A Novel Drug-Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature.

Computational drug repurposing utilizes data analysis and predictive models to identify new uses for existing drugs and new drugs, significantly improving research efficiency and reducing costs compared to traditional screening methods. Due to the limitations of current computational models in extracting deep key features, we develop a novel drug repurposing model based on the deep non-negative matrix factorization (DNMF-DDA) to enhance the accuracy of drug-disease association predictions. The model leverages similarity and known association data to extract low-rank features from complex data spaces, allowing for the prediction of potential drug-disease associations.

Heterogeneous effects of physical activity on physiological stress during pregnancy.

Pregnancy is a critical period characterized by profound physiological and psychological adaptations that can significantly impact both maternal and fetal health outcomes. Thus, it is imperative to implement targeted and evidence-based interventions to enhance maternal well-being during the prenatal period. Mobile health (mHealth) technologies enable continuous, real-time monitoring of both physiological and psychological states, providing detailed insights into health behaviors and individual responses in natural settings.

Individualized Time-Varying Nonparametric Model With an Application in Mobile Health.

Individualized modeling has become increasingly popular in recent years with its growing application in fields such as personalized medicine and mobile health studies. With rich longitudinal measurements, it is of great interest to model certain subject-specific time-varying covariate effects. In this paper, we propose an individualized time-varying nonparametric model by leveraging the subgroup information from the population.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction.

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features.

TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks.

Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery. Most computational approaches treat DTI prediction as a binary classification problem, determining whether drug-target pairs interact.

Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases.

Motivation: The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.

Multivariate Temporal Point Process Regression.

Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion.

Predicting Plant miRNA-lncRNA Interactions via a Deep Learning Method.

In recent years, due to the contribution to elucidating the functional mechanisms of miRNAs and lncRNAs, the research on miRNA-lncRNA interaction prediction has increased exponentially. However, the prediction research is challenging in bioinformatics domain. It is expensive and time-consuming to verify the interactions by biological experiments.

Genetic Evidence for a Causal Relationship between Hyperlipidemia and Type 2 Diabetes in Mice.

Dyslipidemia is considered a risk factor for type 2 diabetes (T2D), yet studies with statins and candidate genes suggest that circulating lipids may protect against T2D development. -null () mouse strains develop spontaneous dyslipidemia and exhibit a wide variation in susceptibility to diet-induced T2D. We thus used mice to elucidate phenotypic and genetic relationships of circulating lipids with T2D.

Heterogeneous Mediation Analysis on Epigenomic PTSD and Traumatic Stress in a Predominantly African American Cohort.

DNA methylation (DNAm) has been suggested to play a critical role in post-traumatic stress disorder (PTSD), through mediating the relationship between trauma and PTSD. However, this underlying mechanism of PTSD for African Americans still remains unknown. To fill this gap, in this article, we investigate how DNAm mediates the effects of traumatic experiences on PTSD symptoms in the Detroit Neighborhood Health Study (DNHS) (2008-2013) which involves primarily African Americans adults.

Hyperlipidemia Influences the Accuracy of Glucometer-Measured Blood Glucose Concentrations in Genetically Diverse Mice.

Background: Glucometers are widely used in animal research due to simplicity and ease of utilization, but their accuracy in blood glucose assessment for hyperlipidemic mice is unknown.

Methods: Here, we compared blood glucose levels measured by a glucometer with plasma glucose levels measured by a standard enzymatic assay for 325 genetically diverse F2 mice derived from LP and BALB/c (BALB) Apoe mice. Non-fasting glucose levels were measured before initiation of a Western diet and after 11 weeks on the diet.

iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.

Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately.

Breast Cancer Candidate Gene Detection Through Integration of Subcellular Localization Data With Protein-Protein Interaction Networks.

Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes.

Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs.

Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models.

The pan-cancer landscape of prognostic germline variants in 10,582 patients.

Background: While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed multiple cancers in an unbiased way to identify genetic loci that can improve patient outcome predictions made using clinical factors.

Methods: We analyzed sequencing data from the over 10,000 cancer patients available through The Cancer Genome Atlas to identify germline variants associated with patient outcome using multivariate Cox regression models.

Carriage of Methicillin-resistant in a Colony of Rhesus () and Cynomolgus () Macaques.

Methicillin-resistant (MRSA) carriage and infection are well documented in the human and veterinary literature; however only limited information is available regarding MRSA carriage and infection in laboratory NHP populations. The objective of this study was to characterize MRSA carriage in a representative research colony of rhesus and cynomolgus macaques through a cross-sectional analysis of 300 animals. MRSA carriage was determined by using nasal culture.

Bidirectional long short-term memory with CRF for detecting biomedical event trigger in FastText semantic space.

Background: In biomedical information extraction, event extraction plays a crucial role. Biological events are used to describe the dynamic effects or relationships between biological entities such as proteins and genes. Event extraction is generally divided into trigger detection and argument recognition.

XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction.

Essential proteins as a vital part of maintaining the cells' life play an important role in the study of biology and drug design. With the generation of large amounts of biological data related to essential proteins, an increasing number of computational methods have been proposed. Different from the methods which adopt a single machine learning method or an ensemble machine learning method, this paper proposes a predicting framework named by XGBFEMF for identifying essential proteins, which includes a SUB-EXPAND-SHRINK method for constructing the composite features with original features and obtaining the better subset of features for essential protein prediction, and also includes a model fusion method for getting a more effective prediction model.

Prediction of essential proteins based on subcellular localization and gene expression correlation.

Background: Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years.

Radioactive Seed Localization Versus Wire Localization for Nonpalpable Breast Lesions: A Two-Year Initial Experience at a Large Community Hospital.

Background: Radioactive seed localization (RSL) is a safe and effective alternative to wire localization (WL) for nonpalpable breast lesions. While several large academic institutions currently utilize RSL, few community hospitals have adopted this technique.

Objective: The aim of this study was to examine the experience of RSL versus WL at a large community hospital.

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information.

Background: Diabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus.

A novel algorithm for detecting protein complexes with the breadth first search.

Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction (PPI) data can compromise the utility of the datasets for complexes reconstruction. In order to reduce the impact of such discrepancies, a number of data integration and affinity scoring schemes have been devised.

Predicting Essential Proteins Based on Weighted Degree Centrality.

Essential proteins are vital for an organism's viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency.

Clustering based on multiple biological information: approach for predicting protein complexes.

Protein complexes are a cornerstone of many biological processes. Protein-protein interaction (PPI) data enable a number of computational methods for predicting protein complexes. However, the insufficiency of the PPI data significantly lowers the accuracy of computational methods.