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arXiv 2023年

作者： Hawinkel, Stijn Waegeman, Willem Maere, Steven Department of Plant Biotechnology and Bioinformatics Ghent University Belgium VIB Center for Plant Systems Biology Belgium Department of Data Analysis and Mathematical Modelling Ghent University Belgium

Out-of-sample prediction is the acid test of predictive models, yet an independent test dataset is often not available for assessment of the prediction error. For this reason, out-of-sample performance is commonly estimated using data splitting algorithms such as cross-validation or the bootstrap. For quantitative outcomes, the ratio of variance explained to total variance can be summarized by the coefficient of determination or in-sample R2, which is easy to interpret and to compare across different outcome variables. As opposed to the in-sample R2, the out-of-sample R2 has not been well defined and the variability on the out-of-sample R2 has been largely ignored. Usually only its point estimate is reported, hampering formal comparison of predictability of different outcome variables. Here we explicitly define the out-of-sample R2 as a comparison of two predictive models, provide an unbiased estimator and exploit recent theoretical advances on uncertainty of data splitting estimates to provide a standard error for the R2. The performance of the estimators for the R2 and its standard error are investigated in a simulation study. We demonstrate our new method by constructing confidence intervals and comparing models for prediction of quantitative Brassica napus and Zea mays phenotypes based on gene expression data. © 2023, CC BY.

关键词： Forecasting

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Decision Theory and Risk Simulation Analysis for Optimizing Profit in PayLater Services

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Procedia Computer Science 2023年 219卷 60-67页

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products.

关键词： PayLater decision-making Bayesian analysis fault tree analysis critical path method Monte Carlo simulation

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Fusion Contours as a Source of Segmentation Training data: A Simulation Study

Fusion Contours as a Source of Segmentation Training Data: A...

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IEEE International Symposium on Biomedical Imaging

作者： Dónal M. McSweeney Paul A. Bromiley Andrew Green Marcel van Herk Alan McWilliam Division of Cancer Science University of Manchester Manchester UK Division of Informatics Imaging and Data Sciences University of Manchester UK EMBL European Bioinformatics Institute Cambridge UK

Consensus contours are often used to reduce annotator error in segmentation data. Here, we investigate the use of multi-annotator labels, in simulated data, for training auto-segmentation models. We compare convolutional neural networks (CNNs) trained on all observer annotations, STAPLE and majority voting estimates. We generate annotation sets by simulating observer delineations with varying noise and bias. By altering the quantity of observers and their relative biases, we investigate the impact of bias on CNN *** find that models trained on STAPLE contours are significantly worse when presented with biased annotations. CNNs trained on all annotations performed the best and were able to implicitly account for biased annotations in the training set.

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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A semi-parametric estimation of personalized dose-response function using instrumental variables

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 4227-4264页

作者： Wei Luo Yeying Zhu Xuekui Zhang Lin Lin Center for Data Science Zhejiang University Hangzhou P.R.China Department of Statistics and Actuarial Science University of Waterloo Waterloo ON Canada Department of Mathematics and Statistics University of Victoria Victoria BC Canada Department of Biostatistics and Bioinformatics Duke University Durham NC

In the application of instrumental variable analysis that conducts causal inference in the presence of unmeasured confounding, invalid instrumental variables and weak instrumental variables often exist which complicate the analysis. In this paper, we propose a model-free dimension reduction procedure to select the invalid instrumental variables and refine them into lower-dimensional linear combinations. The procedure also combines the weak instrumental variables into a few stronger instrumental variables that best condense their information. We then introduce the personalized dose-response function that incorporates the subject's personal characteristics into the conventional dose-response function, and use the reduced data from dimension reduction to propose a novel and easily implementable nonparametric estimator of this function. The proposed approach is suitable for both discrete and continuous treatment variables, and is robust to the dimensionality of data. Its effectiveness is illustrated by the simulation studies and the data analysis of ADNI-DoD study, where the causal relationship between depression and dementia is investigated.

关键词： causal inference central mean subspace Mendelian randomization SCAD sufficient dimension reduction

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Short-term Weather Forecasting for Wind Energy Generation using A Deep Learning Technique 5

Short-term Weather Forecasting for Wind Energy Generation us...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Zim, Abid Hasan Zeyad, Mohammad Ahmed, S.M. Masum Hossain, Eftakhar Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh ABCD Laboratory Bangladesh Chattogram Bangladesh Heriot-Watt University Edinburgh United Kingdom University of Antwerp Faculty of Applied Engineering - Construction Antwerp Belgium

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Inadequate or erroneous weather predictions have a great impact on wind turbine energy output. Certain weather fluctuations and interruptions affect the efficient operation of wind turbines. Short-term weather predictions that are calibrated and verified make it simple to reduce the threat presented by meteorological uncertainty. Conventional physics-based techniques for forecasting are relatively weaker than data-driven approaches since wind turbine components usually have a multipart nonlinear relationship. Compared to physical methods, non-parametric alternatives like deep learning are thought to be more stable and lead to fewer mistakes in analyst predictions. This research presents the state-of-the-art Temporal Convolutional Network (TCN), one of the deep learning methods, which is introduced as a means of enhancing the precision of short-term forecasting of weather variables. TCN is an architecture that makes use of casual convolutions and dilations in order to be adaptable for sequential data with its temporality and huge receptive fields. Seven significant meteorological parameters from the NASA POWER dataset of Chouchala Sea Beach in Chittagong, Bangladesh, over a period of four months were used in this research. The accuracy was measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results obtained from the suggested TCN model demonstrate that the predicted outcome is pretty accurate and consistent. © 2022 IEEE.

