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Feature-specific quantile normalization and feature-specific mean–variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data
BackgroundCross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating...
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CircRNA identification and feature interpretability analysis
BackgroundCircular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on...
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LncRNA–protein interaction prediction with reweighted feature selection
LncRNA–protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many...
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iDHS-FFLG: Identifying DNase I Hypersensitive Sites by Feature Fusion and Local–Global Feature Extraction Network
The DNase I hypersensitive sites (DHSs) are active regions on chromatin that have been found to be highly sensitive to DNase I. These regions contain...
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A neural network model to screen feature genes for pancreatic cancer
All the time, pancreatic cancer is a problem worldwide because of its high degree of malignancy and increased mortality. Neural network model...
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Fractal feature selection model for enhancing high-dimensional biological problems
The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode...
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Feature selection for effective prediction of SARS-COV-2 using machine learning
BackgroundWith rise in variants of SARS-CoV-2, it is necessary to classify the emerging SARS-CoV-2 for early detection and thereby reduce human...
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Attention-based dual-path feature fusion network for automatic skin lesion segmentation
Automatic segmentation of skin lesions is a critical step in Computer Aided Diagnosis (CAD) of melanoma. However, due to the blurring of the lesion...
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scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising
Recently developed single-cell RNA-seq (scRNA-seq) technology has given researchers the chance to investigate single-cell level of disease...
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Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm
Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient’s quality of life and lead to permanent brain damage....
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Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data
BackgroundMicroarray data have been widely utilized for cancer classification. The main characteristic of microarray data is “large p and small n” in...
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MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction
Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic...
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Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations
LncRNAs play a part in numerous momentous processes of biology such as disease diagnoses, preventions and treatments. The associations between...
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Investigating the neurocognitive background of speech perception with a fast multi-feature MMN paradigm
The speech multi-feature MMN (Mismatch Negativity) offers a means to explore the neurocognitive background of the processing of multiple speech...
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Rapid identification of medicinal plants via visual feature-based deep learning
BackgroundTraditional Chinese Medicinal Plants (CMPs) hold a significant and core status for the healthcare system and cultural heritage in China. It...
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Essential genes identification model based on sequence feature map and graph convolutional neural network
BackgroundEssential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune...
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A statistical framework for high-content phenotypic profiling using cellular feature distributions
High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space....
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Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance
BackgroundDimension reduction, especially feature selection, is an important step in improving classification performance for high-dimensional data....
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Benchmark study of feature selection strategies for multi-omics data
BackgroundIn the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same...
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Aspect-based sentiment analysis for fish diseases using a feature interaction model based on adversarial strategy
Aspect-based sentiment analysis has achieved many results in recent years, but most of the research focuses on goods, services, and topics. The...
