MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Discovering trends and hotspots of biosafety and biosecurity research via machine learning.

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. RESULTS: To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. AVAILABILITY AND IMPLEMENTATION: The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Impact of Pulmonary Ventilation Dysfunction on Prognosis of Patients with Coronary Artery Disease: A Single-Center, Observational Study.

Background: Patients with coronary artery disease (CAD) often experience pulmonary ventilation dysfunction following their initial event. However, there is insufficient research exploring the relationship between this dysfunction and CAD prognosis. Methods: To address this gap, a retrospective observational study was conducted involving 3800 CAD patients without prior pulmonary ventilation disease who underwent cardiopulmonary exercise testing (CPET) during hospitalization between November 2015 and September 2021. The primary endpoint was a composite of major adverse cardiovascular events (MACE), such as death, myocardial infarction (MI), repeat revascularization, and stroke. Propensity score matching (PSM) was used to minimize selection bias between the two groups, with a subgroup analysis stratified by smoking status. Results: The results showed that patients were divided into normal (n = 2159) and abnormal (n = 1641) groups based on their pulmonary ventilation function detected by CPET, with 1469 smokers and 2331 non-smokers. The median follow-up duration was 1237 (25-75% interquartile range 695-1596) days. The primary endpoint occurred in 390 patients (10.26%). 1472 patients in each of the two groups were enrolled in the current analysis after PSM, respectively. However, pulmonary function was not associated with MACE before (hazard ratio (HR) 1.20, 95% confidence interval (95% CI) 0.99-1.47; Log-rank p = 0.069) or after PSM (HR 1.07, 95% CI 0.86-1.34; Log-rank p = 0.545) among the entire population. Nonetheless, pulmonary ventilation dysfunction was significantly associated with an increased risk of MACE in smoking patients (HR 1.65, 95% CI 1.25-2.18; p < 0.001) but not in non-smoking patients (HR 0.81, 95% CI 0.60-1.09; p = 0.159). In addition, there was a significant interaction between current smoking status and pulmonary ventilation dysfunction on MACE (p for interaction < 0.001). Conclusions: Pulmonary ventilation dysfunction identified through CPET was independently associated with long-term poor prognosis in smoking patients with CAD but not in the overall population.

Drug-Target Interaction Prediction Based on an Interactive Inference Network.

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Impact of Combined Modulation of Two Potassium Ion Currents on Spiral Waves and Turbulent States in the Heart.

In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving combined control of currents are superior to singular approaches. While previous studies have proposed some combination strategies, further reinforcement and supplementation are required, particularly in the context of controlling arrhythmias through the combined regulation of two potassium ion currents. This study employs the Luo-Rudy phase I cardiac model, modulating the maximum conductance of the time-dependent potassium current and the time-independent potassium current, to investigate the effects of this combined modulation on spiral waves and turbulent states. Numerical simulation results indicate that, compared to modulating a single current, combining reductions in the conductance of two potassium ion currents can rapidly control spiral waves and turbulent states in a short duration. This implies that employing blockers for both potassium ion currents concurrently represents a more efficient control strategy. The control outcomes of this study represent a novel and effective combination for antiarrhythmic interventions, offering potential avenues for new antiarrhythmic drug targets.

The bioinformatics toolbox for circRNA discovery and analysis.

Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects. RESULTS: In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ability to remotely monitor atrial high-rate episodes using a single-chamber implantable cardioverter-defibrillator with a floating atrial sensing dipole.

