bluuebunny/crossref_metadata_2025_split huggingface dataset:
circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier
Authors: Li Hui , Deng Zhaohong , Yang Haitao , Pan Xiaoyong , Wei Zhisheng , Shen Hong-Bin , Choi Kup-Sze , Wang Lei , Wang Shitong , Wu Jing 
Published: September 2021

Abstract
Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views’ raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views’ common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.
Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer
Authors: Li Xin , Pan Xu , Zhou Hanxiao , Wang Peng , Gao Yue , Shang Shipeng , Guo Shuang , Sun Jie , Xiong Zhiying , Ning Shangwei , Zhi Hui , Li Xia 
Published: September 2021

Abstract
Long non-coding RNAs (lncRNAs) that emanate from enhancer regions (defined as enhancer-associated lncRNAs, or elncRNAs) are emerging as critical regulators in disease progression. However, their biological characteristics and clinical relevance have not been fully portrayed. Here, based on the traditional expression quantitative loci (eQTL) and our optimized residual eQTL method, we comprehensively described the genetic effect on elncRNA expression in more than 300 lymphoblastoid cell lines. Meanwhile, a chromatin atlas of elncRNAs relative to the genetic regulation state was depicted. By applying the maximum likelihood estimate method, we successfully identified causal elncRNAs for protein-coding gene expression reprogramming and showed their associated single nucleotide polymorphisms (SNPs) favor binding of transcription factors. Further epigenome analysis revealed two immune-associated elncRNAs AL662844.4 and LINC01215 possess high levels of H3K27ac and H3K4me1 in human cancer. Besides, pan-cancer analysis of 3D genome, transcriptome, and regulatome data showed they potentially regulate tumor-immune cell interaction through affecting MHC class I genes and CD47, respectively. Moreover, our study showed there exist associations between elncRNA and patient survival. Finally, we made a user-friendly web interface available for exploring the regulatory relationship of SNP-elncRNA-protein-coding gene triplets (http://bio-bigdata.hrbmu.edu.cn/elncVarReg). Our study provides critical mechanistic insights for elncRNA function and illustrates their implications in human cancer.
An overview of machine learning methods for monotherapy drug response prediction
Authors: Firoozbakht Farzaneh , Yousefi Behnam , Schwikowski Benno 
Published: September 2021

Abstract
For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.
High dimensionality reduction by matrix factorization for systems pharmacology
Authors: Mehrpooya Adel , Saberi-Movahed Farid , Azizizadeh Najmeh , Rezaei-Ravari Mohammad , Saberi-Movahed Farshad , Eftekhari Mahdi , Tavassoly Iman 
Published: September 2021

AbstractThe extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.
Socket2: a program for locating, visualizing and analyzing coiled-coil interfaces in protein structures
Authors: Kumar Prasun , Woolfson Derek N 
Published: September 2021

Abstract

Motivation
Protein–protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualize and analyze CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterizes CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and nonexpert users alike.

Results
The original Socket only classified CCs with up to six helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating nonnatural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualizing CCs, and tabs for viewing the sequence repeats, helix-packing angles and core-packing geometries of CCs identified and calculated by Socket2.

Availability and implementation
Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT licence and available to download under the Downloads tab of the Socket2 home page.
MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks
Authors: Fu Haitao , Huang Feng , Liu Xuan , Qiu Yang , Zhang Wen 
Published: September 2021

