bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interpretation of deep learning in genomics and epigenomics
Authors: Talukder Amlan , Barham Clayton , Li Xiaoman , Hu Haiyan 
Published: July 2020

AbstractMachine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu
Deep-belief network for predicting potential miRNA-disease associations
Authors: Chen Xing , Li Tian-Hao , Zhao Yan , Wang Chun-Chun , Zhu Chi-Chi 
Published: July 2020

AbstractMicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.
Large-scale entity representation learning for biomedical relationship extraction
Authors: Sänger Mario , Leser Ulf 
Published: July 2020

AbstractMotivationThe automatic extraction of published relationships between molecular entities has important applications in many biomedical fields, ranging from Systems Biology to Personalized Medicine. Existing works focused on extracting relationships described in single articles or in single sentences. However, a single record is rarely sufficient to judge upon the biological correctness of a relation, as experimental evidence might be weak or only valid in a certain context. Furthermore, statements may be more speculative than confirmative, and different articles often contradict each other. Experts therefore always take the complete literature into account to take a reliable decision upon a relationship. It is an open research question how to do this effectively in an automatic manner.ResultsWe propose two novel relation extraction approaches which use recent representation learning techniques to create comprehensive models of biomedical entities or entity-pairs, respectively. These representations are learned by considering all publications from PubMed mentioning an entity or a pair. They are used as input for a neural network for classifying relations globally, i.e. the derived predictions are corpus-based, not sentence- or article based as in prior art. Experiments on the extraction of mutation–disease, drug–disease and drug–drug relationships show that the learned embeddings indeed capture semantic information of the entities under study and outperform traditional methods by 4–29% regarding F1 score.Availability and implementationSource codes are available at: https://github.com/mariosaenger/bio-re-with-entity-embeddings.Supplementary informationSupplementary data are available at Bioinformatics online.
HATK: HLA analysis toolkit
Authors: Choi Wanson , Luo Yang , Raychaudhuri Soumya , Han Buhm 
Published: July 2020

Abstract

Summary
Fine-mapping human leukocyte antigen (HLA) genes involved in disease susceptibility to individual alleles or amino acid residues has been challenging. Using information regarding HLA alleles obtained from HLA typing, HLA imputation or HLA inference, our software expands the alleles to amino acid sequences using the most recent IMGT/HLA database and prepares a dataset suitable for fine-mapping analysis. Our software also provides useful functionalities, such as various association tests, visualization tools and nomenclature conversion.

Availability and implementation
https://github.com/WansonChoi/HATK.
The microRNA-200 cluster on chromosome 23 is required for oocyte maturation and ovulation in zebrafish†
Authors: Xiong Shuting , Tian Jinsong , Ge Si , Li Zhi , Long Zhe , Guo Wenjie , Huang Peipei , He Yan , Xiao Tiaoyi , Gui Jian-Fang , Mei Jie 
Published: July 2020

AbstractThe reproductive process is usually controlled by the hypothalamic-pituitary-gonad axis in vertebrates, while Kiss/gonadotropin-releasing hormone (GnRH) system in the hypothalamus is required for mammalian reproduction but dispensable for fish reproduction. The regulation of follicle stimulating hormone/luteinizing hormone (LH) expression in fish species is still unknown. Here, we identified miR-200s on chromosome 23 (chr23-miR-200s) as important regulators for female zebrafish reproduction. Knockout of chr23-miR-200s (chr23-miR-200s-KO) resulted in dysregulated expression of luteinizing hormone beta lhb (luteinizing hormone beta) and some hormone genes in the pituitary as revealed by comparative transcriptome profiling, leading to failure of oocyte maturation and ovulation as well as defects in reproductive duct development. Chr23-miR-200s mainly expressed in the pituitary and regulated lhb expression by targeting the transcription repressor wt1a. Injection of human chorionic gonadotropin (hCG) could rescue the defects of oocyte maturation in chr23-miR-200s-KO zebrafish, whereas GnRH or LHRH-A2 could not, suggesting that Chr23-miR-200s regulated lhb expression in a GnRH-independent pathway. It was remarkable that either injection of carp pituitary extraction, or co-injection of hCG with synthetic oxytocin and vasotocin could greatly rescue the defects of both oocyte maturation and ovulation in chr23-miR-200s-KO zebrafish. Altogether, our findings highlight an important function of chr23-miR-200s in controlling oocyte maturation by regulation LH expression, and oxytocin and vasotocin are potentially responsible for the ovulation in fish species.
Statistical properties of sketching algorithms
Authors: Ahfock D C , Astle W J , Richardson S 
Published: July 2020

