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Spatial Gene Expression Prediction Using Hierarchical Sparse Attention
Spatial Transcriptomics (ST) quantitatively interprets human diseases by providing the gene expression of each fine-grained spot (i.e., window) in a...
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Review on Gene Expression Meta-analysis: Techniques and Implementations
The massive use of high-efficiency gene expression evaluation progress over the last twenty years and certainty the majority of the produced research...
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Spatial Gene Expression Prediction Using Coarse and Fine Attention Network
Spatial Transcriptomics (ST) quantitatively interprets human diseases by providing the gene expression of each fine-grained spot (i.e., window) on a...
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Ensemble Learning with SVM for High-Dimensional Gene Expression Data
The gene expression classification is the most important study in cancer diagnosis and drug discovery. Nevertheless, this task is very complicated to...
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Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data
The use of gene chips and microarrays for measuring gene expression is becoming widespread and is producing enormous amounts of data. With increasing...
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Map** the Topography of Spatial Gene Expression with Interpretable Deep Learning
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this...
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Improved Gene Expression Classification Through Multi-class Support Vector Machines Feature Selection
This paper proposes a new approach for gene expression classification by using a multi-class support vector machine (SVM) with feature selection. The...
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IGUANER - DIfferential Gene Expression and fUnctionAl aNalyzER
In the past fifteen years, the advent of Next-Generation Sequencing technologies, characterized by high efficiency and reduced costs, has marked a...
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Multiobjective Interactive Fuzzy Clustering for Gene Expression Data
Clustering, an unsupervised method for classifying patterns, seeks to organize data points based on their similarities or differences. Common...
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Spatial Gene Expression Prediction Using Multi-Neighborhood Network with Reconstructing Attention
Spatial transcriptomics (ST) has made it possible to link local spatial gene expression with the properties of tissue, which is very helpful to the...
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Association Analysis of Gene Expression and Brain Image Identifies Gene Signatures in Major Depression Disorder
Major depression disorder (MDD) usually comes with structural and functional alterations of the brain, which are determined by altered gene...
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Enhancing Gene Expression Classification Through Explainable Machine Learning Models
This paper proposes explainable machine learning models for enhancing gene expression classification. The proposed multi-class 1-norm support vector...
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Bladder cancer gene expression prediction with explainable algorithms
In this study, we aimed to classify bladder cancer patients using tumoral and non-tumoral gene expression data. In this way, we aimed to determine...
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Imputation of Compound Property Assay Data Using a Gene Expression Programming-Based Method
Compound property assays are an important part of drug development, but incomplete data may occur for a variety of reasons. To deal with these...
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A Classifier Based on Gene Expression Programming
Classification is an important branch of Data Mining technologies. The purpose of classification is to construct a classifier, for training with the...
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Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU
Gene co-expression networks are valuable tools for discovering biologically relevant information within gene expression data. However, analysing...
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Attention-Based Interpretable Regression of Gene Expression in Histology
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient...
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Improved aquila optimizer with mRMR for feature selection of high-dimensional gene expression data
Accurate classification of gene expression data is crucial for disease diagnosis and drug discovery. However, gene expression data usually has a...
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SCREEN: predicting single-cell gene expression perturbation responses via optimal transport
In this study, we propose SCREEN, a novel method for predicting perturbation responses of scRNA-seq data. Through extensive experiments on various...
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Ensemble Learning with Extended Newton Support Vector Machines for Enhancing Gene Expression Classification
Gene expression classification plays a crucial role in diagnosing diseases. In response to this critical challenge, the research community has...
