bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stratified neural networks in a time-to-event setting
Authors: Kuruc Fabrizio , Binder Harald , Hess Moritz 
Published: September 2021

AbstractDeep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.
Prediction of RNA secondary structure including pseudoknots for long sequences
Authors: Sato Kengo , Kato Yuki 
Published: September 2021

AbstractRNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is infeasible to construct an efficiently computable secondary structure model including pseudoknots, secondary structure prediction methods considering pseudoknots are not yet widely available. We developed IPknot, which uses heuristics to speed up computations, but it has remained difficult to apply it to long sequences, such as messenger RNA and viral RNA, because it requires cubic computational time with respect to sequence length and has threshold parameters that need to be manually adjusted. Here, we propose an improvement of IPknot that enables calculation in linear time by employing the LinearPartition model and automatically selects the optimal threshold parameters based on the pseudo-expected accuracy. In addition, IPknot showed favorable prediction accuracy across a wide range of conditions in our exhaustive benchmarking, not only for single sequences but also for multiple alignments.
scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding
Authors: Wu Hao , Wu Yingfu , Jiang Yuhong , Zhou Bing , Zhou Haoru , Chen Zhongli , Xiong Yi , Liu Quanzhong , Zhang Hongming 
Published: September 2021

Abstract
Single-cell Hi-C data are a common data source for studying the differences in the three-dimensional structure of cell chromosomes. The development of single-cell Hi-C technology makes it possible to obtain batches of single-cell Hi-C data. How to quickly and effectively discriminate cell types has become one hot research field. However, the existing computational methods to predict cell types based on Hi-C data are found to be low in accuracy. Therefore, we propose a high accuracy cell classification algorithm, called scHiCStackL, based on single-cell Hi-C data. In our work, we first improve the existing data preprocessing method for single-cell Hi-C data, which allows the generated cell embedding better to represent cells. Then, we construct a two-layer stacking ensemble model for classifying cells. Experimental results show that the cell embedding generated by our data preprocessing method increases by 0.23, 1.22, 1.46 and 1.61$\%$ comparing with the cell embedding generated by the previously published method scHiCluster, in terms of the Acc, MCC, F1 and Precision confidence intervals, respectively, on the task of classifying human cells in the ML1 and ML3 datasets. When using the two-layer stacking ensemble framework with the cell embedding, scHiCStackL improves by 13.33, 19, 19.27 and 14.5 over the scHiCluster, in terms of the Acc, ARI, NMI and F1 confidence intervals, respectively. In summary, scHiCStackL achieves superior performance in predicting cell types using the single-cell Hi-C data. The webserver and source code of scHiCStackL are freely available at http://hww.sdu.edu.cn:8002/scHiCStackL/ and https://github.com/HaoWuLab-Bioinformatics/scHiCStackL, respectively.
Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data
Authors: Song Wenjing , Wang Weiwen , Dai Dao-Qing 
Published: September 2021

AbstractThe discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.
Characterizing viral circRNAs and their application in identifying circRNAs in viruses
Authors: Niu Mengting , Ju Ying , Lin Chen , Zou Quan 
Published: September 2021

Abstract
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism, which play an important role in a variety of biological activities. Viruses can encode circRNA, and viral circRNAs have been found in multiple single-stranded and double-stranded viruses. However, the characteristics and functions of viral circRNAs remain unknown. Sequence alignment showed that viral circRNAs are less conserved than circRNAs in animal, indicating that the viral circRNAs may evolve rapidly. Through the analysis of the sequence characteristics of viral circRNAs and circRNAs in animal, it was found that viral circRNAs and animals circRNAs are similar in nucleic acid composition, but have obvious differences in secondary structure and autocorrelation characteristics. Based on these characteristics of viral circRNAs, machine learning algorithms were employed to construct a prediction model to identify viral circRNA. Additionally, analysis of the interaction between viral circRNA and miRNAs showed that viral circRNA is expected to interact with 518 human miRNAs, and preliminary analysis of the role of viral circRNA. And it has been also found that viral circRNAs may be involved in many KEGG pathways related to nervous system and cancer. We curated an online server, and the data and code are available: http://server.malab.cn/viral-CircRNA/.
Heterogeneous graph attention network based on meta-paths for lncRNA–disease association prediction
Authors: Zhao Xiaosa , Zhao Xiaowei , Yin Minghao 
Published: September 2021

