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. Although several data augmentation methods have been proposed to address this issue, most of existing methods employ a random way to manipulate the original networks, which ignores the causality of existence of DSAs, leading to the poor performance on the task of DSAs prediction. In this paper, we propose a counterfactual inference-based data augmentation method for improving the performance of the task. First, we construct a heterogeneous information network (HIN) by integrating multiple biomedical data. Based on the community detection on the HIN, a counterfactual inference-based method is designed to derive augmented links, and an augmented HIN is obtained accordingly. Then, a meta-path-based graph neural network is applied to learn high-quality representations of drugs and side effects, on which the predicted DSAs are obtained. Finally, comprehensive experiments are conducted, and the results demonstrate the effectiveness of the proposed counterfactual inference-based data augmentation for the task of DSAs prediction.

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. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. RESULTS: In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

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. However, acquiring all of these properties of ARGs can help researchers gain a more comprehensive understanding of the essence of antibiotic resistance, which will facilitate the development of antibiotics. In this paper, the task of predicting properties of ARGs is modeled as a multi-task learning problem, and an effective subtask-aware representation learning-based framework is proposed accordingly. More specifically, property-specific expert networks and shared expert networks are utilized respectively to learn subtask-specific features for each subtask and shared features among different subtasks. In addition, a gating-controlled mechanism is employed to dynamically allocate weights to subtask-specific semantics and shared semantics obtained respectively from property-specific expert networks and shared expert networks, thus adjusting distinctive contributions of subtask-specific features and shared features to achieve optimal performance for each subtask simultaneously. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs properties prediction.

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. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.

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. Concurrently, we develop a query-based question answering system over our knowledge graph, proficiently addressing three types of questions. The results show that our proposed model outperforms other state-of-art methods, achieving a precision of 0.92, a recall of 0.81, and an F1 score of 0.86 in the nutrition and disease relation extraction task. Meanwhile, our question answering system achieves an accuracy of 0.68 and an F1 score of 0.61 on our benchmark dataset, showcasing competitiveness in practical scenarios. Furthermore, we design five independent experiments to assess the quality of the data structure in the knowledge graph, ensuring results characterized by high accuracy and interpretability. In conclusion, the construction of our knowledge graph shows significant promise in facilitating diet recommendations, enhancing patient care applications, and informing decision-making in clinical research.

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. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of 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. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.

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. Acetic acid decarboxylation and CO2 reduction are two key pathways of methanation process. At the same time, K11261 (formylmethanofuran dehydrogenase subunit) and K01499 (methenyltetrahydromethanopterin cyclohydrolase) genes were further enriched in low rank slime systems, which enhanced the proportion of CO2 reduction in methanation pathway and was beneficial to biomethane production. Research revealed the roles of different coal slime ranks in biomethane production process and is considered as an important reference significance for further exploration of coal slime resource utilization.

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. Although many models have been proposed for predicting phage-host interactions, most methods fail to consider fully the sparsity and unconnectedness of phage-host heterogeneous information network, deriving the undesirable performance on phage-host interactions prediction. To address the challenge, we propose an effective model called GERMAN-PHI for predicting Phage-Host Interactions via Graph Embedding Representation learning with Multi-head Attention mechaNism. In GERMAN-PHI, the multi-head attention mechanism is utilized to learn representations of phages and hosts from multiple perspectives of phage-host associations, addressing the sparsity and unconnectedness in phage-host heterogeneous information network. More specifically, a module of GAT with talking-heads is employed to learn representations of phages and bacteria, on which neural induction matrix completion is conducted to reconstruct the phage-host association matrix. Results of comprehensive experiments demonstrate that GERMAN-PHI performs better than the state-of-the-art methods on phage-host interactions prediction. In addition, results of case study for two high-risk human pathogens show that GERMAN-PHI can predict validated phages with high accuracy, and some potential or new associated phages are provided as well.

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 1.8 g lipid was added. The gene expression of functional metabolic enzymes was also found to be enhanced during the anaerobic fermentation. Moreover, the enzymes related to fatty acid degradation such as long-chain Acyl-CoA synthetase and Acyl-CoA dehydrogenase were increased by 1.72 and 10.48 times, respectively, which consequently, accelerated the conversion of fatty acid. Furthermore, the addition of lipid enhanced the carbon dioxide trophic and acetic acid trophic metabolic pathways. Hence, the addition of lipids was argued to promote the production of methane from lignite anaerobic fermentation, which provided a new insight for the conversion and utilization of lipid waste.

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. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.