SummarySketching is a probabilistic data compression technique that has been largely developed by the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original dataset, and this stochastic generation process makes them amenable to statistical analysis. We argue that the sketched data can be modelled as a random sample, thus placing this family of data compression methods firmly within an inferential framework. In particular, we focus on the Gaussian, Hadamard and Clarkson–Woodruff sketches and their use in single-pass sketching algorithms for linear regression with huge samples. We explore the statistical properties of sketched regression algorithms and derive new distributional results for a large class of sketching estimators. A key result is a conditional central limit theorem for data-oblivious sketches. An important finding is that the best choice of sketching algorithm in terms of mean squared error is related to the signal-to-noise ratio in the source dataset. Finally, we demonstrate the theory and the limits of its applicability on two datasets.
Adaptive treatment strategies for chronic conditions: shared-parameter G-estimation with an application to rheumatoid arthritis
Authors: Wang Shouao , Moodie Erica Em , Stephens David A , Nijjar Jagtar S 
Published: July 2020

Summary
Most estimation algorithms for adaptive treatment strategies assume that treatment rules at each decision point are independent from one another in the sense that they do not possess any common parameters. This is often unrealistic, as the same decisions may be made repeatedly over time. Sharing treatment-decision parameters across decision points offers several advantages, including estimation of fewer parameters and the clinical ease of a single, time-invariant decision to implement. We propose a new computational approach to estimation of shared-parameter G-estimation, which is efficient and shares the double robustness of the “unshared” sequential G-estimation. We use this approach to analyze data from the Scottish Early Rheumatoid Arthritis (SERA) Inception Cohort.
Working memory training increases neural efficiency in Parkinson’s disease: a randomized controlled trial
Authors: Giehl Kathrin , Ophey Anja , Hammes Jochen , Rehberg Sarah , Lichtenstein Thorsten , Reker Paul , Eggers Carsten , Kalbe Elke , van Eimeren Thilo 
Published: July 2020

Abstract
Impairment of working memory and executive functions is already frequently observed in early stages of Parkinson’s disease. Improvements in working memory performance in this cohort could potentially be achieved via working memory training. However, the specific neural mechanisms underlying different working memory processes such as maintenance as opposed to manipulation are largely under-investigated in Parkinson’s disease. Moreover, the plasticity of these correlates as a function of working memory training is currently unknown in this population. Thus, the working memory subprocesses of maintenance and manipulation were assessed in 41 cognitively healthy patients with Parkinson’s disease using a newly developed working memory paradigm and functional MRI. Nineteen patients were randomized to a 5-week home-based digital working memory training intervention while the remaining patients entered a control, wait list condition. Working memory task-related activation patterns and context-dependent functional connectivity, as well as the change of these neural correlates as a function of training, were assessed. While both working memory processes activated an extended frontoparietal–cerebellar network, only the manipulation of items within working memory also recruited the anterior striatum. The intervention effect on the neural correlates was small, but decreased activation in areas relevant for working memory could be observed, with activation changes correlating with behavioural change. Moreover, training seemed to result in decreased functional connectivity when pure maintenance was required, and in a reorganization of functional connectivity when items had to be manipulated. In accordance with the neural efficacy hypothesis, training resulted in overall reduced activation and reorganized functional connectivity, with a differential effect on the different working memory processes under investigation. Now, larger trials including follow-up examinations are needed to further explore the long-term effects of such interventions on a neural level and to estimate the clinical relevance to potentially delay cognitive decline in cognitively healthy patients with Parkinson’s disease.
Platelet-rich plasma accelerates skin wound healing by promoting re-epithelialization
Authors: Xu Pengcheng , Wu Yaguang , Zhou Lina , Yang Zengjun , Zhang Xiaorong , Hu Xiaohong , Yang Jiacai , Wang Mingying , Wang Binjie , Luo Gaoxing , He Weifeng , Cheng Biao 
Published: July 2020

Abstract

Background
Autologous platelet-rich plasma (PRP) has been suggested to be effective for wound healing. However, evidence for its use in patients with acute and chronic wounds remains insufficient. The aims of this study were to comprehensively examine the effectiveness, synergy and possible mechanism of PRP-mediated improvement of acute skin wound repair.

