bluuebunny/crossref_metadata_2025_split huggingface dataset:
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
Authors: Mohammad-Taheri Sara , Zucker Jeremy , Hoyt Charles Tapley , Sachs Karen , Tewari Vartika , Ness Robert , Vitek Olga 
Published: April 2022

AbstractMotivationEstimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways.ResultsIn this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl’s do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation.Availability and implementationThe code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus.Supplementary informationSupplementary data are available at Bioinformatics online.
DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features
Authors: Weng Zhenyu , Yue Zongliang , Zhu Yuesheng , Chen Jake Yue 
Published: April 2022

AbstractSummaryIn biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene’s relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein–protein interaction weights, gene-to-gene correlations and the gene set annotations—four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer’s disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.Supplementary informationSupplementary data are available at Bioinformatics online.
QuCo: quartet-based co-estimation of species trees and gene trees
Authors: Rabiee Maryam , Mirarab Siavash 
Published: April 2022

AbstractMotivationPhylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.ResultsWe introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.Availability and implementationQuCo is available on https://github.com/maryamrabiee/quco.Supplementary informationSupplementary data are available at Bioinformatics online.
A graph neural network approach for molecule carcinogenicity prediction
Authors: Fradkin Philip , Young Adamo , Atanackovic Lazar , Frey Brendan , Lee Leo J , Wang Bo 
Published: April 2022

AbstractMotivationMolecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carcinogenicity information is limited and building data-driven models with good prediction accuracy remains a major challenge.ResultsIn this work, we propose CONCERTO, a deep learning model that uses a graph transformer in conjunction with a molecular fingerprint representation for carcinogenicity prediction from molecular structure. Special efforts have been made to overcome the data size constraint, such as multi-round pre-training on related but lower quality mutagenicity data, and transfer learning from a large self-supervised model. Extensive experiments demonstrate that our model performs well and can generalize to external validation sets. CONCERTO could be useful for guiding future carcinogenicity experiments and provide insight into the molecular basis of carcinogenicity.Availability and implementationThe code and data underlying this article are available on github at https://github.com/bowang-lab/CONCERTO
teff: estimation of Treatment EFFects on transcriptomic data using causal random forest
Authors: Cáceres Alejandro , González Juan R 
Published: April 2022

AbstractMotivationCausal inference on high-dimensional feature data can be used to find a profile of patients who will benefit the most from treatment rather than no treatment. However, there is a need for usable implementations for transcriptomic data. We developed teff that applies random causal forest on gene expression data to target individuals with high expected treatment effects.ResultsWe extracted a profile of high benefit of treating psoriasis with brodalumab and observed that it was associated with higher T cell abundance in non-lesional skin at baseline and a lower response for etanercept in an independent study. Individual patient targeting with causal inference profiling can inform patients on choosing between treatments before the intervention begins.Availability and implementationteff is an R package available at https://teff-package.github.io. The data underlying this article are available in GEO, at https://www.ncbi.nlm.nih.gov/geo/Supplementary informationSupplementary data are available at Bioinformatics online.
Steer’n’Detect: fast 2D template detection with accurate orientation estimation
Authors: Uhlmann Virginie , Püspöki Zsuzsanna , Depeursinge Adrien , Unser Michael , Sage Daniel , Fageot Julien 
Published: April 2022

AbstractMotivationRotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly degrades in noisy image data.ResultsWe introduce Steer’n’Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images. Steer’n’Detect provides a faster and more robust substitute to template matching. By adapting to the statistics of the image background, it guarantees accurate results even in the presence of noise. The plugin comes with an intuitive user interface facilitating results analysis and further post-processing.Availability and implementationhttps://github.com/Biomedical-Imaging-Group/Steer-n-Detect.Supplementary informationSupplementary data are available at Bioinformatics online.
DrugCVar: a platform for evidence-based drug annotation for genetic variants in cancer
Authors: Zhang Xiaolong , Qian Zhikai , Wang Ye , Zhang Qingfeng , Yu Kai , Zheng Yongqiang , Liu Zekun , Zhao Qi , Liu Ze-Xian 
Published: April 2022

AbstractMotivationTargeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI) and My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant–drug targeting evidence from literature and the interpretations from the public resources.ResultsIn total, 7830 clinical evidences for cancer variant–drug targeting were integrated and classified into 10 evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant–drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details, such as the mutation function, location of the variants in gene and protein structures and mutation statistics of queried genes in various tumor types, were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies.Availability and implementationThe database is available at http://drugcvar.omicsbio.info.Supplementary informationSupplementary data are available at Bioinformatics online.
Quantification of transcription noise’s impact on cell fate commitment with digital resolutions
Authors: Jiao Feng , Tang Moxun 
Published: April 2022

AbstractMotivationGene transcription is a random and noisy process. Tremendous efforts in single-cell studies have been mapping transcription noises to phenotypic variabilities between isogenic cells. However, the exact role of the noise in cell fate commitment remains largely descriptive or even controversial.ResultsFor a specified cell fate, we define the jumping digit I of a critical gene as a statistical threshold that a single cell has approximately an equal chance to commit the fate as to have at least I transcripts of the gene. When the transcription is perturbed by a noise enhancer without changing the basal transcription level E0, we find a crossing digit k such that the noise catalyzes cell fate change when I &gt; k while stabilizes the current state when I &lt; k; k remains stable against enormous variations of kinetic rates. We further test the reactivation of latent HIV in 22 integration sites by noise enhancers paired with transcriptional activators. Strong synergistic actions are observed when the activators increase transcription burst frequency, whereas no synergism, but antagonism, is often observed if activators increase burst size. The synergistic efficiency can be predicted accurately by the ratio I/E0. When the noise enhancers double the noise, the activators double the burst frequency, and I/E0≥7, their combination is 10 times more effective than their additive effects across all 22 sites.Availability and implementationThe jumping digit I may provide a novel probe to explore the phenotypic consequences of transcription noise in cell functions. Code is freely available at http://cam.gzhu.edu.cn/info/1014/1223.htm. The data underlying this article are available in the article and in its online supplementary material.Supplementary informationSupplementary data are available at Bioinformatics online.
Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution
Authors: Cai Manqi , Yue Molin , Chen Tianmeng , Liu Jinling , Forno Erick , Lu Xinghua , Billiar Timothy , Celedón Juan , McKennan Chris , Chen Wei , Wang Jiebiao 
Published: April 2022

AbstractMotivationTissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods.ResultsTo achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data.Availability and implementationEnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal.Supplementary informationSupplementary data are available at Bioinformatics online.
Identification of sex chromosomes using genomic and cytogenetic methods in a range-expanding spider,Argiope bruennichi(Araneae: Araneidae)
Authors: Sheffer Monica M , Cordellier Mathilde , Forman Martin , Grewoldt Malte , Hoffmann Katharina , Jensen Corinna , Kotz Matěj , Král Jiří , Kuss Andreas W , Líznarová Eva , Uhl Gabriele 
Published: April 2022

AbstractDifferences between sexes in growth, ecology and behaviour strongly shape species biology. In some animal groups, such as spiders, it is difficult or impossible to identify the sex of juveniles based on external morphology. This information would be useful for field surveys, behavioural experiments and ecological studies, such as those on sex ratios and dispersal. In species with sex chromosomes, sex can be determined based on the specific sex chromosome complement. Additionally, information on the sequence of sex chromosomes provides the basis for studying sex chromosome evolution. We combined cytogenetic and genomic data to identify the sex chromosomes in the sexually dimorphic spider Argiope bruennichi and designed quantitative real-time polymerase chain reaction sex markers. We found that the genome size and GC content of this spider fall into the ranges reported for the majority of araneids. The male karyotype is formed by 24 acrocentric chromosomes with an X1X20 sex chromosome system, with little similarity between X chromosomes, suggesting an origin of these chromosomes by X chromosome fission or early duplication of an X chromosome and subsequent independent differentiation of the copies. Our data suggest X chromosomes of similar sizes in A. bruennichi. They are smaller chromosomes of the complement. Our findings open the door to new directions in spider evolutionary and ecological research.
Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping
Authors: Gao Leiwen , Banerjee Sudipto , Ritz Beate 
Published: April 2022

SummaryRegional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek “difference boundaries” that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.
Ultra-early initiation of postoperative rehabilitation in the post-anaesthesia care unit after major thoracic surgery: case–control study
Authors: Pastene Bruno , Labarriere Ambroise , Lopez Alexandre , Charvet Aude , Culver Aurélien , Fiocchi David , Cluzel Armand , Brioude Geoffrey , Einav Sharon , Tankel James , Hamidou Zeinab , D’Journo Xavier Benoit , Thomas Pascal , Leone Marc , Zieleskiewicz Laurent ,   the GRACE Association, Slim K. , Joris J. , Delaunay L. , Regimbeau J-M. , Ostermann S. , Beyer-Berjot L. , Lavand'homme P. , Lafortune I. , Szymkiewicz O. , Venara A. , Zieleskiewicz L. , Puppo N. , Beaupère S. 
Published: April 2022

