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1st Workshop on the science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM 2015

作者： Shahand, Shayan Van Kampen, Antoine H. C. Olabarriaga, Sílvia D. e-Science Group Dept. of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Bioinformatics Laboratory Dept. of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Biosystems Data Analysis Group Swammerdam Institute for Life Sciences University of Amsterdam Netherlands

ISBN: (纸本)9781450335669

science Gateways (SGs) have emerged as systems that facilitate access to cyberinfrastructures. There is a growing interest in the exploitation and development of SGs. However, it remains challenging to understand and design SG with the required properties because of the complex nature of SGs. Additionally, it is difficult to decide upon frameworks that can be used to build them. In this paper we propose the science Gateway Canvas, a business reference model for SGs that embodies the common SG functions and their organization into groups and categories. We used the science Gateway Canvas for a systematic analysis and comparison of the functions offered by a selection of available frameworks that are used to build SGs. This illustrated the applicability of the science Gateway Canvas as a comprehensive and generic reference model for understanding SGs. The canvas can also be used for systematic analysis by scientists who are searching for an existing SG that supports their research goal or developers who want to determine the functional requirements for a new SG. Copyright © 2015 ACM.

关键词： Business reference model Collaboratory Cyberinfrastructure e-Infrastructure e-Research environment e-science Problem Solving Environment (PSE) science Gateway (SG) science Gateway Canvas (SGC) science Gateway development science Gateway frameworks science Gateway technologies science Portal Virtual Laboratory (VL) Virtual Research Environment (VRE)

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Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from A data-driven Perspective

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Genomics, Proteomics & bioinformatics 2023年第1期21卷 164-176页

作者： Jianlei Gu Jiawei Dai Hui Lu Hongyu Zhao SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision MedicineShanghai Children’s HospitalShanghai 200040China Department of Biostatistics Yale UniversityNew HavenCT 06511USA

Comprehensive characterization of spatial and temporal gene expression patterns in humans is critical for uncovering the regulatory codes of the human genome and understanding the molecular mechanisms of human *** expressed genes(UEGs)refer to the genes expressed across a majority of,if not all,phenotypic and physiological conditions of an *** is known that many human genes are broadly expressed across ***,most previous UEG studies have only focused on providing a list of UEGs without capturing their global expression patterns,thus limiting the potential use of UEG *** this study,we proposed a novel data-driven framework to leverage the extensive collection of40,000 human transcriptomes to derive a list of UEGs and their corresponding global expression patterns,which offers a valuable resource to further characterize human *** results suggest that about half(12,234;49.01%)of the human genes are expressed in at least 80%of human transcriptomes,and the median size of the human transcriptome is 16,342 genes(65.44%).Through gene clustering,we identified a set of UEGs,named LoVarUEGs,which have stable expression across human transcriptomes and can be used as internal reference genes for expression *** further demonstrate the usefulness of this resource,we evaluated the global expression patterns for 16 previously predicted disallowed genes in islet beta cells and found that seven of these genes showed relatively more varied expression patterns,suggesting that the repression of these genes may not be unique to islet beta cells.

关键词： Ubiquitous expression Housekeeping gene Disallowed gene Expression specificity Expression variability

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Parallel Genome-Wide Analysis With Central And Graphic Processing Units

Parallel Genome-Wide Analysis With Central And Graphic Proce...

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2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Muhamad Fitra Kacamarga James W.Baurley Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University

The Indonesia Colorectal Cancer Consortium(IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the Map Reduce and Compute Unified Device Architecture(CUDA) frameworks and evaluate performance(speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data(1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620(6 cores) and NVIDIA Tesla K20 over sequential processing.

