Drug-target interaction prediction using knowledge graph embedding.

The prediction of drug-target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.

Reaction Pathway Analysis of PET Deconstruction via Methanolysis and Tertiary Amine Catalysts.

Polyethylene terephthalate (PET) is a type of polymer frequently used in plastic packaging that significantly adds the amount of plastic waste found in landfills. One of the ways to recover valuable raw materials from postconsumer plastic is by depolymerizing PET into its monomeric constituents, which are dimethyl terephthalate (DMT) and ethylene glycol. PET depolymerization is often done in methanolysis with the help of acidic or base catalysts. Tertiary amine is one of the most attractive base catalysts for PET depolymerization in methanolysis since it does not lead to the generation of potentially environmentally harmful waste, unlike metal-based catalysts. However, the mechanism by which tertiary amines catalyze PET depolymerization in methanolysis remains unexplored. Developing a detailed mechanistic understanding of this process is important for improving plastic upcycling since it opens the possibility of employing various cheaper and more environmentally friendly reaction conditions. Using density functional theory and transition state analysis, we show that in the presence of tertiary amine catalysts, methanolysis of PET consists of multiple discrete-step reactions rather than a single concerted step. Furthermore, by comparing our calculations to recent experimental results, we were able to rationalize the DMT yield from the depolymerization process by relating it to charge polarization within tertiary amine catalysts, thus opening a pathway to identify atomic descriptors for future catalyst design.

Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module.

MOTIVATION: Biomedical relation extraction at the document level (Bio-DocRE) involves extracting relation instances from biomedical texts that span multiple sentences, often containing various entity concepts such as genes, diseases, chemicals, variants, etc. Currently, this task is usually implemented based on graphs or transformers. However, most work directly models entity features to relation prediction, ignoring the effectiveness of entity pair information as an intermediate state for relation prediction. In this article, we decouple this task into a three-stage process to capture sufficient information for improving relation prediction. RESULTS: We propose an innovative framework HTGRS for Bio-DocRE, which constructs a hierarchical tree graph (HTG) to integrate key information sources in the document, achieving relation reasoning based on entity. In addition, inspired by the idea of semantic segmentation, we conceptualize the task as a table-filling problem and develop a relation segmentation (RS) module to enhance relation reasoning based on the entity pair. Extensive experiments on three datasets show that the proposed framework outperforms the state-of-the-art methods and achieves superior performance. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/passengeryjy/HTGRS.

Location-enhanced syntactic knowledge for biomedical relation extraction.

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guidance for the semantic understanding and text representation of models. However, the utilization of syntactic knowledge in most studies is not exhaustive, and there is often a lack of fine-grained noise reduction, leading to confusion in relation classification. In this paper, we propose an attention generator that comprehensively considers both syntactic dependency type information and syntactic position information to distinguish the importance of different dependency connections. Additionally, we integrate positional information, dependency type information, and word representations together to introduce location-enhanced syntactic knowledge for guiding our biomedical relation extraction. Experimental results on three widely used English benchmark datasets in the biomedical domain consistently outperform a range of baseline models, demonstrating that our approach not only makes full use of syntactic knowledge but also effectively reduces the impact of noisy words.

Knowledge enhanced attention aggregation network for medicine recommendation.

The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients' clinical records often contain repeated medical information that can significantly impact their health condition. Most existing methods for modeling longitudinal patient information overlook the impact of individual diagnoses and procedures on the patient's health, resulting in insufficient patient representation and limited accuracy of medicine recommendations. Therefore, we propose a medicine recommendation model called KEAN, which is based on an attention aggregation network and enhanced graph convolution. Specifically, KEAN can aggregate individual diagnoses and procedures in patient visits to capture significant features that affect patients' diseases. We further incorporate medicine knowledge from complex medicine combinations, reduce drug-drug interactions (DDIs), and recommend medicines that are beneficial to patients' health. The experimental results on the MIMIC-III dataset demonstrate that our model outperforms existing advanced methods, which highlights the effectiveness of the proposed method.

Ionic Pairs-Engineered Fluorinated Covalent Organic Frameworks Toward Direct Air Capture of CO2.

