检索结果-内蒙古大学图书馆

Quantile regression on inactivity time

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Balmert, Lauren C. Li, Ruosha Peng, Limin Jeong, Jong-Hyeon Feinberg School of Medicine Northwestern University Chicago United States Department of Biostatistics and Data Science University of Texas Health Science Center Huston United States Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Atlanta United States Department of Biostatistics Graduate School of Public Health University of Pittsburgh Pittsburgh United States

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical tests

Orientation of Fitch graphs and detection of horizontal gene transfer in gene trees

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Horizontal gene transfer events partition a gene tree T and thus, its leaf set into subsets of genes whose evolutionary history is described by speciation and duplication events alone. Indirect phylogenetic methods can be used to infer such partitions p from sequence similarity or evolutionary distances without any a priory knowledge about the underlying tree T. In this contribution, we assume that such a partition p of a set of genes X is given and that, independently, an estimate T of the original gene tree on X has been derived. We then ask to what extent T and the xenology information, i.e., p can be combined to determine the horizontal transfer edges in T. We show that for each pair of genes x and y with x,y being in different parts of p, it can be decided whether there always exists or never exists a horizontal gene transfer in T along the path connecting y and the most recent common ancestor of x and y. This problem is equivalent to determining the presence or absence of the directed edge (x,y) in so-called Fitch graphs;a more fine-grained version of graphs that represent the dependencies between the sets in p. We then consider the generalization to insufficiently resolved gene trees and show that analogous results can be obtained. We show that the classification of (x,y) can be computed in constant time after linear-time preprocessing. Using simulated gene family histories, we observe empirically that the vast majority of horizontal transfer edges in the gene tree T can be recovered unambiguously. © 2021, CC BY-NC-SA.

关键词： Clustering algorithms

Inhibition of colorectal cancer targets IL-6, CTLA-4, & B7-2 by Tislelizumab: molecular docking, dynamics, & STRING protein-protein network analysis

学校读者我要写书评

暂无评论

Informatics in Medicine Unlocked 2023年 41卷

作者： Elkazzaz, Mahmoud Ullah, Shahid Gao, Tianshun Shamkh, Israa M. Ahmed, Amr Wu, Ted X. Elsharayidi, Maii S. Khan, Mohammad Shahbaz Rehman, Aziz-ur Lotfy, Marwa M. Haikal, Abdullah Abdalrahman, Mahmoud Department of Chemistry and Biochemistry Faculty of Science Damietta University Damietta 7952567 Egypt S Khan Lab Mardan Khyber Pakhtunkhwa Pakistan Big Data Center The Seventh Affiliated Hospital of Sun Yat-sen University Shenzhen 518107 China Botany and Microbiology Department Faculty of Science Cairo University Cairo 12613 Egypt Chemo and Bioinformatics Lab Bio Search Research Institution BSRI Giza 12613 Egypt Public Health Department First Health Cluster Ministry of Health Riyadh 966-11 Saudi Arabia Science Department Milton Academy Milton 02186 MA United States Central Public Health Laboratories Egyptian Ministry of Health Egypt Children's National Hospital Washington DC United States Keystone Pharmacogenomics LLC Bensalem 19020 PA United States Faculty of Pharmacy Zagazig University Zagazig 44511 Egypt Department of Pharmacognosy Faculty of Pharmacy Mansoura University Mansoura 35516 Egypt Lowell Institute CPS Northeastern University 360 Huntington Ave Boston 02115 MA United States

As a DNA mismatch repair deficit (dMMR) anticancer medication, and a humanized monoclonal antibody (hmAb), Tislelizumab has attracted significant interest in recent years. The programmed cell death protein-1 (PD-1) and its ligands (PD-L1/CD274/B7-H1 and PDL2/CD273) may bind to each other with great affinity. This binding prevents activated T-cells from continuing to proliferate, produce cytokines, and engage in cytotoxic action. The B7:PD-1 inhibitory immunological checkpoint receptor binds to the B7-2(CD86) B-lymphocyte activation antigen. This paper presents Tislelizumab's molecular docking and dynamics simulations with the receptor B7-2(CD86) and its downstream signaling proteins CTLA-4 and IL-6. When docked to B cell stimulatory factor-2 soluble IL-6 receptor alpha (sIL6Ra/CD126), Tislelizumab had a strong binding affinity of 329 kcal/mol and provided an excellent quality model with an LGscore of 4.54. The type I membrane protein B7-2(CD86), a member of the immunoglobulin superfamily, responded to docking with an affinity of −332.35 kcal/mol and LGscore of 2.54. Docking to the immune checkpoint protein receptor cytotoxic T-lymphocyte-associated protein-4 (CTLA-4/CD152) had a binding affinity of −305.91 kcal/mol, facilitating immunological control and cellular dissociation. Tislelizumab and these target proteins exhibited persistent and advantageous interactions in molecular dynamics studies. The STRING functional protein-protein network analysis supported our in silico docking and dynamic simulation results. This analysis also included comparisons of the binding modes of mAbs to protein receptors involved in the cancer hallmark mechanism(s), demonstrating that IL-6, B7-2, and CTLA-4 act and are inhibited in group-mode. Functional enrichment analysis and molecular modelling supported the relationship between the hallmarks of the tumor microenvironment that link inflammation to immunity. Tislelizumab may therefore be able to prevent these three proteins from actin

