An Improved Framework for Drug-Side Effect Associations Prediction via Counterfactual Inference-Based Data Augmentation.

Detecting side effects of drugs is a fundamental task in drug development. With the expansion of publicly available biomedical data, researchers have proposed many computational methods for predicting drug-side effect associations (DSAs), among which network-based methods attract wide attention in the biomedical field. However, the problem of data scarcity poses a great challenge for existing DSAs prediction models.

Subtask-Aware Representation Learning for Predicting Antibiotic Resistance Gene Properties via Gating-Controlled Mechanism.

The crisis of antibiotic resistance has become a significant global threat to human health. Understanding properties of antibiotic resistance genes (ARGs) is the first step to mitigate this issue. Although many methods have been proposed for predicting properties of ARGs, most of these methods focus only on predicting antibiotic classes, while ignoring other properties of ARGs, such as resistance mechanisms and transferability.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes.

Motivation: The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs.

Prediction of Drug-Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding.

Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes.

KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human Health.

It is commonly known that food nutrition is closely related to human health. The complex interactions between food nutrients and diseases, influenced by gut microbial metabolism, present challenges in systematizing and practically applying knowledge. To address this, we propose a method for extracting triples from a vast amount of literature, which is used to construct a comprehensive knowledge graph on nutrition and human health.

An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network.

Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction.

An effective multi-task learning framework for drug repurposing based on graph representation learning.

Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations.

Analysis on methane production from various coal slime fermentations based on metagenomics.

Metagenomic sequencing technology was applied to evaluate differences in the anaerobic fermentation process of coal slimes by analyzing microbial diversity, functional activity structure, and cooperative relationship during the anaerobic fermentation of coal slimes with different coal ranks. The obtained results showed that the production of biomethane from coal slime was decreased by increasing metamorphism degree. Internal reason was higher abundance of microbial community in low rank coal slimes compared to that in high rank coal which had higher activity in the gene expression of key steps such as hydrolysis and acidification, methanation and the production of hydrogen and acetic acid.

An Effective Model for Predicting Phage-Host Interactions Via Graph Embedding Representation Learning With Multi-Head Attention Mechanism.

In the treatment of bacterial infectious diseases, overuse of antibiotics may lead to not only bacterial resistance to antibiotics but also dysbiosis of beneficial bacteria which are essential for maintaining normal human life activities. Instead, phage therapy, which invades and lyses specific pathogenic bacteria without affecting beneficial bacteria, becomes more and more popular to treat bacterial infectious diseases. For the effective phage therapy, it requires to accurately predict potential phage-host interactions from heterogeneous information network consisting of bacteria and phages.

Metagenomic characterization of biomethane transformation by lipid-catalyzed anaerobic fermentation of lignite.

The present study aims at using lipid in a novel way to improve the efficiency of methane production from lignite anaerobic digestion. The obtained results showed an increase by 3.13 times of the cumulative biomethane content of lignite anaerobic fermentation, when 1.

Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion.

Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance.

The characteristics of solid-phase substrate during the co-fermentation of lignite and straw.

Co-fermentation of lignite and biomass has been considered as a new approach in achieving clean energy. Moreover, the study of the characteristics of solid phase in the synergistic degradation process is of great significance in revealing their synergistic relationship. Accordingly, in order to produce biogas, lignite, straw, and the mixture of the two were used as the substrates, the solid phase characteristics of which were analyzed before and after fermentation using modern analytical methods.

Biochemical Process and Microbial Evolution in the Conversion of Corn Straw Combined with Coal to Biogas.

The combined anaerobic fermentation of coal and straw can increase the production of biogas. To explore the mechanism of adding corn straw to increase methane production, coal with different metamorphic degrees and corn straw were collected for biogas production simulation experiments under different substrate ratios. The changes in liquid products, the structure of lignocellulose in corn straw, and microbial evolution were monitored.

Culture medium optimization for producing biomethane by coal anaerobic digestion.