关键词： Genome-wide Analysis Parallel Programming MapReduce CUDA

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CanSeer: a translational methodology for developing personalized cancer models and therapeutics

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Scientific Reports 2025年第1期15卷 1-22页

作者： Butt, Rida Nasir Amina, Bibi Sultan, Muhammad Umer Tanveer, Zain Bin Gondal, Mahnoor Naseer Hussain, Risham Khan, Salaar Akbar, Rida Nasir, Zainab Khalid, Muhammad Farhan Channan-Khan, Asher Alban Faisal, Amir Shoaib, Muhammad Chaudhary, Safee Ullah Biomedical Informatics and Engineering Research Laboratory (BIRL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor 48109 MI United States Michigan Center for Translational Pathology University of Michigan Ann Arbor 48109 MI United States Data Science Institute Lancaster University Lancaster LA1 4YW United Kingdom Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology Penn State University University Park 16802 PA United States Mayo Clinic School of Medicine New York University NY United States Cancer Therapeutics Lab Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Epigenome and Genome Integrity Lab (EaGIL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan

Computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose “CanSeer”—a methodology for developing personalized therapeutics. CanSeer employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. CanSeer reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc. © The Author(s) 2025.

关键词： Biomolecular network modeling Dynamical analysis of in silico models Integrative omics Personalized mono- and combinatorial therapies Therapeutic screening Translational cancer systems biology

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CLASS: A general approach to classifying categorical sequences

CLASS: A general approach to classifying categorical sequenc...

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作者： Kelil, Abdellali Nordell-Markovits, Alexei Ousman Yassine Zaralahy, Parakh Wang, Shengrui ProspectUS Data Mining and Bioinformatics Laboratory Department of Computer Science Université de Sherbrooke Sherbrooke QC J1K 2R1 Canada

The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Signifi cant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches. © 2009 IEEE.

关键词： Semantics

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Short-Term Effects of Ambient Air Pollutants on Outpatient Visits for Childhood Allergic Diseases in Shanghai,China

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Journal of Shanghai Jiaotong university(science) 2024年第6期29卷 979-994页

作者： HU Yi GU Jianlei WU Dan WANG Xiaolei LU Hui YU Guangjun Center for Biomedical Informatics Shanghai Children’s HospitalShanghai Jiao Tong University School of MedicineShanghai 200040China SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsShanghai Jiao Tong UniversityShanghai 200240China Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai 200040China

This study aims to investigate the short-term effects of ambient air pollutants on outpatient visits for childhood allergic *** data on ambient air pollutants(NO2,SO2,CO and PM2.5)and outpatient visits for childhood allergic diseases(asthma,atopic dermatitis and allergic rhinitis)were obtained in Shanghai,China from 2013 to *** effects of ambient air pollutants were estimated for total outpatient visits for childhood allergic diseases,gender and age stratification and disease classification by using distributed lag non-linear model(DLNM).We found positive associations between short-term exposure to air pollutants and childhood allergic *** and children aged 7 years old were more likely to be sensitive to ambient air ***2 and SO2 showed stronger effects on asthma and atopic dermatitis,*** study provides evidence that short-term exposure to ambient air pollutants can increase the risk of childhood allergic diseases.

关键词： childhood allergic diseases ambient air pollutant nitrogen dioxide(NO2) sulfur dioxide(SO2) particulate matter<2.5μm in diameter(PM2.5)

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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On testing proportional odds assumptions for proportional odds models

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General Psychiatry 2023年第3期36卷 214-219页

作者： Anqi Liu Hua He Xin M Tu Wan Tang Department of Bostaistis and Data Science Tulane UniversityNew OrleansLouisianaUSA Department of Epidemiology Tulane UniversityNew OrleansLouisianaUSA Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity ScienceUC San DiegoLa JollaCaliforniaUSA

Proportional odds models are commonly used to model ordinal responses,but the proportional odds assumption may not hold in practice,leading to biased *** such as score,Wald and likelihood ratio(LR)have been proposed to evaluate the proportional odds assumption based on models without the *** has proposed an independent binary model-based Wald-type test,and Wolfe and Gould have extended the idea to propose an LR-type *** paper provides a brief review of the Brant and Wolfe-Gould tests for evaluating the proportional odds assumption and evaluates their performance through simulation studies and a real data *** programs are provided in SAS,SPSS and Stata to facilitate the implementation of these tests using standard statistical software *** study highlights the importance of evaluating the proportional odds assumption when using proportional odds models for ordinal *** sample programs provided in this paper make it easy for researchers to apply these tests in their own analyses using standard statistical software packages.

