Identification of the methotrexate resistance-related diagnostic markers in osteosarcoma via adaptive total variation netNMF and multi-omics datasets.

Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.

An improved immune algorithm with parallel mutation and its application.

The objective of this paper is to design a fast and efficient immune algorithm for solving various optimization problems. The immune algorithm (IA), which simulates the principle of the biological immune system, is one of the nature-inspired algorithms and its many advantages have been revealed. Although IA has shown its superiority over the traditional algorithms in many fields, it still suffers from the drawbacks of slow convergence and local minima trapping problems due to its inherent stochastic search property.

Biomimetic hydrogel derived from decellularized dermal matrix facilitates skin wounds healing.

Cutaneous wound healing affecting millions of people worldwide represents an unsolvable clinical issue that is frequently challenged by scar formation with dramatical pain, impaired mobility and disfigurement. Herein, we prepared a kind of light-sensitive decellularized dermal extracellular matrix-derived hydrogel with fast gelling performance, biomimetic porous microstructure and abundant bioactive functions. On account of its excellent cell biocompatibility, this ECM-derived hydrogel could induce a marked cellular infiltration and enhance the tube formation of HUVECs.

Application of orthogonal sparse joint non-negative matrix factorization based on connectivity in Alzheimer's disease research.

Based on the mining of micro- and macro-relationships of genetic variation and brain imaging data, imaging genetics has been widely applied in the early diagnosis of Alzheimer's disease (AD). However, effective integration of prior knowledge remains a barrier to determining the biological mechanism of AD. This paper proposes a new connectivity-based orthogonal sparse joint non-negative matrix factorization (OSJNMF-C) method based on integrating the structural magnetic resonance image, single nucleotide polymorphism and gene expression data of AD patients; the correlation information, sparseness, orthogonal constraint and brain connectivity information between the brain image data and genetic data are designed as constraints in the proposed algorithm, which efficiently improved the accuracy and convergence through multiple iterative experiments.

A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.

Traditional image genetics primarily uses linear models to investigate the relationship between brain image data and genetic data for Alzheimer's disease (AD) and does not take into account the dynamic changes in brain phenotype and connectivity data across time between different brain areas. In this work, we proposed a novel method that combined Deep Subspace reconstruction with Hypergraph-Based Temporally-constrained Group Sparse Canonical Correlation Analysis (DS-HBTGSCCA) to discover the deep association between longitudinal phenotypes and genotypes. The proposed method made full use of dynamic high-order correlation between brain regions.

An improved MOPSO approach with adaptive strategy for identifying biomarkers from gene expression dataset.

Biomarkers plays an important role in the prediction and diagnosis of cancers. Therefore, it is urgent to design effective methods to extract biomarkers. The corresponding pathway information of the microarray gene expression data can be obtained from public database, which makes possible to identify biomarkers based on pathway information and has been attracted extensive attention.

Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors.

Predicting the prognosis of hepatocellular carcinoma based on the interaction between pyroptosis, apoptosis, and necroptosis.

Multiple programmed cell death pathways (pyroptosis, apoptosis, and necroptosis) are closely related to the progression of hepatocellular carcinoma (HCC). Furthermore, molecular interactions among pyroptotic, apoptotic, and necroptotic components may be new targets for cancer therapy. However, the signature of the genes involved in the interaction between pyroptosis, apoptosis, and necroptosis (PANRGs), and their prognostic value, is still unclear in HCC.

Biomarker Genes Discovery of Alzheimer's Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia.

Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer's disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene co-expression network analysis (WGCNA) with single-cell regulatory network inference and clustering (SCENIC).

Identify Biomarkers of Alzheimer's Disease Based on Multi-task Canonical Correlation Analysis and Regression Model.

Imaging genetics using imaging technology is regarded as a neuroanatomical phenotype to evaluate gene single nucleotide polymorphisms and their effects on the structure and function of different brain regions. It plays a vital role in bridging the initial understanding of the genetic basis of brain structure and dysfunction. Sparse canonical correlation analysis (SCCA) has become a widespread technique in this field because of its powerful ability to identify bivariate relationships and feature selection.

Exploring autophagy-related prognostic genes of Alzheimer's disease based on pathway crosstalk analysis.

