Bridging heterogeneous mutation data to enhance disease gene discovery.

Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due to the differences of size and quality between GWAS studies, not all de facto vital variations are able to pass the multiple testing. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain of function and loss of function. To bring together the heterogeneous mutation data, we propose a 'Gene-Disease Association prediction by Mutation Data Bridging (GDAMDB)' pipeline with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types and recovers false-negative GWAS mutations that fail to pass significant test but represent supportive evidences of functional biological process in literature. Eventually, we applied GDAMDB in Alzheimer's disease (AD) and predicted 79 AD-associated genes. Besides, 12 of them from the original GWAS, 60 of them are supported to be AD-related by other GWAS or literature report, and rest of them are newly predicted genes. Our model is capable of enhancing the GWAS-based gene association discovery by well combining text mining results. The positive result indicates that bridging the heterogeneous mutation data is contributory for the novel disease-related gene discovery.

Defect Detection in Steel Using a Hybrid Attention Network.

Defect detection in steel surface focuses on accurately identifying and precisely locating defects on the surface of steel materials. Methods of defect detection with deep learning have gained significant attention in research. Existing algorithms can achieve satisfactory results, but the accuracy of defect detection still needs to be improved. Aiming at this issue, a hybrid attention network is proposed in this paper. Firstly, a CBAM attention module is used to enhance the model's ability to learn effective features. Secondly, an adaptively spatial feature fusion (ASFF) module is used to improve the accuracy by extracting multi-scale information of defects. Finally, the CIOU algorithm is introduced to optimize the training loss of the baseline model. The experimental results show that the performance of our method in this work is superior on the NEU-DET dataset, with an 8.34% improvement in mAP. Compared with major algorithms of object detection such as SSD, EfficientNet, YOLOV3, and YOLOV5, the mAP was improved by 16.36%, 41.68%, 20.79%, and 13.96%, respectively. This demonstrates that the mAP of our proposed method is higher than other major algorithms.

High-quality gene/disease embedding in a multi-relational heterogeneous graph after a joint matrix/tensor decomposition.

MOTIVATION: Node embedding of biological entity network has been widely investigated for the downstream application scenarios. To embed full semantics of gene and disease, a multi-relational heterogeneous graph is considered in a scenario where uni-relation between gene/disease and other heterogeneous entities are abundant while multi-relation between gene and disease is relatively sparse. After introducing this novel graph format, it is illuminative to design a specific data integration algorithm to fully capture the graph information and bring embeddings with high quality. RESULTS: First, a typical multi-relational triple dataset was introduced, which carried significant association between gene and disease. Second, we curated all human genes and diseases in seven mainstream datasets and constructed a large-scale gene-disease network, which compromising 163,024 nodes and 25,265,607 edges, and relates to 27,165 genes, 2,665 diseases, 15,067 chemicals, 108,023 mutations, 2,363 pathways, and 7.732 phenotypes. Third, we proposed a Joint Decomposition of Heterogeneous Matrix and Tensor (JDHMT) model, which integrated all heterogeneous data resources and obtained embedding for each gene or disease. Forth, a visualized intrinsic evaluation was performed, which investigated the embeddings in terms of interpretable data clustering. Furthermore, an extrinsic evaluation was performed in the form of linking prediction. Both intrinsic and extrinsic evaluation results showed that JDHMT model outperformed other eleven state-of-the-art (SOTA) methods which are under relation-learning, proximity-preserving or message-passing paradigms. Finally, the constructed gene-disease network, embedding results and codes were made available. DATA AND CODES AVAILABILITY: The constructed massive gene-disease network is available at: https://hzaubionlp.com/heterogeneous-biological-network/. The codes are available at: https://github.com/bionlp-hzau/JDHMT.

Aerobic Exercise Prevents Arterial Stiffness and Attenuates Hyperexcitation of Sympathetic Nerves in Perivascular Adipose Tissue of Mice after Transverse Aortic Constriction.

We aimed to investigate the efficacy of exercise on preventing arterial stiffness and the potential role of sympathetic nerves within perivascular adipose tissue (PVAT) in pressure-overload-induced heart failure (HF) mice. Eight-week-old male mice were subjected to sham operation (SHAM), transverse aortic constriction-sedentary (TAC-SE), and transverse aortic constriction-exercise (TAC-EX) groups. Six weeks of aerobic exercise training was performed using a treadmill. Arterial stiffness was determined by measuring the elastic modulus. The elastic and collagen fibers of the aorta and sympathetic nerve distribution in PVAT were observed. Circulating noradrenaline (NE), expressions of ß3-adrenergic receptor (ß3-AR), and adiponectin in PVAT were quantified. During the recovery of cardiac function by aerobic exercise, thoracic aortic collagen elastic modulus (CEM) and collagen fibers were significantly decreased (p < 0.05, TAC-SE vs. TAC-EX), and elastin elastic modulus (EEM) was significantly increased (p < 0.05, TAC-SE vs. TAC-EX). Circulating NE and sympathetic nerve distribution in PVAT were significantly decreased (p < 0.05, TAC-SE vs. TAC-EX). The expression of ß3-AR was significantly reduced (p < 0.05, TAC-SE vs. TAC-EX), and adiponectin was significantly increased (p < 0.05, TAC-SE vs. TAC-EX) in PVAT. Regular aerobic exercise can effectively prevent arterial stiffness and extracellular matrix (ECM) remodeling in the developmental course of HF, during which sympathetic innervation and adiponectin within PVAT might be strongly implicated.

A multimodal deep learning framework for predicting drug-drug interaction events.

MOTIVATION: Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. RESULTS: In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug-drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision-recall curve of 0.9208. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/YifanDengWHU/DDIMDL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Oxidative stress by Ca2+ overload is critical for phosphate-induced vascular calcification.

Hyperphosphatemia is the primary risk factor for vascular calcification, which is closely associated with cardiovascular morbidity and mortality. Recent evidence showed that oxidative stress by high inorganic phosphate (Pi) mediates calcific changes in vascular smooth muscle cells (VSMCs). However, intracellular signaling responsible for Pi-induced oxidative stress remains unclear. Here, we investigated molecular mechanisms of Pi-induced oxidative stress related with intracellular Ca2+ ([Ca2+]i) disturbance, which is critical for calcification of VSMCs. VSMCs isolated from rat thoracic aorta or A7r5 cells were incubated with high Pi-containing medium. Extracellular signal-regulated kinase (ERK) and mammalian target of rapamycin were activated by high Pi that was required for vascular calcification. High Pi upregulated expressions of type III sodium-phosphate cotransporters PiT-1 and -2 and stimulated their trafficking to the plasma membrane. Interestingly, high Pi increased [Ca2+]i exclusively dependent on extracellular Na+ and Ca2+ as well as PiT-1/2 abundance. Furthermore, high-Pi induced plasma membrane depolarization mediated by PiT-1/2. Pretreatment with verapamil, as a voltage-gated Ca2+ channel (VGCC) blocker, inhibited Pi-induced [Ca2+]i elevation, oxidative stress, ERK activation, and osteogenic differentiation. These protective effects were reiterated by extracellular Ca2+-free condition, intracellular Ca2+ chelation, or suppression of oxidative stress. Mitochondrial superoxide scavenger also effectively abrogated ERK activation and osteogenic differentiation of VSMCs by high Pi. Taking all these together, we suggest that high Pi activates depolarization-triggered Ca2+ influx via VGCC, and subsequent [Ca2+]i increase elicits oxidative stress and osteogenic differentiation. PiT-1/2 mediates Pi-induced [Ca2+]i overload and oxidative stress but in turn, PiT-1/2 is upregulated by consequences of these alterations.NEW & NOTEWORTHY The novel findings of this study are type III sodium-phosphate cotransporters PiT-1 and -2-dependent depolarization by high Pi, leading to Ca2+ entry via voltage-gated Ca2+ channels in vascular smooth muscle cells. Cytosolic Ca2+ increase and subsequent oxidative stress are indispensable for osteogenic differentiation and calcification. In addition, plasmalemmal abundance of PiT-1/2 relies on Ca2+ overload and oxidative stress, establishing a positive feedback loop. Identification of mechanistic components of a vicious cycle could provide novel therapeutic strategies against vascular calcification in hyperphosphatemic patients.

Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy.

BACKGROUND: It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and brought significant improvement in cancer management. However, on the other hand, immune checkpoints blockade (ICB) by monoclonal antiboties may cause common and severe adverse reactions (ADRs), the cause of which remains largely undetermined. We hypothesize that ICB-agents may induce adverse reactions through off-target protein interactions, similar to the ADR-causing off-target effects of small molecules. In this study, we propose a hybrid phenotype mining approach which integrates molecular level information and provides new mechanistic insights for ICB-associated ADRs. METHODS: We trained a conditional random fields model on the TAC 2017 benchmark training data, then used it to extract all drug-centric phenotypes for the five anti-PD-1/PD-L1 drugs from the drug labels of the DailyMed database. Proteins with structure similar to the drugs were obtained by using BlastP, and the gene targets of drugs were obtained from the STRING database. The target-centric phenotypes were extracted from the human phenotype ontology database. Finally, a screening module was designed to investigate off-target proteins, by making use of gene ontology analysis and pathway analysis. RESULTS: Eventually, through the cross-analysis of the drug and target gene phenotypes, the off-target effect caused by the mutation of gene BTK was found, and the candidate side-effect off-target site was analyzed. CONCLUSIONS: This research provided a hybrid method of biomedical natural language processing and bioinformatics to investigate the off-target-based mechanism of ICB treatment. The method can also be applied for the investigation of ADRs related to other large molecule drugs.

CRISPR/Cas9-mediated efficient and precise targeted integration of donor DNA harboring double cleavage sites in Xenopus tropicalis.

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) 9 system has emerged as a powerful tool for knock-in of DNA fragments via donor plasmid and homology-independent DNA repair mechanism; however, conventional integration includes unnecessary plasmid backbone and may result in the unfaithful expression of the modified endogenous genes. Here, we report an efficient and precise CRISPR/Cas9-mediated integration strategy using a donor plasmid that harbors 2 of the same cleavage sites that flank the cassette at both sides. After the delivery of donor plasmid, together with Cas9 mRNA and guide RNA, into cells or fertilized eggs, concurrent cleavages at both sides of the exogenous cassette and the desired chromosomal site result in precise targeted integration without plasmid backbone. We successfully used this approach to precisely integrate the EGFP reporter gene into the myh6 locus or the GAPDH locus in Xenopus tropicalis or human cells, respectively. Furthermore, we demonstrate that replacing conventional terminators with the endogenous 3UTR of target genes in the cassette greatly improves the expression of reporter gene after integration. Our efficient and precise method will be useful for a variety of targeted genome modifications, not only in X. tropicalis, but also in mammalian cells, and can be readily adapted to many other organisms.-Mao, C.-Z., Zheng, L., Zhou, Y.-M., Wu, H.-Y., Xia, J.-B., Liang, C.-Q., Guo, X.-F., Peng, W.-T., Zhao, H., Cai, W.-B., Kim, S.-K., Park, K.-S., Cai, D.-Q., Qi, X.-F. CRISPR/Cas9-mediated efficient and precise targeted integration of donor DNA harboring double cleavage sites in Xenopus tropicalis.

CpG oligodeoxynucleotide preconditioning improves cardiac function after myocardial infarction via modulation of energy metabolism and angiogenesis.

Unmethylated CpG oligodeoxynucleotide (CpG-ODN), a Toll-like receptor 9 (TLR9) ligand, has been shown to protect against myocardial ischemia/reperfusion injury. However, the potential effects of CpG-ODN on myocardial infarction (MI) induced by persistent ischemia remains unclear. Here, we investigated whether and how CpG-ODN preconditioning protects against MI in mice. C57BL/6 mice were treated with CpG-ODN by i.p. injection 2 hr prior to MI induction, and cardiac function, and histology were analyzed 2 weeks after MI. Both 1826-CpG and KSK-CpG preconditioning significantly improved the left ventricular (LV) ejection fraction (LVEF) and LV fractional shortening (LVFS) when compared with non-CpG controls. Histological analysis further confirmed the cardioprotection of CpG-ODN preconditioning. In vitro studies further demonstrated that CpG-ODN preconditioning increases cardiomyocyte survival under hypoxic/ischemic conditions by enhancing stress tolerance through TLR9-mediated inhibition of the SERCA2/ATP and activation of AMPK pathways. Moreover, CpG-ODN preconditioning significantly increased angiogenesis in the infarcted myocardium compared with non-CpG. However, persistent TLR9 activation mediated by lentiviral infection failed to improve cardiac function after MI. Although CpG-ODN preconditioning increased angiogenesis in vitro, both the persistent stimulation of CpG-ODN and stable overexpression of TLR9 suppressed the tube formation of cardiac microvascular endothelial cells. CpG-ODN preconditioning significantly protects cardiac function against MI by suppressing the energy metabolism of cardiomyocytes and promoting angiogenesis. Our data also indicate that CpG-ODN preconditioning may be useful in MI therapy.

Hypoxia/ischemia promotes CXCL10 expression in cardiac microvascular endothelial cells by NFkB activation.

CXCL10, the chemokine with potent chemotactic activity on immune cells and other non-immune cells expressing its receptor CXCR3, has been demonstrated to involve in myocardial infarction, which was resulted from hypoxia/ischemia. The cardiac microvascular endothelial cells (CMECs) are the first cell type which is implicated by hypoxia/ischemia. However, the potential molecular mechanism by which hypoxia/ischemia regulates the expression of CXCL10 in CMECs remains unclear. In the present study, the expression of CXCL10 was firstly examined by real-time PCR and ELISA analysis. Several potential binding sites (BS) for transcription factors including NF-kappaB (NFkB), HIF1 alpha (HIF1α) and FoxO3a were identified in the promoter region of CXCL10 gene from -2000 bp to -1 bp using bioinformatics software. Luciferase reporter gene vectors for CXCL10 promoter and for activation of above transcription factors were constructed. The activation of NFkB, hypoxia-inducible transcription factor-1 alpha (HIF-1α) and FoxO3a was also analyzed by Western blotting. It was shown that the production of CXCL10 in CMECs was significantly increased by hypoxia/ischemia treatment, in parallel with the activation of CXCL10 promoter examined by reporter gene vector system. Furthermore, transcription factors including NFkB, HIF1α and FoxO3a were activated by hypoxia/ischemia in CMECs. However, over-expression of NFkB, but not that of HIF1α or FoxO3a, significantly promoted the activation of CXCL10 promoter reporter gene. These findings indicated that CXCL10 production in CMECs was significantly increased by hypoxia/ischemia, at least in part, through activation of NFkB pathway and subsequently binding to CXCL10 promoter, finally promoted the transcription of CXCL10 gene.

The CXCL10/CXCR3 axis promotes cardiac microvascular endothelial cell migration via the p38/FAK pathway in a proliferation-independent manner.

CXCL10 is a chemokine with potent chemotactic activity for immune and non-immune cells expressing its receptor CXCR3. Previous studies have demonstrated that CXCL10 is involved in myocardial infarction. However, the role of CXCL10 in cardiac microvascular endothelial cell (CMEC) regulation and related mechanisms remains unclear. In this study, we investigated the effects of CXCL10 on the CMEC migration and explored its potential molecular mechanism by wound healing, cell proliferation and viability analysis. Furthermore, migration-related signaling pathways, including FAK, Erk, p38 and Smad, were examined by Western blotting. We found that CXCL10 significantly promotes CMEC migration under normal conditions and during hypoxia/ischemia. However, no significant differences in CMEC proliferation and viability were observed with or without CXCL10 treatment. CXCL10-mediated CMEC migration was greatly blocked by treatment with an anti-CXCR3 antibody. Although CXCL10 treatment promoted phosphorylation and activation of the FAK, Erk, and p38 pathways during hypoxia/ischemia, CXCL10-mediated CMEC migration was significantly blocked by p38 and FAK inhibitors, but not by an Erk inhibitor. Furthermore, CXCL10-mediated FAK activation was suppressed by the p38 inhibitor. These findings indicated that the CXCL10/CXCR3 pathway promotes the migration of CMECs under normal conditions and during hypoxia/ischemia in a proliferation-independent manner, at least in part, through regulation of the p38/FAK pathways.

FoxO3a suppresses the senescence of cardiac microvascular endothelial cells by regulating the ROS-mediated cell cycle.

FoxO3a plays an important role in the aging process and decreases with age. However, the potential regulatory roles of FoxO3a in processes involved in cardiac microvascular endothelial cell (CMEC) senescence, and its underlying molecular mechanisms have not been elucidated. This study demonstrates that FoxO3a is deactivated in senescent CMECs together with the inhibition of proliferation and tube formation. Furthermore, the activation of the antioxidant enzymes catalase and SOD, downstream FoxO3a targets, was significantly decreased, thereby leading to cell cycle arrest in G1-phase by increased ROS generation and subsequently the activation of the p27(Kip1) pathway. However, FoxO3a overexpression in primary low-passage CMECs not only significantly suppressed the senescence process by increasing the activation of catalase and SOD but also markedly inhibited ROS generation and p27(Kip1) activation, although it failed to reverse cellular senescence. Moreover, both cell viability and tube formation were greatly increased by FoxO3a overexpression in primary CMECs during continuous passage. In addition, FoxO3a, deficiency in low-passage CMECs, accelerated the senescence process. Collectively, our data suggest that FoxO3a suppresses the senescence process in CMECs by regulating the antioxidant/ROS/p27(Kip1) pathways, although it fails to reverse the cellular senescent phenotype.

Predicting DNA binding proteins using support vector machine with hybrid fractal features.

DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances.

Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.