Abstract

Background
Physiotherapy is a major cornerstone of enhanced rehabilitation after surgery (ERAS) and reduces the development of atelectasis after thoracic surgery. By initiating physiotherapy in the post-anaesthesia care unit (PACU), the aim was to evaluate whether the ultra-early initiation of rehabilitation (in the first hour following tracheal extubation) would improve the outcomes of patients undergoing elective thoracic surgery.

Methods
A case–control study with a before-and-after design was conducted. From a historical control group, patients were paired at a 3:1 ratio with an intervention group. This group consisted of patients treated with the ultra-early rehabilitation programme after elective thoracic surgery (clear fluids, physiotherapy, and ambulation). The primary outcome was the incidence of postoperative atelectasis and/or pneumonia during the hospital stay.

Results
After pairing, 675 patients were allocated to the historical control group and 225 patients to the intervention group. A significant decrease in the incidence of postoperative atelectasis and/or pneumonia was found in the latter (11.4 versus 6.7 per cent respectively; P = 0.042) and remained significant on multivariate analysis (OR 0.53, 95 per cent c.i. 0.26 to 0.98; P = 0.045). A subgroup analysis of the intervention group showed that early ambulation during the PACU stay was associated with a further significant decrease in the incidence of postoperative atelectasis and/or pneumonia (2.2 versus 9.5 per cent; P = 0.012).

Conclusions
Ultra-early rehabilitation in the PACU was associated with a decrease in the incidence of postoperative atelectasis and/or pneumonia after major elective thoracic surgery.
The Politics of Matching: Ethnicity, Religion and Muslim-heritage Children in Care in the UK
Authors: Cheruvallil-Contractor Sariya , Halford Alison , Boti Phiri Mphatso 
Published: April 2022

Abstract
In 2014, in order to improve outcomes for children from ethnic minority backgrounds and to speed up the adoption process, the UK government changed the Children and Families Act. The legal requirement on adoption agencies to consider ethnicity in the decision around ‘matching’ was removed, thus clearing the way for transracial placements. This article interrogates the impact of the change in law on social work practice around adoption, using the experiences of diverse Muslim-heritage children as a case study. Grounded in the sociology of religion, the findings presented here are based on semi-structured qualitative interviews (n = 28) with those involved in the care of Muslim-heritage children. In discussing qualitative findings, all adopters and prospective adopters interviewed in this research insisted on adopting children who ‘look like them’, and social workers continued to look for the ‘best’ possible matches. Children from minoritised backgrounds continue to wait for long periods before finding permanent homes. Our evidence raises questions about the efficacy of policy guidance. Based on this evidence we conclude that greater strategizing is needed around the recruitment of adopters from diverse backgrounds.
Implementation of the Making Safeguarding Personal Approach to Strengths-based Adult Social Care: Systematic Review of Qualitative Research Evidence
Authors: Ahuja Latika , Price Anna , Bramwell Charlotte , Briscoe Simon , Shaw Liz , Nunns Michael , O’Rourke Gareth , Baron Samantha , Anderson Rob 
Published: April 2022

Abstract
Since the Care Act (2014), there is fresh impetus for social workers to apply strengths-based approaches (SBAs) when working with adults. However, implementation challenges remain. This article presents our synthesis of seven studies that examined Making Safeguarding Personal (MSP). It was conducted as part of a systematic review that aimed to summarise research evidence on the implementation of different SBAs within adult social work in the UK. Qualitative studies were analysed using a framework synthesis approach. Four themes were identified: (1) MSP as an intervention: seen as initially demanding but with long-term advantages; (2) Culture and settings: required broad cultural changes; ‘outward facing’ and smaller/specialist councils tended to find this easier; (3) Individual characteristics: related to enhancing the knowledge, skills and confidence of practitioner and stakeholders in MSP; and service user willingness to engage; and (4) Embedding and sustaining MSP: depended on strong leadership and active engagement at all levels. We found a wide range of factors affecting the implementation of MSP. These may have broader relevance for other strengths-based models of social work practice.