Uncovering topologically associating domains from three-dimensional genome maps with TADGATE.

Topologically associating domains (TADs) are essential components of three-dimensional (3D) genome organization and significantly influence gene transcription regulation. However, accurately identifying TADs from sparse chromatin contact maps and exploring the structural and functional elements within TADs remain challenging. To this end, we develop TADGATE, a graph attention auto-encoder that can generate imputed maps from sparse Hi-C contact maps while adaptively preserving or enhancing the underlying topological structures, thereby facilitating TAD identification.

Identifying cooperating cancer driver genes in individual patients through hypergraph random walk.

Objective: Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.

Effects of aerobic exercise on myocardial injury, NF-B expression, glucolipid metabolism and inflammatory factors in rats with coronary heart disease.

Objective: To investigate the influence of aerobic exercise on myocardial injury, NF-B expression, glucolipid metabolism and inflammatory factors in rats with Coronary Heart Disease (CHD) and explore the possible causative role.

Methods: 45 Sprague Dawley® rats were randomized into model, control and experimental groups. A high-fat diet was adopted for generating a rat CHD model, and the experimental group was given a 4-week aerobic exercise intervention.

mAexpress-enet: Predicting the regulatory expression mA sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, mA controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific mA sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple mA sites in each gene could influence the prediction performance.

Composite of carbon dots and TiNiSn thermoelectric materials: Initial investigation on the electrical and thermal transport properties.

TiNiCu0.025Sn0.99Sb0.

Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs, clinical and genetic data.

Alzheimer's disease (AD) is an irreversible central nervous degenerative disease, while mild cognitive impairment (MCI) is a precursor state of AD. Accurate early diagnosis of AD is conducive to the prevention and early intervention treatment of AD. Although some computational methods have been developed for AD diagnosis, most employ only neuroimaging, ignoring other data (e.

A novel cross-layer dual encoding-shared decoding network framework with spatial self-attention mechanism for hippocampus segmentation.

Accurate segmentation of the hippocampus from the brain magnetic resonance images (MRIs) is a crucial task in the neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, such as Alzheimer's disease (AD). Automatic segmentation of the hippocampus structures is challenging due to the small volume, complex shape, low contrast and discontinuous boundaries of hippocampus. Although some methods have been developed for the hippocampus segmentation, most of them paid too much attention to the hippocampus shape and volume instead of considering the spatial information.

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations.

Effect of Iron Tailing Powder-Based Ternary Admixture on Acid Corrosion Resistance of Concrete.

Exposure of concrete to acidic environments can cause the degradation of concrete elements and seriously affect the durability of concrete. As solid wastes are produced during industrial activity, ITP (iron tailing powder), FA (fly ash), and LS (lithium slag) can be used as admixtures to produce concrete and improve its workability. This paper focuses on the preparation of concrete using a ternary mineral admixture system consisting of ITP, FA, and LS to investigate the acid erosion resistance of concrete in acetic acid solution at different cement replacement rates and different water-binder ratios.

A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification.

ncRPI-LGAT: Prediction of ncRNA-protein interactions with line graph attention network framework.

Identification of ncRNA-protein interactions (ncRPIs) through wet experiments is still time-consuming and highly-costly. Although several computational approaches have been developed to predict ncRPIs using the structure and sequence information of ncRNAs and proteins, the prediction accuracy needs to be improved, and the results lack interpretability. In this work, we proposed a novel computational method (called ncRPI-LGAT) to predict the ncRNA-Protein Interactions by transforming the link prediction (, subgraph classification) task into a node classification task in the line network, and introducing a Line Graph ATtention network framework.

Few-Shot Drug Synergy Prediction With a Prior-Guided Hypernetwork Architecture.

Predicting drug synergy is critical to tailoring feasible drug combination treatment regimens for cancer patients. However, most of the existing computational methods only focus on data-rich cell lines, and hardly work on data-poor cell lines. To this end, here we proposed a novel few-shot drug synergy prediction method (called HyperSynergy) for data-poor cell lines by designing a prior-guided Hypernetwork architecture, in which the meta-generative network based on the task embedding of each cell line generates cell line dependent parameters for the drug synergy prediction network.