The covalent organic frameworks (COFs) possessing high crystallinity and capability to capture low-concentration CO2 (400 ppm) from air are still underdeveloped. The challenge lies in simultaneously incorporating high-density active sites for CO2 insertion and maintaining the ordered structure. Herein, a structure engineering approach is developed to afford an ionic pair-functionalized crystalline and stable fluorinated COF (F-COF) skeleton. The ordered structure of the F-COF is well maintained after the integration of abundant basic fluorinated alcoholate anions, as revealed by synchrotron X-ray scattering experiments. The breakthrough test demonstrates its attractive performance in capturing (400 ppm) CO2 from gas mixtures via O─C bond formation, as indicated by the in situ spectroscopy and operando nuclear magnetic resonance spectroscopy using 13C-labeled CO2 sources. Both theoretical and experimental thermodynamic studies reveal the reaction enthalpy of ≈-40 kJ mol-1 between CO2 and the COF scaffolds. This implies weaker interaction strength compared with state-of-the-art amine-derived sorbents, thus allowing complete CO2 release with less energy input. The structure evolution study from synchrotron X-ray scattering and small-angle neutron scattering confirms the well-maintained crystalline patterns after CO2 insertion. The as-developed proof-of-concept approach provides guidance on anchoring binding sites for direct air capture (DAC) of CO2 in crystalline scaffolds.

Temporal Network Embedding Enhanced With Long-Range Dynamics and Self-Supervised Learning.

Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks. In LongTNE, we employ graph self-supervised learning (Graph SSL) to optimize the establishment probability of deep links in each network snapshot. We also present an accumulated forward update (AFU) module to fathom global temporal evolution among multiple network snapshots. The empirical results on six temporal networks demonstrate that, in addition to achieving state-of-the-art performance on network mining tasks, LongTNE can be handily extended to existing TNE methods.

Minimal Kinetic Model of Direct Air Capture of CO2 by Supported Amine Sorbents in Dry and Humid Conditions.

Dilute concentration (∼400 ppm) and humidity are two important factors in the direct air capture (DAC) of CO2 by supported sorbents. In this work, a minimal DAC CO2 adsorption-kinetics model was formulated for supported amine sorbents under dry and humid conditions. Our model fits well with a recent DAC experiment with supported amine sorbent in both dry and humid conditions. Temperature and flow rate effects on breakthrough curves were quantitatively captured, and increasing temperature led to faster CO2 adsorption kinetics. Moisture was shown to broaden the breakthrough curve with slower CO2 adsorption kinetics but significantly improve the uptake capacity. The present minimal model provides a versatile platform for kinetic modeling of the DAC of CO2 on supported amine and other chemisorption systems.

CO-Assisted Methane Oxidation into Oxygenates over Surface Platinum-Titanium Alloyed Layers.

Methane oxidation using molecular oxygen remains a grand challenge in which the obstacle is not only the activation of methane but also the reaction with oxygen, considering the mismatch of the ground spin states. Herein, we report TiO2-supported Pt nanocrystals (Pt/TiO2) with surface Pt-Ti alloyed layers that directly convert methane into oxygenates by using O2 as the oxidant with the assistance of CO. The oxygenate yield reached 749.8 mmol gPt-1 in a H2O aqueous solution over 0.1% Pt/TiO2 under 31 bar of mixed gas (20:5:6 CH4:CO:O2) at 150 °C for 3 h, while the CH3OH selectivity was 62.3%. On the basis of the control experiments and spectroscopic results, we identified the surface Pt-Ti alloy as the active sites. Moreover, CO promoted the dissociation of O2 on the surface of Pt-Ti alloyed layers and the subsequent activation of CH4 to form oxygenated products.

Efficient fluoride removal using nano MgO: mechanisms and performance evaluation.

In this study, highly efficient fluoride removal of nano MgO was successfully synthesized using a simple hydrothermal precipitation method. Hexadecyl trimethyl ammonium bromide (CTMAB) was utilized as a surfactant. Its long-chain structure tightly wrapped around the precursor crystal of basic magnesium chloride, inhibiting the growth of precursor crystals, reducing their size, and improving crystal dispersion. This process enhanced the adsorption capacity of nano MgO for fluoride. The adsorption performance of nano MgO on fluoride was investigated. The results indicate that pseudo-second-order kinetics and the Langmuir isotherm model can describe the adsorption behavior for fluoride, with a maximum adsorption capacity of 122.47 mg/g. Methods such as XRD, SEM, XPS, and FTIR were employed to study the adsorption mechanisms of the adsorbent. Additionally, factors potentially affecting adsorption performance in practical applications, such as pH and competing ions, were examined. This study enhances our profound understanding of the defluorination effectiveness and mechanisms of nano MgO.

Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks.

OBJECTIVE: Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks. MATERIALS AND METHODS: We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks. RESULTS: Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking. CONCLUSION: Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models.

Predicting Protein Functions Based on Heterogeneous Graph Attention Technique.

