bluuebunny/crossref_metadata_2025_split huggingface dataset:
MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph
Authors: Vandewiele Gilles , Steenwinckel Bram , Turck Filip De , Ongenae Femke 
Published: December 2020

Abstract
Background
Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care.

Methods
We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime.

Results
We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable.

Conclusions
The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance.
Treatment effect prediction with adversarial deep learning using electronic health records
Authors: Chu Jiebin , Dong Wei , Wang Jinliang , He Kunlun , Huang Zhengxing 
Published: December 2020

AbstractBackgroundTreatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption of electronic health records (EHRs) has provided a comprehensive data source for intelligent clinical applications including the TEP investigated in this study.MethodWe examined the problem of using a large volume of heterogeneous EHR data to predict treatment effects and developed an adversarial deep treatment effect prediction model to address the problem. Our model employed two auto-encoders for learning the representative and discriminative features of both patient characteristics and treatments from EHR data. The discriminative power of the learned features was further enhanced by decoding the correlational information between the patient characteristics and subsequent treatments by means of a generated adversarial learning strategy. Thereafter, a logistic regression layer was appended on the top of the resulting feature representation layer for TEP.ResultThe proposed model was evaluated on two real clinical datasets collected from the cardiology department of a Chinese hospital. In particular, on acute coronary syndrome (ACS) dataset, the proposed adversarial deep treatment effect prediction (ADTEP) (0.662) exhibited 1.4, 2.2, and 6.3% performance gains in terms of the area under the ROC curve (AUC) over deep treatment effect prediction (DTEP) (0.653), logistic regression (LR) (0.648), and support vector machine (SVM) (0.621), respectively. As for heart failure (HF) case study, the proposed ADTEP also outperformed all benchmarks. The experimental results demonstrated that our proposed model achieved competitive performance compared to state-of-the-art models in tackling the TEP problem.ConclusionIn this work, we propose a novel model to address the TEP problem by utilizing a large volume of observational data from EHR. With adversarial learning strategy, our proposed model can further explore the correlational information between patient statuses and treatments to extract more robust and discriminative representation of patient samples from their EHR data. Such representation finally benefits the model on TEP. The experimental results of two case studies demonstrate the superiority of our proposed method compared to state-of-the-art methods.
A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets
Authors: Zhang Li , Hu Jiamei , Xu Qianzhi , Li Fang , Rao Guozheng , Tao Cui 
Published: December 2020

Abstract
Background
Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.

Methods
First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.

Results and conclusions
We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.
KGen: a knowledge graph generator from biomedical scientific literature
Authors: Rossanez Anderson , dos Reis Julio Cesar , Torres Ricardo da Silva , de Ribaupierre Hélène 
Published: December 2020

Abstract
Background
Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society.

Methods
In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool.

Results
The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation.

Conclusions
We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.
A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction
Authors: Zhang Zhen , Qiu Hang , Li Weihao , Chen Yucheng 
Published: December 2020

Abstract
Background
Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources.

Methods
In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly,
we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast.

Results
The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713).

Conclusion
It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.
Nature of dispensing errors in selected hospitals providing free healthcare: a multi-center study in Sri Lanka
Authors: Dilsha R. A. N. , Kularathne H. M. I. P. , Mujammil M. T. M. , Irshad S. M. M. , Samaranayake N. R. 
Published: December 2020

Abstract
Background
Dispensing errors, known to result in significant patient harm, are preventable if their nature is known and recognized. However, there is a scarcity of such data on dispensing errors particularly in resource poor settings, where healthcare is provided free-of-charge. Therefore, the purpose of this study was to determine the types, and prevalence of dispensing errors in a selected group of hospitals in Sri Lanka.

Methods
A prospective, cross sectional, multi-center study on dispensing errors was conducted, in a single tertiary care, and two secondary care hospitals, in a cohort of 420 patients attending medical, surgical, diabetic and pediatric clinics. The patients were selected according to the population size, through consecutive sampling. The prescription audit was conducted in terms of dispensing errors which were categorized as i) content, ii) labelling, iii) documentation, iv) concomitant, and v) other errors based on in-house developed definitions.