Defining the separation landscape of topological domains for decoding consensus domain organization of the 3D genome.

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. Obvious inconsistencies among the TADs identified by different methods indeed make the statistical and biological properties of TADs overly depend on the chosen method rather than on the data.

scHi-CSim: a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking.

Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells. However, high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream analysis. Here, we developed a single-cell Hi-C simulator (scHi-CSim) that generates high-fidelity data for benchmarking.

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions.

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs.

DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.

Identification of cancer driver genes plays an important role in precision oncology research, which is helpful to understand cancer initiation and progression. However, most existing computational methods mainly used the protein-protein interaction (PPI) networks, or treated the directed gene regulatory networks (GRNs) as the undirected gene-gene association networks to identify the cancer driver genes, which will lose the unique structure regulatory information in the directed GRNs, and then affect the outcome of the cancer driver gene identification. Here, based on the multi-omics pan-cancer data (i.

Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.

Background: Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network.

Pyrolae herba: A review on its botany, traditional uses, phytochemistry, pharmacology and quality control.

Ethnopharmacological Relevance: Pyrolae herba is the dried whole plant of Pyrola calliantha H. Andres or Pyrola decorata H. Andres (Pyrolaceae).

m 6 Aexpress-BHM: predicting m6A regulation of gene expression in multiple-groups context by a Bayesian hierarchical mixture model.

As the most abundant RNA modification, N6-methyladenosine (m6A) plays an important role in various RNA activities including gene expression and translation. With the rapid application of MeRIP-seq technology, samples of multiple groups, such as the involved multiple viral/ bacterial infection or distinct cell differentiation stages, are extracted from same experimental unit. However, our current knowledge about how the dynamic m6A regulating gene expression and the role in certain biological processes (e.

Abrine Elicits Liver Carcinoma Immunity and Enhances Antitumor Efficacy of Immune Checkpoint Blockade by Modulating PD-L1 Signaling.

Background: Liver cancer is the most malignant type of human malignancies. In recent years, immune therapy that targets the immune check points such as programmed cell death ligand 1 (PD-L1) has achieve great success. Abrine is the dominant alkaloid in and Linn.

kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the -Mer Neighborhood Graph.

With the rapid development of single molecular sequencing (SMS) technologies such as PacBio single-molecule real-time and Oxford Nanopore sequencing, the output read length is continuously increasing, which has dramatical potentials on cutting-edge genomic applications. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. However, these long reads contain higher sequencing errors and could more frequently span the breakpoints of structural variants (SVs) than those of shorter reads, leading to many unaligned reads or reads that are partially aligned for most state-of-the-art mappers.

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.

The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms.

Detection of Alzheimer's disease using features of brain region-of-interest-based individual network constructed with the sMRI image.

Brain networks constructed with regions of interest (ROIs) from the structural magnetic resonance imaging (sMRI) image are widely investigated for detecting Alzheimer's disease (AD). However, the ROI is generally represented by spatial domain-based features, so attentions are hardly paid to constructing a brain network with the frequency domain-based feature. In order to accurately characterize the ROI in the frequency domain and then construct an individual network, in this study, a novel method, which can describe the ROI properly by directional subbands and capture correlations between those ROIs, is proposed to construct a shearlet subband energy feature-based individual network (SSBIN) for AD detection.

Food wanting is mediated by transient activation of dopaminergic signaling in the honey bee brain.

The biological bases of wanting have been characterized in mammals, but whether an equivalent wanting system exists in insects remains unknown. In this study, we focused on honey bees, which perform intensive foraging activities to satisfy colony needs, and sought to determine whether foragers leave the hive driven by specific expectations about reward and whether they recollect these expectations during their waggle dances. We monitored foraging and dance behavior and simultaneously quantified and interfered with biogenic amine signaling in the bee brain.

Prediction of the transcription factor binding sites with meta-learning.

With the accumulation of ChIP-seq data, convolution neural network (CNN)-based methods have been proposed for predicting transcription factor binding sites (TFBSs). However, biological experimental data are noisy, and are often treated as ground truth for both training and testing. Particularly, existing classification methods ignore the false positive and false negative which are caused by the error in the peak calling stage, and therefore, they can easily overfit to biased training data.