In bioinformatics, protein function prediction stands as a fundamental area of research and plays a crucial role in addressing various biological challenges, such as the identification of potential targets for drug discovery and the elucidation of disease mechanisms. However, known functional annotation databases usually provide positive experimental annotations that proteins carry out a given function, and rarely record negative experimental annotations that proteins do not carry out a given function. Therefore, existing computational methods based on deep learning models focus on these positive annotations for prediction and ignore these scarce but informative negative annotations, leading to an underestimation of precision. To address this issue, we introduce a deep learning method that utilizes a heterogeneous graph attention technique. The method first constructs a heterogeneous graph that covers the protein-protein interaction network, ontology structure, and positive and negative annotation information. Then, it learns embedding representations of proteins and ontology terms by using the heterogeneous graph attention technique. Finally, it leverages these learned representations to reconstruct the positive protein-term associations and score unobserved functional annotations. It can enhance the predictive performance by incorporating these known limited negative annotations into the constructed heterogeneous graph. Experimental results on three species (i.e., Human, Mouse, and Arabidopsis) demonstrate that our method can achieve better performance in predicting new protein annotations than state-of-the-art methods.

Atomically synergistic Zn-Cr catalyst for iso-stoichiometric co-conversion of ethane and CO2 to ethylene and CO.

Developing atomically synergistic bifunctional catalysts relies on the creation of colocalized active atoms to facilitate distinct elementary steps in catalytic cycles. Herein, we show that the atomically-synergistic binuclear-site catalyst (ABC) consisting of [Formula: see text]-O-Cr6+ on zeolite SSZ-13 displays unique catalytic properties for iso-stoichiometric co-conversion of ethane and CO2. Ethylene selectivity and utilization of converted CO2 can reach 100 % and 99.0% under 500 °C at ethane conversion of 9.6%, respectively. In-situ/ex-situ spectroscopic studies and DFT calculations reveal atomic synergies between acidic Zn and redox Cr sites. [Formula: see text] ([Formula: see text]) sites facilitate ß-C-H bond cleavage in ethane and the formation of Zn-Hδ- hydride, thereby the enhanced basicity promotes CO2 adsorption/activation and prevents ethane C-C bond scission. The redox Cr site accelerates CO2 dissociation by replenishing lattice oxygen and facilitates H2O formation/desorption. This study presents the advantages of the ABC concept, paving the way for the rational design of novel advanced catalysts.

NiFe Nanoparticle Nest Supported on Graphene as Electrocatalyst for Highly Efficient Oxygen Evolution Reaction.

Designing cost-efffective electrocatalysts for the oxygen evolution reaction (OER) holds significant importance in the progression of clean energy generation and efficient energy storage technologies, such as water splitting and rechargeable metal-air batteries. In this work, an OER electrocatalyst is developed using Ni and Fe precursors in combination with different proportions of graphene oxide. The catalyst synthesis involved a rapid reduction process, facilitated by adding sodium borohydride, which successfully formed NiFe nanoparticle nests on graphene support (NiFe NNG). The incorporation of graphene support enhances the catalytic activity, electron transferability, and electrical conductivity of the NiFe-based catalyst. The NiFe NNG catalyst exhibits outstanding performance, characterized by a low overpotential of 292.3 mV and a Tafel slope of 48 mV dec-1, achieved at a current density of 10 mA cm- 2. Moreover, the catalyst exhibits remarkable stability over extended durations. The OER performance of NiFe NNG is on par with that of commercial IrO2 in alkaline media. Such superb OER catalytic performance can be attributed to the synergistic effect between the NiFe nanoparticle nests and graphene, which arises from their large surface area and outstanding intrinsic catalytic activity. The excellent electrochemical properties of NiFe NNG hold great promise for further applications in energy storage and conversion devices.

Hyperbolic hierarchical knowledge graph embeddings for biological entities.

Predicting relationships between biological entities can greatly benefit important biomedical problems. Previous studies have attempted to represent biological entities and relationships in Euclidean space using embedding methods, which evaluate their semantic similarity by representing entities as numerical vectors. However, the limitation of these methods is that they cannot prevent the loss of latent hierarchical information when embedding large graph-structured data into Euclidean space, and therefore cannot capture the semantics of entities and relationships accurately. Hyperbolic spaces, such as Poincaré ball, are better suited for hierarchical modeling than Euclidean spaces. This is because hyperbolic spaces exhibit negative curvature, causing distances to grow exponentially as they approach the boundary. In this paper, we propose HEM, a hyperbolic hierarchical knowledge graph embedding model to generate vector representations of bio-entities. By encoding the entities and relations in the hyperbolic space, HEM can capture latent hierarchical information and improve the accuracy of biological entity representation. Notably, HEM can preserve rich information with a low dimension compared with the methods that encode entities in Euclidean space. Furthermore, we explore the performance of HEM in protein-protein interaction prediction and gene-disease association prediction tasks. Experimental results demonstrate the superior performance of HEM over state-of-the-art baselines. The data and code are available at : https://github.com/Nan-ll/HEM.