AbstractMotivation: Discovering long noncoding RNA (lncRNA)–disease associations is a fundamental and critical part in understanding disease etiology and pathogenesis. However, only a few lncRNA–disease associations have been identified because of the time-consuming and expensive biological experiments. As a result, an efficient computational method is of great importance and urgently needed for identifying potential lncRNA–disease associations. With the ability of exploiting node features and relationships in network, graph-based learning models have been commonly utilized by these biomolecular association predictions. However, the capability of these methods in comprehensively fusing node features, heterogeneous topological structures and semantic information is distant from optimal or even satisfactory. Moreover, there are still limitations in modeling complex associations between lncRNAs and diseases. Results: In this paper, we develop a novel heterogeneous graph attention network framework based on meta-paths for predicting lncRNA–disease associations, denoted as HGATLDA. At first, we conduct a heterogeneous network by incorporating lncRNA and disease feature structural graphs, and lncRNA–disease topological structural graph. Then, for the heterogeneous graph, we conduct multiple metapath-based subgraphs and then utilize graph attention network to learn node embeddings from neighbors of these homogeneous and heterogeneous subgraphs. Next, we implement attention mechanism to adaptively assign weights to multiple metapath-based subgraphs and get more semantic information. In addition, we combine neural inductive matrix completion to reconstruct lncRNA–disease associations, which is applied for capturing complicated associations between lncRNAs and diseases. Moreover, we incorporate cost-sensitive neural network into the loss function to tackle the commonly imbalance problem in lncRNA–disease association prediction. Finally, extensive experimental results demonstrate the effectiveness of our proposed framework.
Learning representation for multiple biological networks via a robust graph regularized integration approach
Authors: Zhang Xiwen , Wang Weiwen , Ren Chuan-Xian , Dai Dao-Qing 
Published: September 2021

AbstractLearning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug–target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.
Hypergraph-based logistic matrix factorization for metabolite–disease interaction prediction
Authors: Ma Yingjun , Ma Yuanyuan 
Published: September 2021

AbstractMotivationFunction-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships.ResultsIn this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite–disease interactions. In computational experiments, HGLMF accurately predicted the metabolite–disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies.Availability and implementationThe codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF.Supplementary informationSupplementary data are available at Bioinformatics online.
Eutrophication on Coral Reefs: What Is the Evidence for Phase Shifts, Nutrient Limitation and Coral Bleaching
Authors: Lesser Michael P 
Published: September 2021

Abstract
Coral reefs continue to experience extreme environmental pressure from climate change stressors, but many coral reefs are also exposed to eutrophication. It has been proposed that changes in the stoichiometry of ambient nutrients increase the mortality of corals, whereas eutrophication may facilitate phase shifts to macroalgae-dominated coral reefs when herbivory is low or absent. But are corals ever nutrient limited, and can eutrophication destabilize the coral symbiosis making it more sensitive to environmental stress because of climate change? The effects of eutrophication are confounded not just by the effects of climate change but by the presence of chemical pollutants in industrial, urban, and agricultural wastes. Because of these confounding effects, the increases in nutrients or changes in their stoichiometry in coastal environments, although they are important at the organismal and community level, cannot currently be disentangled from each other or from the more significant effects of climate change stressors on coral reefs.
A Science Agenda to Inform Natural Resource Management Decisions in an Era of Ecological Transformation
Authors: Crausbay Shelley D , Sofaer Helen R , Cravens Amanda E , Chaffin Brian C , Clifford Katherine R , Gross John E , Knapp Corrine N , Lawrence David J , Magness Dawn R , Miller-Rushing Abraham J , Schuurman Gregor W , Stevens-Rumann Camille S 
Published: September 2021

AbstractEarth is experiencing widespread ecological transformation in terrestrial, freshwater, and marine ecosystems that is attributable to directional environmental changes, especially intensifying climate change. To better steward ecosystems facing unprecedented and lasting change, a new management paradigm is forming, supported by a decision-oriented framework that presents three distinct management choices: resist, accept, or direct the ecological trajectory. To make these choices strategically, managers seek to understand the nature of the transformation that could occur if change is accepted while identifying opportunities to intervene to resist or direct change. In this article, we seek to inspire a research agenda for transformation science that is focused on ecological and social science and based on five central questions that align with the resist–accept–direct (RAD) framework. Development of transformation science is needed to apply the RAD framework and support natural resource management and conservation on our rapidly changing planet.
Mobile health technology for remote home monitoring after surgery: a meta-analysis
Authors: Dawes A J , Lin A Y , Varghese C , Russell M M , Lin A Y 
Published: September 2021

Abstract

Background
Mobile health (mHealth) technology has been proposed as a method of improving post-discharge surveillance. Little is known about how mHealth has been used to track patients after surgery and whether its use is associated with differences in postoperative recovery.