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. The results revealed that the mixed fermentation of lignite and straw promoted the production of biomethane. Moreover, the ratios of C/O and C/H were found to be complementary in the co-fermentation process. Furthermore, while the relative content of C-C/C-H bonds was observed to be significantly decreased, the aromatics degree of lignite was weakened. Also, while the degree of branching increased, there found to be an increase in the content of cellulose amorphous zone, which, consequently, led to an increase in the crystallinity index of the wheat straw. Hence, the results provide a theoretical guidance for the efficient utilization of straw and lignite.

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. The results showed that the combined fermentation of bituminous coal A with corn straw and bituminous coal C with corn straw at a mass ratio of 2:1 each ((AC-2) and (CC-2)) and that of bituminous coal B and corn straw at a mass ratio of 3:1 (BC-3) had the best gas production, and methane yields reached 17.28, 12.51, and 14.88 mL/g, respectively. The fermentation liquid had organic matter with more types and higher contents during the early and peak stages of gas production, and fewer types of organic matter were detected in the terminal stage. The degradation of lignocelluloses in the corn straw of AC-2 was higher. With the increase in fermentation time, the carbohydrates in the fermentation system increased and the degradation rate of cellulose decreased gradually. The abundance of genes related to nitrate reduction gradually increased, while that of sulfate reduction was on the contrary. Bacteria in the cofermentation system mainly metabolized carbohydrates. During cofermentation with high metamorphic coal, corn straw would be preferentially degraded. The structure of the archaea community changed from Methanosarcina and Methanothrix to Methanobacterium.

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. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.

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. In this study, the single factor test and response surface curve analysis are used to clarify the essential components in the culture medium and the optimal content of these chemicals. The influence of a single component on microbial community structure and major metabolic pathways in AD system are discussed. Under the optimal conditions, SEM observation show that the coal surface sediment is significantly reduced after AD process. The results of GC-MS show that there is no significant difference in the composition and content of organic compounds in the liquid phase before and after the optimization; the microbial community structure and gene function did not weaken with the decrease of culture medium addition, but formed a more targeted and stable microbial community.

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%. Metagenomic analysis showed that the addition of corn straw enriched the abundance of Methanosarcina in the combined fermentation system and promoted the complementary advantages of the microorganisms. At the same time, the abundance of genes that convert glucose into acetic acid (K00927, K01689, K01905, etc.) in the combined fermentation system increased, which is conducive to acidification process and biomethane production. In addition, there were the two key methanogenic pathways, namely aceticlastic (57.1%-63.5%) and hydrogenotrophic (23.4%-25.1%) methanogenesis, identified in the single coal fermentation system and the combined coal and corn straw fermentation system. Combined fermentation enhanced the hydrogenotrophic and methylotrophic methanogenic pathways by increasing the gene abundance of K00200 (methane production from CO2 and oxidation of coenzyme M to CO2), K00440 (participates in the binding to other known physiological receptors with hydrogen as a donor), and K00577 (methyltransferase).

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. Moreover, organic matter was decomposed and volatile fatty acids (VFAs) were consumed effectively. The abundance of Bacteroides in the bacterial community and Methanosarcina in the archaea was increased. In addition, the reduction of CO2 was the main methanogenic pathway, and adding CS raised the abundance of genes for key enzymes in metabolic pathways during methane metabolism. The results provide a novel method for the efficient methane production from CM.

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. More specifically, a stacked Hypergraph Aggregation Neural Network (HANN) layers are introduced to model the complicated interaction between local and global contexts, based on which better contextualized representations are obtained for CID relation extraction. In addition, the CID Relation Heterogeneous Graph is constructed to capture the information with different granularities and improve further the performance of CID relation classification. Experiments on a real-world dataset demonstrate the effectiveness of the proposed framework.

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. RESULTS: We propose a novel framework of reinforcement learning (RL) for the task of multiple biomedical events extraction. More specifically, trigger identification and argument detection are treated as main-task and subsidiary-task, respectively. Assigning the event type of triggers (in the main-task) is viewed as the action taken in RL, and the result of corresponding argument detection (i.e. the subsidiary-task) for the identified trigger is used for computing the reward of the taken action. Moreover, the result of the subsidiary-task is modeled as part of environment information in RL to help the procedure of trigger identification. In addition, external biomedical knowledge bases are employed for representation learning of biomedical text, which can improve the performance of biomedical event extraction. Results on two widely used biomedical corpora demonstrate that the proposed framework performs better than the selected baselines on the task of multiple events extraction. The ablation test indicates the contributions of RL and external KBs to the performance improvement in the proposed method. In addition, by modeling multiple events extraction under the RL framework, the supervised information is exploited more effectively than the classical supervised learning paradigm. Availability and implementationSource codes will be available at: https://github.com/David-WZhao/BioEE-RL.