Methods
Full-thickness wounds were made on the back of C57/BL6 mice. PRP or saline solution as a control was administered to the wound area. Wound healing rate, local inflammation, angiogenesis, re-epithelialization and collagen deposition were measured at days 3, 5, 7 and 14 after skin injury. The biological character of epidermal stem cells (ESCs), which reflect the potential for re-epithelialization, was further evaluated in vitro and in vivo.

Results
PRP strongly improved skin wound healing, which was associated with regulation of local inflammation, enhancement of angiogenesis and re-epithelialization. PRP treatment significantly reduced the production of inflammatory cytokines interleukin-17A and interleukin-1β. An increase in the local vessel intensity and enhancement of re-epithelialization were also observed in animals with PRP administration and were associated with enhanced secretion of growth factors such as vascular endothelial growth factor and insulin-like growth factor-1. Moreover, PRP treatment ameliorated the survival and activated the migration and proliferation of primary cultured ESCs, and these effects were accompanied by the differentiation of ESCs into adult cells following the changes of CD49f and keratin 10 and keratin 14.

Conclusion
PRP improved skin wound healing by modulating inflammation and increasing angiogenesis and re-epithelialization. However, the underlying regulatory mechanism needs to be investigated in the future. Our data provide a preliminary theoretical foundation for the clinical administration of PRP in wound healing and skin regeneration.
Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network
Authors: Rezaeipanah Amin , Ahmadi Gholamreza 
Published: July 2020

Abstract
Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.
Covariance and correlation measures on a graph in a generalized bag-of-paths formalism
Authors: Guex Guillaume , Courtain Sylvain , Saerens Marco 
Published: July 2020

Abstract
This work derives closed-form expressions computing the expectation of co-presence and of number of co-occurrences of nodes on paths sampled from a network according to general path weights (a bag of paths). The underlying idea is that two nodes are considered as similar when they often appear together on (preferably short) paths of the network. The different expressions are obtained for both regular and hitting paths and serve as a basis for computing new covariance and correlation measures between nodes, which are valid positive semi-definite kernels on a graph. Experiments on semi-supervised classification problems show that the introduced similarity measures provide competitive results compared to other state-of-the-art distance and similarity measures between nodes.
Habitat complexity influences selection of thermal environment in a common coral reef fish
Authors: Nay Tiffany J , Johansen Jacob L , Rummer Jodie L , Steffensen John F , Pratchett Morgan S , Hoey Andrew S 
Published: July 2020

AbstractCoral reef species, like most tropical species, are sensitive to increasing environmental temperatures, with many species already living close to their thermal maxima. Ocean warming and the increasing frequency and intensity of marine heatwaves are challenging the persistence of reef-associated species through both direct physiological effects of elevated water temperatures and the degradation and loss of habitat structure following disturbance. Understanding the relative importance of habitat degradation and ocean warming in shaping species distributions is critical in predicting the likely biological effects of global warming. Using an automated shuttle box system, we investigated how habitat complexity influences the selection of thermal environments for a common coral reef damselfish, Chromis atripectoralis. In the absence of any habitat (i.e. control), C. atripectoralis avoided temperatures below 22.9 ± 0.8°C and above 31.9 ± 0.6°C, with a preferred temperature (Tpref) of 28.1 ± 0.9°C. When complex habitat was available, individual C. atripectoralis occupied temperatures down to 4.3°C lower (mean ± SE; threshold: 18.6 ± 0.7°C; Tpref: 18.9 ± 1.0°C) than control fish. Conversely, C. atripectoralis in complex habitats occupied similar upper temperatures as control fish (threshold: 31.7 ± 0.4°C; preference: 28.3 ± 0.7°C). Our results show that the availability of complex habitat can influence the selection of thermal environment by a coral reef fish, but only at temperatures below their thermal preference. The limited scope of C. atripectoralis to occupy warmer environments, even when associated with complex habitat, suggests that habitat restoration efforts in areas that continue to warm may not be effective in retaining populations of C. atripectoralis and similar species. This species may have to move to cooler (e.g. deeper or higher latitude) habitats under predicted future warming. The integration of habitat quality and thermal environment into conservation efforts will be essential to conserve of coral reef fish populations under future ocean warming scenarios.
Heart failure with preserved ejection fraction: insights into diagnosis and pathophysiology
Authors: Nagueh Sherif F 
Published: July 2020