AbstractMotivationThere are various interaction/association bipartite networks in biomolecular systems. Identifying unobserved links in biomedical bipartite networks helps to understand the underlying molecular mechanisms of human complex diseases and thus benefits the diagnosis and treatment of diseases. Although a great number of computational methods have been proposed to predict links in biomedical bipartite networks, most of them heavily depend on features and structures involving the bioentities in one specific bipartite network, which limits the generalization capacity of applying the models to other bipartite networks. Meanwhile, bioentities usually have multiple features, and how to leverage them has also been challenging.ResultsIn this study, we propose a novel multi-view graph convolution network (MVGCN) framework for link prediction in biomedical bipartite networks. We first construct a multi-view heterogeneous network (MVHN) by combining the similarity networks with the biomedical bipartite network, and then perform a self-supervised learning strategy on the bipartite network to obtain node attributes as initial embeddings. Further, a neighborhood information aggregation (NIA) layer is designed for iteratively updating the embeddings of nodes by aggregating information from inter- and intra-domain neighbors in every view of the MVHN. Next, we combine embeddings of multiple NIA layers in each view, and integrate multiple views to obtain the final node embeddings, which are then fed into a discriminator to predict the existence of links. Extensive experiments show MVGCN performs better than or on par with baseline methods and has the generalization capacity on six benchmark datasets involving three typical tasks.Availability and implementationSource code and data can be downloaded from https://github.com/fuhaitao95/MVGCN.Supplementary informationSupplementary data are available at Bioinformatics online.
Quantifying uncertainty in spikes estimated from calcium imaging data
Authors: Chen Yiqun T , Jewell Sean W , Witten Daniela M 
Published: September 2021

Summary
In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike—i.e., that there was no increase in calcium—at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample $p$-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the $\texttt{spikefinder}$ challenge.
Building Public Confidence in Parole Boards: Findings From a Four-Country Study
Authors: Fitzgerald Robin , Freiberg Arie , Dodd Shannon , Bartels Lorana 
Published: September 2021

Abstract
Parole and parole boards play critical roles in criminal justice systems. With parolee numbers and imprisonment rates increasing in many countries, parole decision-making is a crucial contributor to prison population sizes and, more broadly, public confidence in the operation of correctional systems. This article examines the public understanding of and confidence in parole, from the perspectives of parole board members and other parole authority staff. It aims to determine whether and, if so, how, public opinion influences parole decision-making and how parole boards feel they can or should respond to this. It draws on interviews with 80 parole board members and other relevant staff in Australia, New Zealand, Canada and Scotland.
St Gallen 2019 guidelines understage the axilla in lobular breast cancer: a population-based study
Authors: Narbe U , Bendahl P-O , Fernö M , Ingvar C , Dihge L , Rydén L 
Published: September 2021

Abstract

Background
The St Gallen 2019 guidelines for primary therapy of early breast cancer recommend omission of completion axillary lymph node dissection (cALND), regardless of histological type, in patients with one or two sentinel lymph node (SLN) metastases. Concurrently, adjuvant chemotherapy is endorsed for luminal A-like disease with four or more axillary lymph node (ALN) metastases. The aim of this study was to estimate the proportion of patients with invasive lobular cancer (ILC) versus invasive ductal cancer of no special type (NST) with one or two SLN metastases for whom cALND would have led to a recommendation for adjuvant chemotherapy.

Methods
Patients with ILC and NST who had surgery between 2014 and 2017 were identified in the National Breast Cancer Register of Sweden. After exclusion of patients with incongruent or missing data, those who fulfilled the St Gallen 2019 criteria for cALND omission were included in the population-based study cohort.

Results
Some 1886 patients in total were included in the study, 329 with ILC and 1507 with NST. Patients with ILC had a higher metastatic nodal burden and were more likely to have a luminal A-like subtype than those with NST. The prevalence of at least four ALN metastases was higher in ILC (31.0 per cent) than NST (14.9 per cent), corresponding to an adjusted odds ratio of 2.26 (95 per cent c.i. 1.59 to 3.21). Luminal A-like breast cancers with four or more ALN metastases were over-represented in ILC compared with NST, 52 of 281 (18.5 per cent) versus 43 of 1299 (3.3 per cent) (P &lt; 0.001).