Biomedical document relation extraction with prompt learning and KNN.

Document-level relation extraction is designed to recognize connections between entities a cross sentences or between sentences. The current mainstream document relation extraction model is mainly based on the graph method or combined with the pre-trained language model, which leads to the relatively complex process of the whole workflow. In this work, we propose biomedical relation extraction based on prompt learning to avoid complex relation extraction processes and obtain decent performance. Particularity, we present a model that combines prompt learning with T5 for document relation extraction, by integrating a mask template mechanism into the model. In addition, this work also proposes a few-shot relation extraction method based on the K-nearest neighbor (KNN) algorithm with prompt learning. We select similar semantic labels through KNN, and subsequently conduct the relation extraction. The results acquired from two biomedical document benchmarks indicate that our model can improve the learning of document semantic information, achieving improvements in the relation F1 score of 3.1% on CDR.

Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning.

MOTIVATION: Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedical named entity recognition (BioNER) often encounters limited human-labeled data in real-world scenarios, leading to poor generalization performance when training only a few labeled instances. Recent approaches either leverage cross-domain high-resource data or fine-tune the pre-trained masked language model using limited labeled samples to generate new synthetic data, which is easily stuck in domain shift problems or yields low-quality synthetic data. Therefore, in this article, we study a more realistic scenario, i.e. few-shot learning for BioNER. RESULTS: Leveraging the domain knowledge graph, we propose knowledge-guided instance generation for few-shot BioNER, which generates diverse and novel entities based on similar semantic relations of neighbor nodes. In addition, by introducing question prompt, we cast BioNER as question-answering task and propose prompt contrastive learning to improve the robustness of the model by measuring the mutual information between query-answer pairs. Extensive experiments conducted on various few-shot settings show that the proposed framework achieves superior performance. Particularly, in a low-resource scenario with only 20 samples, our approach substantially outperforms recent state-of-the-art models on four benchmark datasets, achieving an average improvement of up to 7.1% F1. AVAILABILITY AND IMPLEMENTATION: Our source code and data are available at https://github.com/cpmss521/KGPC.

Protocol for the automatic extraction of epidemiological information via a pre-trained language model.

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2.

Harnessing the Hybridization of a Metal-Organic Framework and Superbase-Derived Ionic Liquid for High-Performance Direct Air Capture of CO2.

Direct air capture (DAC) of CO2 has emerged as the most promising "negative carbon emission" technologies. Despite being state-of-the-art, sorbents deploying alkali hydroxides/amine solutions or amine-modified materials still suffer from unsolved high energy consumption and stability issues. In this work, composite sorbents are crafted by hybridizing a robust metal-organic framework (Ni-MOF) with superbase-derived ionic liquid (SIL), possessing well maintained crystallinity and chemical structures. The low-pressure (0.4 mbar) volumetric CO2 capture assessment and a fixed-bed breakthrough examination with 400 ppm CO2 gas flow reveal high-performance DAC of CO2 (CO2 uptake capacity of up to 0.58 mmol g-1 at 298 K) and exceptional cycling stability. Operando spectroscopy analysis reveals the rapid (400 ppm) CO2 capture kinetics and energy-efficient/fast CO2 releasing behaviors. The theoretical calculation and small-angle X-ray scattering demonstrate that the confinement effect of the MOF cavity enhances the interaction strength of reactive sites in SIL with CO2 , indicating great efficacy of the hybridization. The achievements in this study showcase the exceptional capabilities of SIL-derived sorbents in carbon capture from ambient air in terms of rapid carbon capture kinetics, facile CO2 releasing, and good cycling performance.

A patient information mining network for drug recommendation.

As an important task of natural language processing, medication recommendation aims to recommend medication combinations according to the electronic health record, which can also be regarded as a multi-label classification task. But patients often have multiple diseases simultaneously, and the model must consider drug-drug interactions (DDI) of medication combinations when recommending medications, making medication recommendation more difficult. There is little existing work to explore the changes in patient conditions. However, these changes may point to future trends in patient conditions that are critical for reducing DDI rates in recommended drug combinations. In this paper, we proposed the Patient Information Mining Network (PIMNet), which models the current core medications of patient by mining the temporal and spatial changes of patient medication order and patient condition vector, and allocates some auxiliary medications as the currently recommended medication combination. The experimental results show that the proposed model greatly reduces the recommended DDI of medications while achieving results no lower than the state-of-the-art results.