Recent studies have shown that different signaling pathways are involved in the pathogenesis of Alzheimer's disease (AD), with complex molecular connections existing between these pathways. Autophagy is crucial for the degradation and production of pathogenic proteins in AD, and it shows link with other AD-related pathways. However, current methods for identifying potential therapeutic targets for AD are primarily based on single-gene analysis or a single signal pathway, both of which are somewhat limited.

Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis.

Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved.

An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer's Disease.

Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics.

Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints.

Imaging genetics research can explore the potential correlation between imaging and genomics. Most association analysis methods cannot effectively use the prior knowledge of the original data. In this respect, we add the prior knowledge of each original data to mine more effective biomarkers.

Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach.

Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain's biological network.

Identifying Imaging Genetics Biomarkers of Alzheimer's Disease by Multi-Task Sparse Canonical Correlation Analysis and Regression.

Imaging genetics combines neuroimaging and genetics to assess the relationships between genetic variants and changes in brain structure and metabolism. Sparse canonical correlation analysis (SCCA) models are well-known tools for identifying meaningful biomarkers in imaging genetics. However, most SCCA models incorporate only diagnostic status information, which poses challenges for finding disease-specific biomarkers.

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization.

Imaging genetics reveals the connection between microscopic genetics and macroscopic imaging, enabling the identification of disease biomarkers. In this work, we make full use of prior knowledge that has significant reference value for investigating the correlation between the brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose joint-connectivity-based sparse nonnegative matrix factorization (JCB-SNMF).

Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured KCCA approach.

Neuroimaging genetics has become an important research topic since it can reveal complex associations between genetic variants (i.e. single nucleotide polymorphisms (SNPs) and the structures or functions of the human brain.

Prediction of Overall Survival Rate in Patients With Hepatocellular Carcinoma Using an Integrated Model Based on Autophagy Gene Marker.

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB).

Exploring the changes of brain immune microenvironment in Alzheimer's disease based on PANDA algorithm combined with blood brain barrier injury-related genes.

Studies have shown that the specific entry of peripheral cells into the brain parenchyma caused by BBB injury and the imbalance of the immune microenvironment in the brain are closely related to the pathogenesis of Alzheimer's disease (AD). Because of the difficulty of obtaining data inside the brain, it is urgent to find out the relationship between the peripheral and intracerebral data and their influence on the development of AD by machine learning methods. However, in the actual algorithm design, it is still a challenge to extract relevant information from a variety of data to establish a complete and accurate regulatory network.

Peripheral Blood Biomarkers CXCL12 and TNFRSF13C Associate with Cerebrospinal Fluid Biomarkers and Infiltrating Immune Cells in Alzheimer Disease.

Neuroinflammation-induced neurodegeneration and immune cell infiltration are two features of Alzheimer disease (AD). This study aimed to identify potential peripheral biomarkers that interact with cerebrospinal fluid (CSF) and infiltrating immune cells in AD. Blood and CSF data were downloaded from the Alzheimer's disease Neuroimaging Initiative database.

Investigating the correlation between DNA methylation and immune‑associated genes of lung adenocarcinoma based on a competing endogenous RNA network.

In recent years, there have been major breakthroughs in immunotherapies for the treatment of cancer. However, different patients have different responses to immunotherapy. Numerous studies have shown that the accumulation of epigenetic abnormalities, such as DNA methylation, serve an important role in the immune response of lung adenocarcinoma (LUAD).

Association Analysis of Peripheral and CSF Biomarkers in Late Mild Cognitive Impairment.

Research shows that late mild cognitive impairment (LMCI) has a high risk of turning into Alzheimer's disease (AD). Due to the invasion of detection methods and physical damage to the patients, it is not a convenient way to diagnose and detect early AD and LMCI by cerebrospinal fluid (CSF) data. So there is an urgent need to find the correlation between peripheral biological data and CSF data in the brain, and to find new diagnostic methods through changes in the peripheral biological data.

Recognizing novel chemicals/drugs for anatomical therapeutic chemical classes with a heat diffusion algorithm.

Drug Anatomical Therapeutic Chemical (ATC) classification system is a widely used and accepted drug classification system. It is recommended and maintained by World Health Organization (WHO). Each drug in this system is assigned one or more ATC codes, indicating which classes it belongs to in each of five levels.

Drug Target Group Prediction with Multiple Drug Networks.

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments.