systems, particularly in the context of navigating complex urban environments. The complementary nature of image and point cloud data allows for the detection of objects with greater accuracy and robustness when both image and point cloud features are employed...

... is properly cited. For commercial re-use, please contact [email protected] Abstract When the traditional random forest (RF) algorithm is used to select feature elements in biostatistical data, a large amount of noise data and parameters can affect the importance of the selected feature elements...