AIMS: To allow timely initiation of anticoagulation therapy for the prevention of stroke, the European guidelines on atrial fibrillation (AF) recommend remote monitoring (RM) of device-detected atrial high-rate episodes (AHREs) and progression of arrhythmia duration along pre-specified strata (6 min…<1 h, 1 h…<24 h, ≥ 24 h). We used the MATRIX registry data to assess the capability of a single-lead implantable cardioverter-defibrillator (ICD) with atrial sensing dipole (DX ICD system) to follow this recommendation in patients with standard indication for single-chamber ICD. METHODS AND RESULTS: In 1841 DX ICD patients with daily automatic RM transmissions, electrograms of first device-detected AHREs per patient in each duration stratum were adjudicated, and the corresponding positive predictive values (PPVs) for the detections to be true atrial arrhythmia were calculated. Moreover, the incidence and progression of new-onset AF was assessed in 1451 patients with no AF history. A total of 610 AHREs ≥6 min were adjudicated. The PPV was 95.1% (271 of 285) for episodes 6min…<1 h, 99.6% (253/254) for episodes 1 h…<24 h, 100% (71/71) for episodes ≥24 h, or 97.5% for all episodes (595/610). The incidence of new-onset AF was 8.2% (119/1451), and in 31.1% of them (37/119), new-onset AF progressed to a higher duration stratum. Nearly 80% of new-onset AF patients had high CHA2DS2-VASc stroke risk, and 70% were not on anticoagulation therapy. Age was the only significant predictor of new-onset AF. CONCLUSION: A 99.7% detection accuracy for AHRE ≥1 h in patients with DX ICD systems in combination with daily RM allows a reliable guideline-recommended screening for subclinical AF and monitoring of AF-duration progression.

Bilateral septal pacing in combination with coronary venous pacing for cardiac resynchronization therapy.

BACKGROUND: Conventional right ventricular pacing combined with coronary venous pacing (CVP) is a mainstay for cardiac resynchronization therapy (CRT). However, QRS duration of conventional CRT may be frequently more than 130 ms. This study aimed to evaluate the effectiveness of QRS narrowing by bilateral septal pacing (BSP) in combination with CVP for CRT (BSP-CRT). METHODS: Fourteen patients with QRS > 130 ms of conventional CRT after failure of physiological conduction system pacing were enrolled. Electrophysiologic characteristics were compared among different modes of CRT during procedure. BSP which was defined as capture of both sides of interventricular septum manifested as shortened R wave peak time without a right bundle branch block QRS pattern. RESULTS: BSP-CRT were successfully achieved in 85.7% (12/14) patients. QRS duration at baseline was 185 ± 13 ms and significantly narrowed to 156 ± 9 ms during conventional CRT (n = 14, P < .001), to 143 ± 7 ms during left ventricular septal pacing (LVSP) in combination with CVP for CRT (LVSP-CRT) (n = 9, P < .001), and further to 122 ± 10 ms during BSP-CRT (n = 12, P < .001). Notably, among 7 patients in whom both LVSP and BSP were achieved, BSP-CRT outperformed LVSP-CRT at QRS narrowing by 16% (P < .001). At 3-month follow-up, left ventricular ejection fraction improved from 29 ± 6% to 41 ± 8% (P < .001). CONCLUSIONS: BSP-CRT resulted in superior acute electrical synchronization to conventional CRT and might be considered as an alternative to conventional CRT with QRS more than 130 ms.

Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling.

Chaotic time series are widely present in practice, but due to their characteristics-such as internal randomness, nonlinearity, and long-term unpredictability-it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods.

In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain.

Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference.

LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBM.

Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules. The LightGBM module applies gradient boosting decision trees for feature processing, which improves LFDNN's ability to handle dense numerical features; the shallow model introduces the FM model for explicitly modeling the finite-order feature crosses, which strengthens the expressive ability of the model; the deep neural network module uses a fully connected feedforward neural network to allow the model to obtain more high-order feature crosses information and mine more data patterns in the features; finally, the Fusion module allows the shallow model and the deep model to obtain a better fusion effect. The results of comparison, parameter influence and ablation experiments on two real advertisement datasets shows that the LFDNN reaches better performance than the representative recommendation models.

LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property.

Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers' requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA-protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.

A dynamic programing approach to integrate gene expression data and network information for pathway model generation.

MOTIVATION: As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. RESULTS: To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. AVAILABILITY AND IMPLEMENTATION: IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Knockdown of circ_0075503 suppresses cell migration and invasion by regulating miR-15a-5p and KLF12 in endometriosis.

