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Recording provenance of workflow runs with RO-Crate

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arXiv 2023年

作者： Leo, Simone Crusoe, Michael R. Rodríguez-Navas, Laura Sirvent, Raül Kanitz, Alexander De Geest, Paul Wittner, Rudolf Pireddu, Luca Garijo, Daniel Fernández, José M. Colonnelli, Iacopo Gallo, Matej Ohta, Tazro Suetake, Hirotaka Capella-Gutierrez, Salvador de Wit, Renske Kinoshita, Bruno P. Soiland-Reyes, Stian CA Pula Italy Vrije Universiteit Amsterdam Amsterdam Netherlands DTL Projects Netherlands Forschungszentrum Jülich Germany Barcelona Supercomputing Center Barcelona Spain Biozentrum University of Basel Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland VIB Data Core Gent Belgium Faculty of Informatics Masaryk University Brno Czech Republic Institute of Computer Science Masaryk University Brno Czech Republic BBMRI-ERIC Graz Austria Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Computer Science Department Università degli Studi di Torino Torino Italy Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Shizuoka Japan Institute for Advanced Academic Research Chiba University Chiba Japan Sator Incorporated Tokyo Japan Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and *** to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. © 2023, CC BY.

关键词： Workflow management

Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine 3

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic...

3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Samodro, Bayu Mahesworo, Bharuno Suparyanto, Teddy Surya Atmaja, Don Bosco Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

Assessing the potential of PlanetScope satellite imagery to estimate particulate matter oxidative potential

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arXiv 2024年

作者： Hough, Ian Argentier, Loïc Jiang, Ziyang Zheng, Tongshu Bergin, Mike Carlson, David Jaffrezo, Jean-Luc Chanussot, Jocelyn Uzu, Gaëlle Univ. Grenoble Alpes CNRS INRAE IRD Grenoble INP IGE Grenoble38000 France Data Science Experts Grenoble38000 France Department of Civil and Environmental Engineering Duke University DurhamNC27708 United States Duke Kunshan University Division of Natural and Applied Sciences Kunshan215316 China Duke Global Health Institute Duke University DurhamNC27708 United States Department of Biostatistics and Bioinformatics Duke University DurhamNC27708 United States Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Since OP is not routinely monitored, it can be challenging to estimate exposure and health impacts. Remote sensing data are commonly used to estimate PM mass concentration, but have never been used to estimate OP. In this study, we evaluate the potential of satellite images to estimate OP as measured by acellular ascorbic acid (OPAA) and dithiothreitol (OPDTT) assays of 24-hour PM10 sampled periodically over five years at three locations around Grenoble, France. We use a deep convolutional neural network to extract features of daily 3 m/pixel PlanetScope satellite images and train a multilayer perceptron to estimate OP at a 1 km spatial resolution based on the image features and common meteorological variables. The model captures more than half of the variation in OPAA and almost half of the variation in OPDTT (test set R2 = 0.62 and 0.48, respectively), with relative mean absolute error (MAE) of about 32%. Using only satellite images, the model still captures about half of the variation in OPAA and one third of the variation in OPDTT (test set R2 = 0.49 and 0.36, respectively) with relative MAE of about 37%. If confirmed in other areas, our approach could represent a low-cost method for expanding the temporal or spatial coverage of OP estimates. © 2024, CC BY.

关键词： Satellite imagery

CONCENTRATION INEQUALITIES AND OPTIMAL NUMBER OF LAYERS FOR STOCHASTIC DEEP NEURAL NETWORKS

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arXiv 2022年

作者： Caprio, Michele Mukherjee, Sayan PRECISE Center Department of Computer and Information Science University of Pennsylvania 3330 Walnut Street PhiladelphiaPA19104 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany The Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce an expected classifier (EC), and to give probabilistic upper bound for the classification error of the EC. We also state the optimal number of layers for the SDNN via an optimal stopping procedure. We apply our analysis to a stochastic version of a feedforward neural network with ReLU activation *** Codes Primary: 62M45, Secondary: 60G42 © 2022, CC BY.