关键词： B7-2 Colorectal Cancer CTLA-4 Dostarlimab IL-6 Immunotherapy PD-1 Tislelizumab

Quasi-Best Match Graphs

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Korchmaros, Annachiara Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Quasi-best match graphs (qBMGs) are a hereditary class of directed, properly vertex-colored graphs. They arise naturally in mathematical phylogenetics as a generalization of best match graphs, which formalize the notion of evolutionary closest relatedness of genes (vertices) in multiple species (vertex colors). They are explained by rooted trees whose leaves correspond to vertices. In contrast to BMGs, qBMGs represent only best matches at a restricted phylogenetic distance. We provide characterizations of qBMGs that give rise to polynomial-time recognition algorithms and identify the BMGs as the qBMGs that are color-sink-free. Furthermore, two-colored qBMGs are characterized as directed graphs satisfying three simple local conditions, two of which have appeared previously, namely bi-transitivity in the sense of Das et al. (2021) and a hierarchy-like structure of out-neighborhoods, i.e., N(x) ∩ N(y) ∈ {N(x), N(y), ∅} for any two vertices x and y. Further results characterize qBMGs that can be explained by binary phylogenetic trees. Copyright © 2021, The Authors. All rights reserved.

关键词： Phylogenetic

Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

Corrigendum to“Antibiotic Treatment Drives the Diversification of the Human Gut Resistome”[Genomics Proteomics bioinformatics 17(1)(2019)39-51]

学校读者我要写书评

暂无评论

Genomics, Proteomics & bioinformatics 2020年第5期18卷 624-625页

作者： Jun Li Elizabeth A.Rettedal Eric van der Helm Mostafa Ellabaan Gianni Panagiotou Morten O.A.Sommer Department of Infectious Diseases and Public Health Colleague of Veterinary Medicine and Life SciencesCity University of Hong KongHong Kong Special Administrative RegionChina School of Data Science City University of Hong KongHong Kong Special Administrative RegionChina Novo Nordisk Foundation Center for Biosustainability DK-2900 HørsholmDenmark Systems Biology and Bioinformatics Unit Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute07745 JenaGermany Systems Biology and Bioinformatics Group School of Biological SciencesFaculty of SciencesThe University of Hong KongHong Kong Special Administrative RegionChina Department of Microbiology Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina

The editors regret there was an error in“Shotgun metagenome library construction and sequencing”section.“The raw sequences can be found in BGID(CRA000815)”should be corrected to“The raw metagenome sequencing data have been deposited in the Genome Sequence Archive at Beijing Institute of Genomics,Chinese Academy of sciences(GSA:CRA000815),and are publicly accessible at https://***/gsa/”.The correct section is shown *** editors would like to apologize for any inconvenience caused.

关键词： (1) Antibiotic Genome

Exploring Causal Effects of Hormone- and Radio-Treatments in an Observational Study of Breast Cancer Using Copula-Based Semi-Competing Risks Models

学校读者我要写书评

暂无评论

Statistics in medicine 2025年第13-14期44卷 e70131页

作者： Tonghui Yu Mengjiao Peng Yifan Cui Elynn Chen Chixiang Chen School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore. School of Statistics East China Normal University Shanghai China. Center for Data Science Zhejiang University Hangzhou China. Stern School of Business New York University New York NY USA. Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health University of Maryland School of Medicine Baltimore MD USA. Department of Neurosurgery University of Maryland School of Medicine Baltimore MD USA.

Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon, known as semi-competing risk, imposes challenges in analyzing treatment effects on breast cancer and necessitates advanced statistical tools for unbiased analysis. Despite progress in estimation and inference within semi-competing risks regression, its application to causal inference is still in its early stages. This article aims to propose a frequentist and semi-parametric framework based on copula models that can facilitate valid causal inference, net quantity estimation and interpretation, and sensitivity analysis for unmeasured factors under right-censored semi-competing risks data. We also propose novel procedures to enhance parameter estimation and its applicability in practice. After that, we apply the proposed framework to a breast cancer study and detect the time-varying causal effects of hormone- and radio-treatments on patients' relapse and overall survival. Moreover, extensive numerical evaluations demonstrate the method's feasibility, highlighting minimal estimation bias and reliable statistical inference.

关键词： principle stratification semiparametric copula models semi‐competing risks sensitivity analysis treatment effects

Prediction of Oral Food Challenge Outcomes via Ensemble Learning

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann ArborMI United States Department of Internal Medicine University of Michigan Ann ArborMI United States Department of Pediatrics University of Michigan Ann ArborMI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Computer Science and Engineering University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States Mary H. Weiser Food Allergy Center University of Michigan Ann ArborMI United States Department of Computer Science Queens College City University of New York New YorkNY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥ 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

PapSmear Image Recording System for Artificial Intelligence data Collection

学校读者我要写书评

暂无评论

IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Cervical cancer is one of the most leading causes of death for women worldwide. To reduce the mortality caused by cervical cancer, early screening techniques such as pap smear need to be carried out more extensively. Forthat, the availability of an automatic screening system is essential. In this paper, we proposed a system that can collect the dataset needed to train an Artificial Intelligence (AI) system for the automatic pap smear screening system. The proposed system can be integrated seamlessly to the current pap smear result recording procedure; hence avoiding any possible complication.

关键词：