It is vital to investigate the complex mechanisms underlying tumors to better understand cancer and develop effective treatments. Metabolic abnormalities and clinical phenotypes can serve as essential biomarkers for diagnosing this challenging disease. Additionally, genetic alterations provide profound insights into the fundamental aspects of cancer. This study introduces Cancer-Alterome, a literature-mined dataset that focuses on the regulatory events of an organism's biological processes or clinical phenotypes caused by genetic alterations. By proposing and leveraging a text-mining pipeline, we identify 16,681 thousand of regulatory events records encompassing 21K genes, 157K genetic alterations and 154K downstream bio-concepts, extracted from 4,354K pan-cancer literature. The resulting dataset empowers a multifaceted investigation of cancer pathology, enabling the meticulous tracking of relevant literature support. Its potential applications extend to evidence-based medicine and precision medicine, yielding valuable insights for further advancements in cancer research.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

FoxO3 Regulates the Progress and Development of Aging and Aging-Related Diseases.

Aging is an inevitable risk factor for many diseases, including cardiovascular diseases, neurodegenerative diseases, cancer, and diabetes. Investigation into the molecular mechanisms involved in aging and longevity will benefit the treatment of age-dependent diseases and the development of preventative medicine for agingrelated diseases. Current evidence has revealed that FoxO3, encoding the transcription factor (FoxO)3, a key transcription factor that integrates different stimuli in the intrinsic and extrinsic pathways and is involved in cell differentiation, protein homeostasis, stress resistance and stem cell status, plays a regulatory role in longevity and in age-related diseases. However, the precise mechanisms by which the FoxO3 transcription factor modulates aging and promotes longevity have been unclear until now. Here, we provide a brief overview of the mechanisms by which FoxO3 mediates signaling in pathways involved in aging and aging-related diseases, as well as the current knowledge on the role of the FoxO3 transcription factor in the human lifespan and its clinical prospects. Ultimately, we conclude that FoxO3 signaling pathways, including upstream and downstream molecules, may be underlying therapeutic targets in aging and age-related diseases.

Romboutsia lituseburensis JCM1404 supplementation ameliorated endothelial function via gut microbiota modulation and lipid metabolisms alterations in obese rats.

Our previous study revealed moderate-intensity exercise improved endothelial function associated with decreasing Romboutsia in rats on a high-fat diet. However, whether Romboutsia influences endothelial function remains unclear. The aim of this study was to determine the effects of Romboutsia lituseburensis JCM1404 on the vascular endothelium of rats under standard diet (SD) or high-fat diet (HFD). Romboutsia lituseburensis JCM1404 had a better improvement effect on endothelial function under HFD groups, while no significant effect on small-intestinal and blood vessel morphology. HFD significantly decreased villus height of small intestine and increased outer diameter and media thickness of the vascular tissue. After the treatments by R. lituseburensis JCM1404, the expression of claudin5 was increased in the HFD groups. Romboutsia lituseburensis JCM1404 was found to increase alpha diversity in the SD groups, with an increase in beta diversity in the HFD groups. The relative abundance of Romboutsia and Clostridium_sensu_stricto_1 was decreased significantly in both diet groups after R. lituseburensis JCM1404 intervention. The functions of human diseases and endocrine and metabolic diseases significantly downregulated in the HFD groups by Tax4Fun analysis. Furthermore, we found Romboutsia was significantly associated with bile acids, triglycerides, amino acids and derivatives and organic acids and derivatives in the SD groups, while Romboutsia was significantly associated with triglycerides and free fatty acid in the HFD groups. Romboutsia lituseburensis JCM1404 significantly upregulated several metabolism-related pathways by KEGG analysis in the HFD groups, including glycerolipid metabolism, cholesterol metabolism, regulation of lipolysis in adipocytes, insulin resistance, fat digestion and absorption, thermogenesis. Overall, R. lituseburensis JCM1404 supplementation ameliorated endothelial function via gut microbiota modulation and lipid metabolisms alterations in obese rats.

Hierarchical Sampling for the Visualization of Large Scale-Free Graphs.

Graph sampling frequently compresses a large graph into a limited screen space. This paper proposes a hierarchical structure model that partitions scale-free graphs into three blocks: the core, which captures the underlying community structure, the vertical graph, which represents minority structures that are important in visual analysis, and the periphery, which describes the connection structure between low-degree nodes. A new algorithm named hierarchical structure sampling (HSS) was then designed to preserve the characteristics of the three blocks, including complete replication of the connection relationship between high-degree nodes in the core, joint node/degree distribution between high- and low-degree nodes in the vertical graph, and proportional replication of the connection relationship between low-degree nodes in the periphery. Finally, the importance of some global statistical properties in visualization was analyzed. Both the global statistical properties and local visual features were used to evaluate the proposed algorithm, which verify that the algorithm can be applied to sample scale-free graphs with hundreds to one million nodes from a visualization perspective.

LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19.

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition.

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pre-trained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.