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Frontiers in Computational Neuroscience 2024年 18卷 1283974页

作者： Liu, Hsin-Ping Phoa, Frederick Kin Hing Chen-Burger, Yun-Heh Lin, Shau-Ping Data Science Degree Program National Taiwan University Taipei Taiwan Institute of Statistical Science Academia Sinica Taipei Taiwan Department of Computer Sciences Heriot-Watt University Edinburgh United Kingdom Institute of Biotechnology National Taiwan University Taipei Taiwan

Introduction: The Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints. Method: In this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration. Results: The performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers. Copyright © 2024 Liu, Phoa, Chen-Burger and Lin.

关键词： Supply chain management

Bayesian Inference for Non-Synchronously Observed Diffusions

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arXiv 2025年

作者： Jasra, Ajay Kamatani, Kengo Wu, Amin School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider the problem of Bayesian inference for bi-variate data observed in time but with observation times which occur non-synchronously. In particular, this occurs in a wide variety of applications in finance, such as high-frequency trading or crude oil futures trading. We adopt a diffusion model for the data and formulate a Bayesian model with priors on unknown parameters along with a latent representation for the the so-called missing data. We then consider computational methodology to fit the model using Markov chain Monte Carlo (MCMC). We have to resort to time-discretization methods as the complete data likelihood is intractable and this can cause considerable issues for MCMC when the data are observed in low frequencies. In a high frequency observation frequencies we present a simple particle MCMC method based on an Euler–Maruyama time discretization, which can be enhanced using multilevel Monte Carlo (MLMC). In the low frequency observation regime we introduce a novel bridging representation of the posterior in continuous time to deal with the issues of MCMC in this case. This representation is discretized and fitted using MCMC and MLMC. We apply our methodology to real and simulated data to establish the efficacy of our methodology. © 2025, CC BY.

关键词： Crude oil

Assessment of CYP2D6 gene expression in liver tissue: Variability in CYP2D6 mRNA levels within genotype-predicted metabolizer phenotype groups

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Chemico-Biological Interactions 2025年 416卷

作者： Meaddough, Erika L. Sarasua, Sara M. Kunkel, Deborah Boccuto, Luigi Ganakammal, Satishkumar R. Moersen, Matt Farrell, Christopher L. School of Nursing Healthcare Genetics & Genomics Program College of Behavioral Social and Health Sciences Clemson University Clemson SC United States School of Mathematical and Statistical Sciences College of Science Clemson University Clemson SC United States Research Computing & Data Clemson Computing and Information Technology Clemson University Clemson SC United States

Pharmacogenetic (PGx) testing can be used to help guide drug therapy and decrease or avoid the risk of adverse drug reactions. CYP2D6 is an important pharmacogene in pharmacogenomics testing panels. However, phenoconversion, whereby an individual's ability to metabolize a drug does not match the genotype-predicted metabolizer status, is a confounding factor to the accurate application of PGx testing results to patient care. To address this issue, CYP2D6 expression between and within genotype-predicted CYP2D6 metabolizer phenotype groups was compared using WGS and RNA-Seq data from 134 normal liver tissue donors obtained from the GTEx program. Wide variability in CYP2D6 mRNA levels was observed within metabolizer phenotype groups. The median expression level for ultrarapid metabolizers (UMs) was 738.9 TPM (transcripts per million;196.8–778.9 TPM), 212.5 TPM (32.1–666.5 TPM) for normal metabolizers (NMs), 219.6 TPM for intermediate metabolizers (IMs) (22–389.8 TPM), and 121.2 TPM for poor metabolizers (PMs) (9.3–298.2 TPM). The PM and UM phenotypes were significant predictors of CYP2D6 expression (p = 0.0004 and p = 0.019, respectively). Interestingly, expression of the gene encoding human serum albumin (ALB) was also a significant predictor of CYP2D6 expression (p = 0.0003). data from 50 patients with hepatocellular carcinoma obtained from the TCGA program showed no significant difference in expression between tumor tissue (median = 119.7 TPM, range 0.16–817.7 TPM) and normal matched tissue (median = 143.3 TPM, range 26.2–810.7 TPM). Transcriptional regulation of CYP2D6 expression may contribute to differences in drug response and risk for CYP2D6 phenoconversion. Efforts to understand the role of gene expression to predict CYP2D6 phenoconversion may inform the use of PGx testing in the clinical setting. © 2025 The Authors