Conclusion
Patients with ILC more often have luminal A-like breast cancer with at least four nodal metastases. Omission of cALND in patients with luminal A-like invasive lobular cancer and one or two SLN metastases warrants future attention as there is a risk of nodal understaging and undertreatment in one-fifth of patients.
Effect of radiotherapy on expanders and permanent implants in immediate breast reconstruction: long-term surgical and patient-reported outcomes in a large multicentre cohort
Authors: Coudé Adam Hannah , Frisell Axel , Liu Yihang , Sackey Helena , Oikonomou Ira , Docherty Skogh Ann-Charlot , Frisell Jan , de Boniface Jana 
Published: September 2021

Abstract

Background
Current evidence for the effects of radiotherapy (RT) on implant-based immediate breast reconstruction (IBR) is limited by short follow-up and lack of patient-reported outcomes (PROs). It is central to integrate long-term comprehensive outcome data into the preoperative decision-making process. The aim of the present study was to determine long-term surgical outcomes and PROs in relation to RT after implant-based IBR.

Methods
This was a longitudinal cohort study of PRO data obtained in surveys conducted in 2012 and 2020 using the BREAST-Q questionnaire. All women undergoing therapeutic mastectomy and implant-based IBR between 1 January 2007 and 31 December 2011 at four breast centres in Stockholm, Sweden, were identified. The endpoint was implant removal owing to surgical complications or patient preference.

Results
Median follow-up was 120 (range 1–171) months. After 754 IBRs in 729 women, implant removal occurred in 128 (17 per cent): 34 of 386 (8.8 per cent) in the no-RT group, 20 of 64 (31.3 per cent) in the group with previous RT, and 74 of 304 (24.3 per cent) in the postoperative RT group (P &lt; 0.001). Implant removal was because of surgical complications in 60 instances (7.9 per cent), and patient preference in 68 (9.0 per cent). The BREAST-Q response rate was 72.2 per cent. Women with previous RT scored lower than those without RT on all scales, apart from psychosocial well-being. Women with postoperative RT scored lower only on physical well-being. No scores in the two RT groups had deteriorated between the survey time points, whereas satisfaction with breasts and overall outcome had decreased in the no-RT group.

Conclusion
Although RT was significantly associated with higher implant removal rates, PROs remained stable over 8 years despite irradiation.
Nationwide registry study on trends in localization techniques and reoperation rates in non-palpable ductal carcinoma in situ and invasive breast cancer
Authors: Schermers Bram , van Riet Yvonne E , Schipper R J , Vrancken Peeters Marie-Jeanne , Voogd Adri C , Nieuwenhuijzen Grard A P , ten Haken Bennie , Ruers Theo J M 
Published: September 2021

Abstract

Background
There is a transition from wire-guided localization (WGL) of non-palpable breast cancer to other localization techniques. Multiple prospective studies have sought to establish superior clinical outcomes for radioactive-seed localization (RSL), but consistent and congruent evidence is missing.

Methods
In this study, female patients with breast cancer operated with breast-conserving surgery after tumour localization of a non-palpable breast cancer or ductal carcinoma in situ (DCIS) were included. The cohort was identified from the nationwide Netherlands Breast Cancer Audit conducted between 2013 and 2018. Trends in localization techniques were analysed. Univariable and multivariable analyses were performed to assess the association between the localization technique and the probability of a reoperation.

Results
A total of 28 370 patients were included in the study cohort. The use of RSL increased from 15.7 to 61.1 per cent during the study years, while WGL decreased from 75.4 to 31.6 per cent. The localization technique used (RSL versus WGL) was not significantly associated with the odds of a reoperation, regardless of whether the lesion was DCIS (odds ratio 0.96 (95 per cent c.i. 0.89 to 1.03; P = 0.281)) or invasive breast cancer (OR 1.02 (95 per cent c.i. 0.96 to 1.10; P = 0.518)).