Abstract
Heart failure with preserved ejection fraction (HFpEF) accounts for at least half the cases of heart failure, currently diagnosed. There are several cardiac and non-cardiac manifestations of the syndrome. Structure and function abnormalities can include all four cardiac chambers. The left ventricle has abnormal systolic and diastolic functions which can be examined by invasive and non-invasive measurements. In addition, the left atrium enlarges with abnormal left atrial function, pulmonary hypertension occurs, and the right ventricle can develop hypertrophy, enlargement, and systolic dysfunction. There are a paucity of data on calcium handling in HFpEF patients. Growing literature supports the presence of abnormalities in titin and its phosphorylation, and increased interstitial fibrosis contributing to increased chamber stiffness. A systemic inflammatory state causing reduced myocardial cyclic guanosine monophosphate along with defects in the unfolded protein response have been recently reported. Diagnosis relies on signs and symptoms of heart failure, preserved ejection fraction, and detection of diastolic function abnormalities based on echocardiographic findings and abnormally elevated natriuretic peptide levels or invasive measurements of wedge pressure at rest or with exercise. There are currently two diagnostic algorithms: H2FPEF, and HFA-PEFF with limited data comparing their performance head to head in the same patient population. Despite the growing understanding of the syndrome’s pathophysiology, there have been little success in developing specific treatment for patients with HFpEF.
Sex differences in the association between myocardial function and prognosis in type 1 diabetes without known heart disease: the Thousand & 1 Study
Authors: Lassen Mats Christian Højbjerg , Biering-Sørensen Tor , Jørgensen Peter Godsk , Andersen Henrik Ullits , Rossing Peter , Jensen Magnus T. 
Published: July 2020

Abstract

Aims
In type 1 diabetes mellitus (T1DM), recent findings suggest that women have a greater excess risk of cardiovascular diseases (CVDs) compared to men. Impaired diastolic function is a common feature in T1DM. We investigated the association between myocardial function by echocardiography and outcomes in T1DM males and females without known heart disease.

Methods and results
A prospective cohort of individuals with T1DM without known heart disease from the outpatient clinic of Steno Diabetes Center Copenhagen. Follow-up was performed through Danish national registers. Outcomes, major adverse cardiovascular events (MACE) and all-cause mortality, were investigated. A total of 1079 participants (mean age: 49.6 ± 14.5 years, 52.6% male, mean duration of diabetes 25.8 ± 14.6 years) were included in the study. During follow-up (median 6.3 years, interquartile range 5.7–6.9), 142 (13.2%) experienced MACE and 63 (5.8%) died. Gender modified the relationship between E/e′ and both MACE and all-cause mortality (P = 0.016 and 0.007, respectively). In females, after multivariable adjustment, both E/e′ and global longitudinal strain (GLS) were significantly associated with MACE [E/e′: hazard ratio (HR) 1.15 confidence interval (CI) 95%: 1.07–1.24, per 1unit increase; and GLS: HR 1.19 CI 95%: 1.04–1.35, per 1% decrease] and with all-cause mortality (E/e′: HR 1.26 CI 95%: 1.11–1.44; and GLS: HR 1.27 CI 95%: 1.03–1.56). In males, the association between E/e′ and GLS and outcomes did not reach statistical significance.

Conclusion
In female individuals with T1DM both E/e′ and GLS provided independent prognostic information, whereas the associations were not significant in males. These results suggest that T1DM affects myocardial function differently in males and females, which may be related to the observed sex difference in CVD risk in T1DM.