关键词： CYP2D6 Drug-metabolizing enzymes Gene expression Hepatocellular carcinoma Metabolizer phenotype Pharmacogenetics Phenoconversion

"playing the whole game": A data collection and analysis exercise with Google Calendar

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arXiv 2020年

作者： Kim, Albert Y. Hardin, Johanna Program in Statistical and Data Sciences Smith College Statistical and Data Sciences NorthamptonMA01063 United States

We provide an exercise suitable for early introduction in an undergraduate statistics or data science course that allows students to 'play the whole game' of data science: performing both data collection and data analysis. While many teaching resources exist for data analysis, such resources are not as abundant for data collection given the inherent difficulty of the task. Our proposed exercise centers around student use of Google Calendar to collect data with the goal of answering the question 'How do I spend my time?' On the one hand, the exercise involves answering a question with near universal appeal, but on the other hand, the data collection mechanism is not beyond the reach of a modal undergraduate student. A further benefit of this exercise is that it provides an opportunity for discussions on ethical questions and considerations that data providers and data analysts face in today's age of large-scale internet-based data collection. Copyright © 2020, The Authors. All rights reserved.

关键词： Students

Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience

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arXiv 2023年

作者： Maama, Mohamed Jasra, Ajay Kamatani, Kengo Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong CN Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [15], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models applied to examples of Bayesian inference problems using both simulated and real observed data from neuroscience. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests (vol 54, pg 1446, 2022)

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NATURE GENETICS 2022年第11期54卷 1755-1755页

作者： Zhou, Wei Bi, Wenjian Zhao, Zhangchen Dey, Kushal K. Jagadeesh, Karthik A. Karczewski, Konrad J. Daly, Mark J. Neale, Benjamin M. Lee, Seunggeun Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Department of Medical Genetics School of Basic Medical Sciences Peking University Beijing China Center for Statistical Genetics University of Michigan School of Public Health Ann Arbor MI USA Department of Biostatistics University of Michigan School of Public Health Ann Arbor MI USA Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA USA Institute for Molecular Medicine Finland Helsinki Institute of Life Sciences University of Helsinki Helsinki Finland Graduate School of Data Science Seoul National University Seoul Korea

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations.

关键词： Genome-wide association studies Software

Predicting drug response through tumor deconvolution by cancer cell lines

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Patterns 2024年第4期5卷 100949页

作者： Hsu, Yu-Ching Chiu, Yu-Chiao Lu, Tzu-Pin Hsiao, Tzu-Hung Chen, Yidong Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei115 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei100 Taiwan Greehey Children's Cancer Research Institute University of Texas Health San Antonio San AntonioTX78229 United States Department of Medicine School of Medicine University of Pittsburgh PittsburghPA15261 United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA15232 United States Department of Medical Research Taichung Veterans General Hospital Taichung40705 Taiwan Department of Population Health Sciences University of Texas Health San Antonio San AntonioTX78229 United States

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing. © 2024 The Authors

关键词： Tumors

SUPP & MAPP: ADAPTABLE STRUCTURE-BASED REPRESENTATIONS FOR MIR TASKS 21

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SUPP & MAPP: ADAPTABLE STRUCTURE-BASED REPRESENTATIONS FOR M...

21st International Society for Music Information Retrieval Conference, ISMIR 2020

作者： Savard, Claire Bugbee, Erin H. McGuirl, Melissa R. Kinnaird, Katherine M. Department of Physics University of Colorado Boulder United States Department of Biostatistics Brown University United States Division of Applied Mathematics Brown University United States Department of Computer Science and Program in Statistical and Data Sciences Smith College United States

ISBN: (纸本)9780981353708

Accurate and flexible representations of music data are paramount to addressing MIR tasks, yet many of the existing approaches are difficult to interpret or rigid in nature. This work introduces two new song representations for structure-based retrieval methods: Surface Pattern Preservation (SuPP), a continuous song representation, and Matrix Pattern Preservation (MaPP), SuPP’s discrete counterpart. These representations come equipped with several user-defined parameters so that they are adaptable for a range of MIR tasks. Experimental results show MaPP as successful in addressing the cover song task on a set of Mazurka scores, with a mean precision of 0.965 and recall of 0.776. SuPP and MaPP also show promise in other MIR applications, such as novel-segment detection and genre classification, the latter of which demonstrates their suitability as inputs for machine learning problems. © Claire Savard, Erin H. Bugbee, Melissa R. McGuirl, Katherine M. Kinnaird.

关键词： Information retrieval