The culture medium in biogas field have been used in coalbed gas bioengineering (CBGB). However, there is a huge difference between the substrate of biogas fermentation and coal. It is necessary to study and optimize the culture medium in the anaerobic digestion (AD) system with coal as substrate.

A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling.

The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required.

Metagenomic insight of corn straw conditioning on substrates metabolism during coal anaerobic fermentation.

Increasing methane production from anaerobic digestion of coal is challenging. This study shows that the combined fermentation of coal and corn straw greatly enriched the substrates available to microorganisms. This was mainly manifested in the increased types and abundance of organic matter in the fermentation liquid, which enhanced methane production by 61%.

The biochemical mechanism of enhancing the conversion of chicken manure to biogenic methane using coal slime as additive.

To improve the efficiency of methane production from chicken manure (CM) anaerobic digestion, the mechanism of coal slime (CS) as an additive on methane production characteristics were investigated. The results showed that adding an appropriate amount of CS quickened the start of the fermentation and effectively increased the methane yield. In addition, the pH changed in a stable manner in the liquid phase, and the concentrations of total ammonia nitrogen (TAN) and free ammonia nitrogen (FAN) were reduced.

Document-Level Chemical-Induced Disease Relation Extraction via Hierarchical Representation Learning.

Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the interaction between local and global contexts in biomedical document, and the derived performance needs to be improved accordingly. In this paper, we propose a novel framework for document-level CID relation extraction.

Efficient multiple biomedical events extraction via reinforcement learning.

Motivation: Multiple events extraction from biomedical literature is a challenging task for biomedical community. Usually, biomedical event extraction is modeled as two sub-tasks, trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially, and fail to make full use of the interaction between them, leading to suboptimal results for multiple biomedical events extraction.

Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning.

Background: Exhaled aerosols from lungs have unique patterns, and their variation can be correlated to the underlying lung structure and associated abnormities. However, it is challenging to characterize such aerosol patterns and differentiate their difference because of their complexity. This challenge is even greater for small airway diseases, where the disturbance signals are weak.

The diversity of hydrogen-producing bacteria and methanogens within an in situ coal seam.

Background: Biogenic and biogenic-thermogenic coalbed methane (CBM) are important energy reserves for unconventional natural gas. Thus, to investigate biogenic gas formation mechanisms, a series of fresh coal samples from several representative areas of China were analyzed to detect hydrogen-producing bacteria and methanogens in an in situ coal seam. Complete microbial DNA sequences were extracted from enrichment cultures grown on coal using the Miseq high-throughput sequencing technique to study the diversity of microbial communities.

Development and Evaluation of Diagnostic Criteria for Vogt-Koyanagi-Harada Disease.

Importance: To our knowledge, a set of well-defined diagnostic criteria is not yet developed for the diagnosis of Vogt-Koyanagi-Harada (VKH) disease.

Objective: To develop and evaluate a set of diagnostic criteria for VKH disease using data from Chinese patients.

Design, Setting, And Participants: This case-control study reviewed medical records of patients from a tertiary referral center between October 2011 and October 2016.

TMD1 domain and CRAC motif determine the association and disassociation of MxIRT1 with detergent-resistant membranes.

Iron is essential for most living organisms. The iron-regulated transporter1 (IRT1) plays a major role in iron uptake in roots, and its trafficking from endoplasmic reticulum (ER) to plasma membrane (PM) is tightly coordinated with changes in iron environment. However, studies on the IRT1 response are limited.

Neuroprotective Effects of Baicalein on Acrolein-induced Neurotoxicity in the Nigrostriatal Dopaminergic System of Rat Brain.

Elevated levels of acrolein, an α,β-unsaturated aldehyde are detected in the brain of patients with Parkinson's disease (PD). In the present study, the neuroprotective effect of baicalein (a phenolic flavonoid in the dried root of Scutellaria baicalensis Georgi) on acrolein-induced neurodegeneration of nigrostriatal dopaminergic system was investigated using local infusion of acrolein in the substantia nigra (SN) of rat brain. Systemic administration of baicalein (30 mg/kg, i.