Endometriosis is an estrogen-dependent disease. Several researches have reported the dysregulated circular RNAs (circRNAs) in endometriosis, whereas the functions of circRNAs are largely unknown. This study aims to explore the role and mechanism of circ_0075503 in migration and invasion of eutopic endometrial stromal cells. 30 paired ectopic and eutopic endometrium tissues were collected from patients with endometriosis. And primary endometrial stromal cells (ESCs) were stimulated with estradiol (E2) to establish the in vitro cellular model of endometriosis. The levels of circ_0075503, miR-15a-5p and Krüppel-like factor 12 (KLF12) were measured by quantitative reverse transcription polymerase chain reaction or western blot assays. Cell viability, migration and invasion were examined via 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide, transwell assay or western blot assays. The target relationship between miR-15a-5p and circ_0075503 or KLF12 was analyzed by dual-luciferase reporter assay and RNA Immunoprecipitation (RIP) assay. Circ_0075503 expression was elevated in ectopic endometrium and ectopic ESCs. Down-regulation of circ_0075503 suppressed E2-induced promotion of cell viability, migration and invasion in eutopic ESCs. Circ_0075503 could act as a sponge for miR-15a-5p, and KLF12 was targeted by miR-15a-5p. Inhibition of miR-15a-5p reversed the effects of circ_0075503 knockdown on E2-treated ESCs migration and invasion. Besides, miR-15a-5p repressed E2-induced promotion effects on cell migration and invasion via targeting KLF12. Circ_0075503 could regulate KLF12 expression by sponging miR-15a-5p. Knockdown of circ_0075503 inhibited E2-induced enhancement of cell migration and invasion in eutopic ESCs by regulating miR-15a-5p/KLF12 axis, indicating a novel target for the treatment of endometriosis.

The change of cardiac axis deviation in catheter ablation of verapamil-sensitive idiopathic left ventricular tachycardia.

BACKGROUND: The underlying mechanism of verapamil-sensitive idiopathic left ventricular tachycardia (ILVT) has been postulated to be reentrant activation in the Purkinje fiber network of the left posterior fascicle or the left anterior fascicle (LAF). However, changing of cardiac axis deviation in sinus rhythm (SR) or during ILVT after radiofrequency catheter ablation (RFCA) has been rarely analyzed. METHODS: Of the 232 patients with sustained ILVT induced and surface electrocardiogram (ECG) in SR recorded before and after RFCA, the changes of ECG morphology in SR and during ILVT were analyzed. RESULTS: The surface ECG in SR changed in 114 (49.1%) patients after RFCA. ILVT could still be induced in 27 (23.7%) patients. In comparison with the original ILVT, three forms of ECG morphology were observed. In eight patients, the ILVT morphology was unchanged. In the 13 patients with ILVT axis deviation conversion after ablation, the successful target was more proximal. In the six patients with ILVT morphology change but without axis deviation conversion after ablation, the successful ablation site was more distal. Among 15 patients with recurrent ILVT during follow-up, seven patients had previous axis deviation changes in SR after RFCA, the changes maintained in four patients and recovered in three patients. CONCLUSIONS: The morphology changes on surface ECG in SR after RFCA would not be a necessary prerequisite or a good endpoint for ILVT ablation. To analyze ILVT morphology changes after ablation would help to further clarify an appropriate approach for catheter ablation of ILVT.

An Overview of Some Latest Development in Chinese Astronomy.

We present brief highlights and updates on some newer projects, both in operation/construction and in preparation stages, of astronomical research on Mainland China, with an emphasis on those involving international collaborations. Limited by the scope of this paper, this sample is not meant to be uniform nor comprehensive, and in some cases it may not be fully up to date. For more specific and detailed information on these or other projects, we refer the readers to the official websites of these projects and those of the National Astronomical Observatories, Chinese Academy of Sciences.

Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.

Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.