Conclusion
RSL is rapidly replacing WGL as the preoperative localization technique in breast surgery. This large nationwide registry study found no association between the type of localization technique and the odds of having a reoperation, thus confirming the results of previous prospective cohort studies.
Repeat hepatic resection versus radiofrequency ablation for recurrent hepatocellular carcinoma: retrospective multicentre study
Authors: Zhong J -H , Xing B -C , Zhang W -G , Chan A W -H , Chong C C N , Serenari M , Peng N , Huang T , Lu S -D , Liang Z -Y , Huo R -R , Wang Y -Y , Cescon M , Liu T -Q , Li L , Wu F -X , Ma L , Ravaioli M , Neri J , Cucchetti A , Johnson P J , Li L -Q , Xiang B -D 
Published: September 2021

Abstract

Background
The therapeutic value of repeat hepatic resection (rHR) or radiofrequency ablation (RFA) for recurrent hepatocellular carcinoma (HCC) is unknown. This study aimed to investigate the safety and efficacy of rHR or RFA.

Methods
This was a retrospective multicentre study of patients with recurrent HCC within the Milan criteria who underwent rHR or RFA at nine university hospitals in China and Italy between January 2003 and January 2018. Survival after rHR or RFA was examined in unadjusted analyses and after propensity score matching (1 : 1).

Results
Of 847 patients included, 307 and 540 underwent rHR and RFA respectively. Median overall survival was 73.5 and 67.0 months after rHR and RFA respectively (hazard ratio 1.01 (95 per cent c.i. 0.81 to 1.26)). Median recurrence-free survival was longer after rHR versus RFA (23.6 versus 15.2 months; hazard ratio 0.76 (95 per cent c.i. 0.65 to 0.89)). These results were confirmed after propensity score matching. RFA was associated with lower morbidity of grade 3 and above (0.6 versus 6.2 per cent; P &lt; 0.001) and shorter hospital stay (8.0 versus 3.0 days, P &lt; 0.001) than rHR.

Conclusion
rHR was associated with longer recurrence-free survival but not overall survival compared with RFA.
Cerebral microbleeds following thoracic endovascular aortic repair
Authors: Eilenberg Wolf , Bechstein Matthias , Charbonneau Philippe , Rohlffs Fiona , Eleshra Ahmed , Panuccio Giuseppe , Bhangu Jagdeep Singh , Fiehler Jens , Greenhalgh Rodger M , Haulon Stephan , Kölbel Tilo 
Published: September 2021

Abstract

Background
Stroke and neurological injury are a complication of thoracic endovascular aortic repair (TEVAR). Cerebral microbleeds (CMBs) are common in patients with white matter damage to the brain secondary to chronic vasculopathy. The aim of this study was to examine the occurrence of CMBs after TEVAR, and to evaluate their association with patient and procedural factors.

Methods
Patients who underwent TEVAR between September 2018 and January 2020 in two specialist European aortic centres were analysed. All patients underwent postoperative susceptibility-weighted MRI. The location and number of CMBs were identified, and analysed with regard to procedural aspects, clinical outcome, and Fazekas score as an indicator of pre-existing vascular leucoencephalopathy.

Results
Some 91 patients were included in the study. A total of 1531 CMBs were detected in 58 of 91 patients (64 per cent). In the majority of affected patients, CMBs were found bilaterally (79 per cent). Unilateral CMBs in the right or left hemisphere occurred in 16 and 5 per cent of patients respectively (P &lt; 0.001). More CMBs were found in the middle cerebral than in the vertebrobasilar/posterior and anterior cerebral artery territories (mean(s.d.) 3.35(5.56) versus 2.26(4.05) versus 0.97(2.87); P = 0.045). Multivariable analysis showed an increased probability of CMBs after placement of TEVAR stent-grafts with a proximal diameter of at least 40 mm (odds ratio (OR) 6.85, 95 per cent c.i. 1.65 to 41.59; P = 0.007) and in patients with a higher Fazekas score on postoperative T2-weighted MRI (OR 2.62, 1.06 to 7.92; P = 0.037).

Conclusion
CMBs on postoperative MRI are common after endovascular repair in the aortic arch. Their occurrence appears to be associated with key aspects of the procedure and pre-existing vascular leucoencephalopathy.