关键词： Deep neural networks

AutoScore-ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes

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arXiv 2022年

作者： Saffari, Seyed Ehsan Ning, Yilin Feng, Xie Chakraborty, Bibhas Volovici, Victor Vaughan, Roger Ong, Marcus Eng Hock Liu, Nan Centre For Quantitative Medicine Duke-NUS Medical School Singapore Singapore Programme In Health Services And Systems Research Duke-NUS Medical School Singapore Singapore Department Of Biostatistics And Bioinformatics Duke University DurhamNC United States Department Of Statistics And Data Science National University Of Singapore Singapore Singapore Department Of Neurosurgery Erasmus MC Stroke Center Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Department Of Public Health Erasmus University Rotterdam Netherlands 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

Background Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patients. AutoScore is a machine learning-based automatic clinical score generator for binary outcomes. This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes. Methods The AutoScore-Ordinal framework is generated using the same 6 modules of the original AutoScore algorithm including variable ranking, variable transformation, score derivation (from proportional odds models), model selection, score fine-tuning, and model evaluation. To illustrate the AutoScore-Ordinal performance, the method was conducted on electronic health records data from the emergency department at Singapore General Hospital over 2008 to 2017. The model was trained on 70% of the data, validated on 10% and tested on the remaining 20%. Results This study included 445,989 inpatient cases, where the distribution of the ordinal outcome was 80.7% alive without 30-day readmission, 12.5% alive with 30-day readmission, and 6.8% died inpatient or by day 30 post discharge. Two point-based risk prediction models were developed using two sets of 8 predictor variables identified by the flexible variable selection procedure. The two models indicated reasonably good performance measured by mean area under the receiver operating characteristic curve (0.785 and 0.793) and generalized c-index (0.737 and 0.760), which were comparable to alternative models. Conclusion AutoScore-Ordinal provides an automated and easy-to-use framework for development and validation of risk prediction models for ordinal outcomes, which can systematically identify potential predictors from high-dimensional data. © 2022, CC BY.

关键词： Records management

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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Nature Communications 2025年第1期16卷 1-21页

作者： Zhang, Hefei Li, Xuhang Song, Dongyuan Yukselen, Onur Nanda, Shivani Kucukural, Alper Li, Jingyi Jessica Garber, Manuel Walhout, Albertha J. M. Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA United States Via Scientific Inc. Cambridge MA United States Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA United States Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA United States Department of Genetics and Genome Sciences University of Connecticut Health Center Farmington CT United States Pathology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present ‘Worm Perturb-Seq (WPS)’, a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking ‘pairwise modularity’ in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells. © The Author(s) 2025.

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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

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AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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Cistrome data Browser and Toolkit:analyzing human and mouse genomic data using compendia of ChlP-seq and chromatin accessibility data

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Quantitative Biology 2020年第3期8卷 267-276页

作者： Rongbin Zheng Xin Dong Changxin Wan Xiaoying Shi Xiaoyan Zhang Clifford A.Meyer Clin ical Tran slati onal Research Center Shan ghai Pulmonary HospitalSchool of Life Science and Tech no logyTbngji UniversityShanghai 200433China Department of Bioinformatics School of Life Science and TechnologyTongji UniversityShanghai 200092China Department of Data Science Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public HealthBostonMA 02215USA Center for Functional Cancer Epigenetics Dana-Farber Cancer InstituteBostonMA 02215USA

The Cistrome data Browser(DB)at the website(***/db)provides about 56,000 published human and mouse ChlP-seq,DNase-seq,and ATAC-seq chromatin profiles,which we have processed using uniform analysis and quality control *** Cistrome DB Toolkit at the website(***)was developed to allow users to investigate fundamental questions using this data *** this tutorial,we describe how to use the Cistrome DB to search for publicly available chromatin profiles,to assess sample quality,to access peak results,to visualize signal intensities,to explore DNA sequence motifs,and to identify putative target genes・We also describe the use of the Toolkit module to seek the factors most likely to regulate a gene of interest,the factors that bind to a given genomic interval(enhancer,SNP,etc.),and samples that have significant peak overlaps with user-defined peak *** tutorial guides biomedical researchers in the use of Cistrome DB resources to rapidly obtain valuable insights into gene regulatory questions.

关键词： ChlP-seq chromatin accessibility gene regulatory analysis transcription factor

AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber 3

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in ...

3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Rahutomo, Reza Mahesworo, Bharuno Cenggoro, Tjeng Wawan Budiarto, Arif Suparyanto, Teddy Surya Atmaja, Don Bosco Samoedro, Bayu Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Palm oil