关键词： Wind turbines

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P522 Characterization of linkage disequilibrium between DRB1*01 and DRB5

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Human Immunology 2023年 84卷 145-146页

作者： Norvell, B. Williams, J. Norvell, B. Williams, J. Liu, H. Iyer, L. Riley, E. Coggins, M. Deters, J. Mone, C. Research and Development Labcorp Burlington North Carolina United States Data Science Artificial Intelligence and Bioinformatics Labcorp Westborough Massachusetts United States Consumer Genetics Labcorp Burlington North Carolina United States

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 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 Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Concordance between an FDA-approved companion diagnostic and an alternative assay kit for assessing homologous recombination deficiency in ovarian cancer

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GYNECOLOGIC ONCOLOGY 2024年 184卷 67-73页

作者： Wehn, Amy K. Qiu, Ping Lunceford, Jared Yarunin, Alexander Cristescu, Razvan Liu, Li Roessler, Kyria Bilke, Sven Timms, Kirsten M. Weichert, Wilko Marton, Matthew J. Merck & Co Inc MRL Rahway NJ 07065 USA Merck & Co Inc Biostat & Res Decis Sci MRL Rahway NJ USA AstraZeneca Affairs Global Oncol Milton Cambridge England Merck & Co Inc Data & Genom Sci Rahway NJ USA Illumina Assay Dev San Diego CA USA Illumina Bioinformatics San Diego CA USA Myriad Genet Inc Salt Lake City UT USA Tech Univ Munich Inst Pathol Munich Germany 126 E Lincoln Ave Rahway NJ 07065 USA

Objective: Authors evaluated the performance of a commercially available next-generation sequencing assay kit;this was based on genomic content from Illumina's TruSight (TM) Oncology 500 research assay that identifies BRCA variants and proprietary algorithms licensed from Myriad and, with additional genomic content, measures the homologous recombination deficiency (HRD) genomic instability score (GIS) in tumor tissue (TSO 500 HRD assay). Methods: data from the TSO 500 HRD assay were compared with data from the Myriad MyChoice (R) CDx PLUS assay (Myriad assay). Prevalence rates for overall HRD status and BRCA mutations (a deleterious or suspected deleterious BRCA1 or BRCA2 mutation or both) and assay agreement rates for HRD GIS and BRCA analysis were assessed in ovarian tumor samples. Pearson correlations of the continuous HRD GIS and analytic sensitivity and specificity were evaluated. Results: The prevalence of overall HRD positivity was 51.2% (TSO 500 HRD assay) versus 49.2% (Myriad assay) and the prevalence of BRCA mutations was 27.6% (TSO 500 HRD assay) versus 25.5% (Myriad assay). After post-processing optimization, concordance of the HRD GIS was 0.980 in all samples and 0.976 in the non-BRCA mutation cohort;the area under the receiver operating characteristic curve was 0.995 and 0.992, respectively. Conclusions: Comparison between the Illumina and Myriad assays showed that overall HRD status, the individual components of BRCA analysis, and HRD GIS detection results were highly concordant (>93%), suggesting the TSO 500 HRD assay will approach the analytical accuracy of the FDA-approved Myriad assay.

关键词： Homologous recombination de ficiency Genomic instability score Ovarian cancer BRCA

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Artificial Intelligence in IBD: How Will It Change Patient Management?

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Current Treatment Options in Gastroenterology 2023年第4期21卷 365-377页

作者： Stone, Molly L. Stidham, Ryan W. Division of Gastroenterology Department of Internal Medicine Michigan Medicine Ann Arbor USA Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor USA Michigan Institute for Data Science (MIDAS) University of Michigan Ann Arbor USA

Artificial intelligence (AI) is quickly evolving and will be integral to the management of IBD patients. In this review, we provide an overview of technologies powering applications for Crohn’s disease and ulcerative colitis, with particular attention to information extraction from imaging and text records. Recent data highlight machine learning capability to replicate expert interpretation and judgment at population scale with accuracy in endoscopic and histology disease grading. Computer vision techniques are also detecting important findings difficult for even expert clinicians to reliably appreciate, including dysplasia on colonoscopy and fibrosis on CT and MRI imaging. Further, segmentation and radiomics can extract more information than manually possible, enabling new possibilities for disease measurement. Finally, natural language processing (NLP) is showing promise automatically extracting IBD-related medical concepts from text and generating conversational responses for both clinicians and patients. Quickly mounting evidence supports AI reliability grading disease severity in endoscopy, histology, and imaging. In particular use cases, AI can surpass the ability of humans for disease feature detecting and the generation of new measures of IBD activity. Ultimately, AI-powered information extraction will provide significant incremental improvement in the personalization predictions of outcomes and treatment course in IBD.

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