关键词： Gould testing apply

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Low-rank tensor estimation via Riemannian Gauss-Newton: statistical optimality and second-order convergence

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 18274-18321页

作者： Yuetian Luo Anru R. Zhang Data Science Institute University of Chicago Chicago IL Departments of Biostatistics & Bioinformatics and Computer Science Duke University Durham NC

In this paper, we consider the estimation of a low Tucker rank tensor from a number of noisy linear measurements. The general problem covers many specific examples arising from applications, including tensor regression, tensor completion, and tensor PCA/SVD. We consider an efficient Riemannian Gauss-Newton (RGN) method for low Tucker rank tensor estimation. Different from the generic (super)linear convergence guarantee of RGN in the literature, we prove the first local quadratic convergence guarantee of RGN for lowrank tensor estimation in the noisy setting under some regularity conditions and provide the corresponding estimation error upper bounds. A deterministic estimation error lower bound, which matches the upper bound, is provided that demonstrates the statistical optimality of RGN. The merit of RGN is illustrated through two machine learning applications: tensor regression and tensor SVD. Finally, we provide the simulation results to corroborate our theoretical findings.

关键词： low-rank tensor estimation quadratic convergence Riemannian optimization statistical optimality

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Effects of sickle trait on maternal and perinatal outcomes among pregnant women

Blood Vessels, Thrombosis & Hemostasis

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Blood Vessels, Thrombosis & Hemostasis 2025年第2期2卷 100049-100049页

作者： Sarah Sewaralthahab Jiling Chou Stephen Fernandez Nawar Shara Hedy P. Smith Department of Medicine College of Medicine King Saud University Riyadh Saudi Arabia Division of Hematology Oncology Center King Saud University Medical City Riyadh Saudi Arabia Department of Biostatistics Bioinformatics and Biomathematics Center of Biostatistics Informatics and Data Science Medstar Research Institute Hyattsville MD Depatment of Internal Medicine Medstar Washington Hospital Center Washington DC

Abstract The risk of multiple adverse pregnancy outcomes and perinatal outcomes among pregnant women with sickle cell trait (SCT) is not known. Our objective was to compare differences in adverse outcomes, specifically pregnancy-related hypertensive disease (PRHD), pyelonephritis/urinary tract infection (UTI), and low birth weight (LBW), between pregnant women with SCT and healthy controls. This was a retrospective cohort study of women who delivered between 2015 and 2020. We included all women with SCT, that is, hemoglobin electrophoresis AS. Women with SCT were matched in a 1:2 to women without SCT, controlling for age, gravidity, and parity. Our primary outcomes were PRHD, pyelonephritis/UTI, and LBW baby. Multivariable logistic regression modeling examined the associations between patients’ characteristics and the primary *** were 162 women with SCT, and 324 healthy control women were enrolled. Bivariate analysis revealed that women with SCT had a higher proportion of PRHD (38.9% vs 34.9%; P = .39), pyelonephritis/UTI (11.7% vs 7.1%; P = .09), but a lower proportion of LBW (10.5% vs 16.0%; P = .1). In multivariable analysis, after controlling for confounders, SCT was not an independent predictor of PRHD. However, SCT was an independent predictor of pyelonephritis/UTI (adjusted odds ratio [aOR], 1.98; 95% confidence interval [CI], 1.02-3.85) and of a lower risk of having a LBW baby (aOR, 0.48; 95% CI, 0.25-0.94). SCT is not associated with an increased risk of PRHD. However, SCT is associated with pregnancy outcomes, including higher risk of pyelonephritis/UTI but a lower risk of LBW babies.

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