Results
A total of 420 prescriptions (1849 medicines) were analyzed (Hospital-I, 248 prescriptions-1010 medicines; Hospital-II, 84 prescriptions-400 medicines; Hospital-III, 88 prescriptions-439 medicines), and a cumulative total of 16,689 dispensing errors (at least one dispensing error in a prescription) were detected. Labelling errors were the most frequent dispensing error (63.1%; N = 10,523; Mostly missing information on the dispensing label), followed by concomitant prescribing and dispensing errors (20.5%; N = 3425; Missing prescribing information overlooked by the pharmacist), documentation errors (10.6%; N = 1772 Missing identification of pharmacist on dispensing label), clinically significant medication interactions overlooked by pharmacists (0.5%; N = 82), content errors (4.9%; N = 812; Discrepancies between medication dispensed and prescription order), medications dispensed in unsuitable packaging (0.4%; N = 74), and lastly medication dispensed to the wrong patient (0.01%; N = 1).

Conclusions
Dispensing errors are frequent in Sri Lankan hospitals which operate with limited resources and provide free healthcare to all citizenry. Over one half of the errors were labeling errors with minimal content errors. Awareness on common types of dispensing errors and emphasis on detecting them could improve medication safety in Sri Lankan hospitals.
Challenges to effective governance in a low income healthcare system: a qualitative study of stakeholder perceptions in Malawi
Authors: Masefield Sarah C. , Msosa Alan , Grugel Jean 
Published: December 2020

Abstract
Background
All countries face challenging decisions about healthcare coverage. Malawi has committed to achieving Universal Health Coverage (UHC) by 2030, the timeframe set out by the Sustainable Development Goals (SDGs). As in other low income countries, scarce resources stand in the way of more equitable health access and quality in Malawi. Its health sector is highly dependent on donor contributions, and recent poor governance of government-funded healthcare saw donors withdraw funding, limiting services and resources. The 2017 National Health Plan II and accompanying Health Strategic Plan II identify the importance of improved governance and strategies to achieve more effective cooperation with stakeholders. This study explores health sector stakeholders’ perceptions of the challenges to improving governance in Malawi’s national health system within the post-2017 context of government attempts to articulate a way forward.

Methods
A qualitative study design was used. Interviews were conducted with 22 representatives of major international and faith-based non-government organisations, civil society organisations, local government and government-funded organisations, and governance bodies operating in Malawi. Open questions were asked about experiences and perceptions of the functioning of the health system and healthcare decision-making. Content relating to healthcare governance was identified in the transcripts and field notes and analysed using inductive content analysis.

Results
Stakeholders view governance challenges as a significant barrier to achieving a more effective and equitable health system. Three categories were identified: accountability (enforceability; answerability; stakeholder-led initiatives); health resource management (healthcare financing; drug supply); influence in decision-making (unequal power; stakeholder engagement).

Conclusions
Health sector stakeholders see serious political, structural, and financial challenges to improving governance in the national health system in Malawi which will impact the government’s goal of achieving UHC by 2030. Stakeholders identify the need for improved oversight, implementation, service delivery and social accountability of government-funded service providers to communities. Eighteen months after the introduction of the policy documents, they see little evidence of improved governance and have little or no confidence in the government’s ability to deliver UHC. The difficulties stakeholders perceive in relation to building equitable and effective healthcare governance in Malawi have relevance for other resource-limited countries which have also committed to the goal of UHC.
Resilin matrix distribution, variability and function in Drosophila
Authors: Lerch Steven , Zuber Renata , Gehring Nicole , Wang Yiwen , Eckel Barbara , Klass Klaus-Dieter , Lehmann Fritz-Olaf , Moussian Bernard 
Published: December 2020

Abstract
Background
Elasticity prevents fatigue of tissues that are extensively and repeatedly deformed. Resilin is a resilient and elastic extracellular protein matrix in joints and hinges of insects. For its mechanical properties, Resilin is extensively analysed and applied in biomaterial and biomedical sciences. However, there is only indirect evidence for Resilin distribution and function in an insect. Commonly, the presence of dityrosines that covalently link Resilin protein monomers (Pro-Resilin), which are responsible for its mechanical properties and fluoresce upon UV excitation, has been considered to reflect Resilin incidence.

