Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

Machine learning techniques have become increasingly important in analyzing single-cell RNA and identifying cell types, providing valuable insights into cellular development and disease mechanisms. However, the presence of batch effects poses major challenges in scRNA-seq analysis due to data distribution variation across batches. Although several batch effect mitigation algorithms have been proposed, most of them focus only on the correlation of local structure embeddings, ignoring global distribution matching and discriminative feature representation in batch correction.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion.

Background: Epilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient's work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals.

Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks with Adaptively Feature-Spatial Balance and Contrastive Learning.

Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information has been the most commonly used method to identify spatial functional domains and genes. However, most existing spatial domain decipherer methods are more focused on spatially neighboring structures and fail to take into account balancing the self-characteristics and the spatial structure dependency of spots.

pscAdapt: pre-trained domain adaptation network based on structural similarity for cell type annotation in single cell RNA-seq data.

Cell type annotation refers to the process of categorizing and labeling cells to identify their specific cell types, which is crucial for understanding cell functions and biological processes. Although many methods have been developed for automated cell type annotation, they often encounter challenges such as batch effects due to variations in data distribution across platforms and species, thereby compromising their performance. To address batch effects, in this study, a pre-trained domain adaptation model based on structural similarity, named pscAdapt, is proposed for cell type annotation.

SGFCCDA: Scale Graph Convolutional Networks and Feature Convolution for circRNA-Disease Association Prediction.

Circular RNAs (circRNAs) have emerged as a novel class of non-coding RNAs with regulatory roles in disease pathogenesis. Computational models aimed at predicting circRNA-disease associations offer valuable insights into disease mechanisms, thereby enabling the development of innovative diagnostic and therapeutic approaches while reducing the reliance on costly wet experiments. In this study, SGFCCDA is proposed for predicting potential circRNA-disease associations based on scale graph convolutional networks and feature convolution.

BIGFormer: A Graph Transformer with Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.

Alzheimer's disease (AD) is a highly inheritable neurological disorder, and brain imaging genetics (BIG) has become a rapidly advancing field for comprehensive understanding its pathogenesis. However, most of the existing approaches underestimate the complexity of the interactions among factors that cause AD. To take full appreciate of these complexity interactions, we propose BIGFormer, a graph Transformer with local structural awareness, for AD diagnosis and identification of pathogenic mechanisms.

MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise.

MHOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction.

Numerous scientific studies have found a link between diverse microorganisms in the human body and complex human diseases. Because traditional experimental approaches are time-consuming and expensive, using computational methods to identify microbes correlated with diseases is critical. In this paper, a new microbe-disease association prediction model is proposed that combines a multi-view multi-modal network and a multi-scale feature fusion mechanism, called MHOGAT.

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection.

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject.

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder.

CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes.

Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases.

iLncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks.

Identification of disease-associated long non-coding RNAs (lncRNAs) is crucial for unveiling the underlying genetic mechanisms of complex diseases. Multiple types of similarity networks of lncRNAs (or diseases) can complementary and comprehensively characterize their similarities. Hence, in this study, we presented a computational model iLncDA-RSN based on reliable similarity networks for identifying potential lncRNA-disease associations (LDAs).

Network embedding framework for driver gene discovery by combining functional and structural information.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes.

NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification.

The development of single-cell RNA sequencing (scRNA-seq) technology has opened up a new perspective for us to study disease mechanisms at the single cell level. Cell clustering reveals the natural grouping of cells, which is a vital step in scRNA-seq data analysis. However, the high noise and dropout of single-cell data pose numerous challenges to cell clustering.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data.

Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection.

The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC) model, aims to utilize the consistency and complementarity of omics data to support biological research.

ARGLRR: A Sparse Low-Rank Representation Single-Cell RNA-Sequencing Data Clustering Method Combined with a New Graph Regularization.

The development of single-cell transcriptome sequencing technologies has opened new ways to study biological phenomena at the cellular level. A key application of such technologies involves the employment of single-cell RNA sequencing (scRNA-seq) data to identify distinct cell types through clustering, which in turn provides evidence for revealing heterogeneity. Despite the promise of this approach, the inherent characteristics of scRNA-seq data, such as higher noise levels and lower coverage, pose major challenges to existing clustering methods and compromise their accuracy.