Methods
Three databases (PubMed, MEDLINE and the Cochrane Central Registry of Controlled Trials) were searched to identify studies published between January 1999 and February 2021. Mobile health was defined as any smartphone or tablet computer capable of electronically capturing health-related patient information and transmitting these data to the clinical team. Comparable outcomes were pooled via meta-analysis with additional studies compiled via narrative review. The quality of each study was assessed based on Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria.

Results
Forty-five articles met inclusion criteria. While the majority of devices were designed to capture general health information, others were specifically adapted to the expected outcomes or potential complications of the index procedure. Exposure to mHealth was associated with fewer emergency department visits (odds ratio 0.42, 95 per cent c.i. 0.23 to 0.79) and readmissions (odds ratio 0.47, 95 per cent c.i. 0.29 to 0.77) as well as accelerated improvements in quality of life after surgery. There were limited data on other postoperative outcomes.

Conclusion
Remote home monitoring via mHealth is feasible, adaptable, and may even promote more effective postoperative care. Given the rapid expansion of mHealth, physicians and policymakers need to understand these technologies better so that they can be integrated into high-quality clinical care.
Early postoperative arterial lactate concentrations to stratify risk of post-hepatectomy liver failure
Authors: Niederwieser Thomas , Braunwarth Eva , Dasari Bobby V M , Pufal Kamil , Szatmary Peter , Hackl Hubert , Haselmann Clemens , Connolly Catherine E , Cardini Benno , Öfner Dietmar , Roberts Keith , Malik Hassan , Stättner Stefan , Primavesi Florian 
Published: September 2021

Abstract

Background
Post-hepatectomy liver failure (PHLF) represents the major determinant for death after liver resection. Early recognition is essential. Perioperative lactate dynamics for risk assessment of PHLF and associated morbidity were evaluated.

Methods
This was a multicentre observational study of patients undergoing hepatectomy with validation in international high-volume units. Receiver operating characteristics analysis and cut-off calculation for the predictive value of lactate for clinically relevant International Study Group of Liver Surgery grade B/C PHLF (clinically relevant PHLF (CR-PHLF)) were performed. Lactate and other perioperative factors were assessed in a multivariable CR-PHLF regression model.

Results
The exploratory cohort comprised 509 patients. CR-PHLF, death, overall morbidity and severe morbidity occurred in 7.7, 3.3, 40.9 and 29.3 per cent of patients respectively. The areas under the curve (AUCs) regarding CR-PHLF were 0.829 (95 per cent c.i. 0.770 to 0.888) for maximum lactate within 24 h (Lactate_Max) and 0.870 (95 per cent c.i. 0.818 to 0.922) for postoperative day 1 levels (Lactate_POD1). The respective AUCs in the validation cohort (482 patients) were 0.812 and 0.751 and optimal Lactate_Max cut-offs were identical in both cohorts. Exploration cohort patients with Lactate_Max 50 mg/dl or greater more often developed CR-PHLF (50.0 per cent) than those with Lactate_Max between 20 and 49.9 mg/dl (7.4 per cent) or less than 20 mg/dl (0.5 per cent; P &lt; 0.001). This also applied to death (18.4, 2.7 and 1.4 per cent), severe morbidity (71.1, 35.7 and 14.1 per cent) and associated complications such as acute kidney injury (26.3, 3.1 and 2.3 per cent) and haemorrhage (15.8, 3.1 and 1.4 per cent). These results were confirmed in the validation group. Combining Lactate_Max with Lactate_POD1 further increased AUC (ΔAUC = 0.053) utilizing lactate dynamics for risk assessment. Lactate_Max, major resections, age, cirrhosis and chronic kidney disease were independent risk factors for CR-PHLF. A freely available calculator facilitates clinical risk stratification (www.liver-calculator.com).

Conclusion
Early postoperative lactate values are powerful, readily available markers for CR-PHLF and associated complications after hepatectomy with potential for guiding postoperative care.
Presented in part as an oral video abstract at the 2020 online Congress of the European Society for Surgical Research and the 2021 Congress of the Austrian Surgical Society.