Results
Using a GFP-tagged Resilin version, we directly identify Resilin in pliable regions of the Drosophila body, some of which were not described before. Interestingly, the amounts of dityrosines are not proportional to the amounts of Resilin in different areas of the fly body, arguing that the mechanical properties of Resilin matrices vary according to their need. For a functional analysis of Resilin matrices, applying the RNA interference and Crispr/Cas9 techniques, we generated flies with reduced or eliminated Resilin function, respectively. We find that these flies are flightless but capable of locomotion and viable suggesting that other proteins may partially compensate for Resilin function. Indeed, localizations of the potentially elastic protein Cpr56F and Resilin occasionally coincide.

Conclusions
Thus, Resilin-matrices are composite in the way that varying amounts of different elastic proteins and dityrosinylation define material properties. Understanding the biology of Resilin will have an impact on Resilin-based biomaterial and biomedical sciences.
A Cas9-mediated adenosine transient reporter enables enrichment of ABE-targeted cells
Authors: Brookhouser Nicholas , Nguyen Toan , Tekel Stefan J. , Standage-Beier Kylie , Wang Xiao , Brafman David A. 
Published: December 2020

Abstract
Background
Adenine base editors (ABE) enable single nucleotide modifications without the need for double-stranded DNA breaks (DSBs) induced by conventional CRIPSR/Cas9-based approaches. However, most approaches that employ ABEs require inefficient downstream technologies to identify desired targeted mutations within large populations of manipulated cells. In this study, we developed a fluorescence-based method, named “Cas9-mediated adenosine transient reporter for editing enrichment” (CasMAs-TREE; herein abbreviated XMAS-TREE), to facilitate the real-time identification of base-edited cell populations.

Results
To establish a fluorescent-based assay able to detect ABE activity within a cell in real time, we designed a construct encoding a mCherry fluorescent protein followed by a stop codon (TGA) preceding the coding sequence for a green fluorescent protein (GFP), allowing translational readthrough and expression of GFP after A-to-G conversion of the codon to “TGG.” At several independent loci, we demonstrate that XMAS-TREE can be used for the highly efficient purification of targeted cells. Moreover, we demonstrate that XMAS-TREE can be employed in the context of multiplexed editing strategies to simultaneous modify several genomic loci. In addition, we employ XMAS-TREE to efficiently edit human pluripotent stem cells (hPSCs), a cell type traditionally resistant to genetic modification. Furthermore, we utilize XMAS-TREE to generate clonal isogenic hPSCs at target sites not editable using well-established reporter of transfection (RoT)-based strategies.

Conclusion
We established a method to detect adenosine base-editing activity within a cell, which increases the efficiency of editing at multiple genomic locations through an enrichment of edited cells. In the future, XMAS-TREE will greatly accelerate the application of ABEs in biomedical research.
Feedback inhibition of AMT1 NH4+-transporters mediated by CIPK15 kinase
Authors: Chen Hui-Yu , Chen Yen-Ning , Wang Hung-Yu , Liu Zong-Ta , Frommer Wolf B. , Ho Cheng-Hsun 
Published: December 2020

Abstract
Background
Ammonium (NH4+), a key nitrogen form, becomes toxic when it accumulates to high levels. Ammonium transporters (AMTs) are the key transporters responsible for NH4+ uptake. AMT activity is under allosteric feedback control, mediated by phosphorylation of a threonine in the cytosolic C-terminus (CCT). However, the kinases responsible for the NH4+-triggered phosphorylation remain unknown.

Results
In this study, a functional screen identified protein kinase CBL-Interacting Protein Kinase15 (CIPK15) as a negative regulator of AMT1;1 activity. CIPK15 was able to interact with several AMT1 paralogs at the plasma membrane. Analysis of AmTryoshka, an NH4+ transporter activity sensor for AMT1;3 in yeast, and a two-electrode-voltage-clamp (TEVC) of AMT1;1 in Xenopus oocytes showed that CIPK15 inhibits AMT activity. CIPK15 transcript levels increased when seedlings were exposed to elevated NH4+ levels. Notably, cipk15 knockout mutants showed higher 15NH4+ uptake and accumulated higher amounts of NH4+ compared to the wild-type. Consistently, cipk15 was hypersensitive to both NH4+ and methylammonium but not nitrate (NO3−).

Conclusion
Taken together, our data indicate that feedback inhibition of AMT1 activity is mediated by the protein kinase CIPK15 via phosphorylation of residues in the CCT to reduce NH4+-accumulation.