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APIR:Aggregating Universal Proteomics database Search Algorithms for Peptide Identification with FDR Control

Genomics, Proteomics & Bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li Department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA Department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA Department of Quantitative and Computational Biology University of Southern CaliforniaLos AngelesCA 90089USA Department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA Department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA Bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA Department of Human Genetics University of CaliforniaLos AngelesCA 90095USA Department of Computational Medicine University of CaliforniaLos AngelesCA 90095USA Department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

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Operations Research and Analytics to combat human trafficking: A systematic review of academic literature

arXiv

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arXiv 2021年

作者： Dimas, Geri L. Konrad, Renata A. Maass, Kayse Lee Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute United States Business School Worcester Polytechnic Institute United States Mechanical and Industrial Engineering Department Northeastern University United States Business School Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute United States

Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. While there have been an increasing number of anti-human trafficking works from the Operations Research and Analytics domains in recent years, no systematic review of this literature currently exists. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain, thereby illustrating the collective impact of the field to date. We classify 142 studies to identify current trends in methodologies, theoretical approaches, data sources, trafficking contexts, target regions, victim-survivor demographics, and focus within the well-established 4Ps principles. Using these findings, we discuss the extent to which the current literature aligns with the global demographics of human trafficking and identify existing research gaps to propose an agenda for Operations Research and Analytics researchers. Copyright © 2021, The Authors. All rights reserved.

关键词： Crime

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SHELLEY: Exploring Learning-Based Network Alignment on Biological data

SHELLEY: Exploring Learning-Based Network Alignment on Biolo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Riccardo De Luca Manuela Petti Pietro H. Guzzi Paolo Tieri MSc Program in Data Science Sapienza University of Rome Rome Italy DIAG Dept. Comp. Contr. Manag. Eng. Sapienza University of Rome Rome Italy Dept. UMG Magna Graecia University Catanzaro Italy IAC Institute for Applied Computing CNR National Research Council Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biological field, particularly for discovering similar roles between the elements of different systems or for transferring knowledge from a well-studied system to another. In this paper, we present SHELLEY, a tool that facilitates the development, testing, and combination of learning-based network alignment algorithms by providing a set of modules that allow for the recreation and combination of both representation learning methods (RLMs) and deep matching methods (DMMs). We then present a case study in which we apply this tool to a protein-protein interaction network (PPI), demonstrating how the representation phase of RLMs is crucial for model robustness against *** code of SHELLEY is available at: https://***/rickydeluca/shelley

关键词： Representation learning Proteins Protein engineering Codes Biological system modeling Noise Robustness data models Biological information theory Testing

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Evolution of electronic and magnetic properties in the topological semimetal SmSbxTe2−x

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Physical Review B 2022年第15期105卷 155139-155139页

作者： Krishna Pandey Rabindra Basnet Jian Wang Bo Da Jin Hu Materials Science and Engineering Program Institute for Nanoscience and Engineering University of Arkansas Fayetteville Arkansas 72701 USA Department of Physics University of Arkansas Fayetteville Arkansas 72701 USA Department of Chemistry and Biochemistry Wichita State University Wichita Kansas 67260 USA Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan

The ZrSiS-type materials have attracted intensive attention due to the existence of various topological fermions. The magnetic version of the ZrSiS-type materials, LnSbTe (Ln=lanthanides), is an ideal candidate to explore novel exotic states due to the interaction between magnetism and topology. In this work, we report the experimental study on structural, magnetic, thermodynamic, and electronic properties for SmSbxTe2−x with various Sb content. The revealed evolutions of these properties with tuning the compositions would provide useful insights for the fundamental topological physics and the future applications.

关键词： Magnetic order Topological materials Topological phases of matter Antiferromagnets Single crystal materials Strongly correlated systems Crystal growth Crystal structures Energy-dispersive x-ray spectroscopy Magnetization measurements Specific heat measurements X-ray diffraction

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Human-like Explanation for Text Classification With Limited Attention Supervision

Human-like Explanation for Text Classification With Limited ...

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IEEE International Conference on Big data

作者： Dongyu Zhang Cansu Sen Jidapa Thadajarassiri Thomas Hartvigsen Xiangnan Kong Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA CodaMetrix Boston USA Worcester Polytechnic Institute Worcester USA

ISBN: (纸本)9781665445993

Human-like explanation for text classification is essential for high-impact settings such as healthcare where human rationales are required to support specialists’ decisions. Conventional approaches learn explanations using attention mechanisms to assign heavy weights to words that have a high impact on a model’s prediction. However, such heavily-weighted words often do not reflect human intuition. To advance human rationale, recent studies propose to supervise attention mechanisms assuming access to a huge set of attention labels collected from humans, called human attention maps (HAMs). Unfortunately, acquiring such HAMs for a huge dataset is very tedious, error-prone, and expensive in practice. Thus, we propose the novel problem of text classification with limited human attention supervision. Specifically, we study the learning of human-like attention weights from a dataset in which all documents contain classification labels but only a few documents provide HAMs. To this end, we design a deep learning architecture, HELAS: Human-like Explanation with Limited Attention Supervision to adaptively learn attention weights that focus on words analogous to a human with very limited attention supervision. HELAS effectively unifies joint learning improving both tasks of text classification and humanlike explanation even with only insufficient supervision labels for the latter task. Our experiments show that HELAS generates attention maps similar to real human annotations raising similarity scores up to 22% over state-of-the-art alternatives, even with as little as 2% of the documents having HAMs. It concurrently improves text classification by driving accuracy up to 19% over four state-of-the-art methods.

关键词： Training Deep learning Conferences Text categorization Bit error rate Medical services Big data

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Combining Experimental and Observational data for Identification and Estimation of Long-Term Causal Effects

arXiv

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arXiv 2022年

作者： Ghassami, AmirEmad Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen Department of Computer Science Johns Hopkins University United States Department of Electrical Engineering Stanford University United States Department of Environmental and Occupational Health Program in Public Health University of California Irvine United States Department of Statistics and Data Science The Wharton School University of Pennsylvania United States

We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational domain is subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period of time;hence, long-term effects of treatment are unobserved but short-term effects will be observed. Therefore, data from neither domain alone suffices for causal inference about the effect of the treatment on the long-term outcome, and must be pooled in a principled way, instead. Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal effect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we first review their proposed approach, and then we propose three alternative approaches for data fusion for the purpose of identifying and estimating average treatment effect as well as the effect of treatment on the treated. Our first approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second approach is based on a relaxed version of the equi-confounding bias assumption, where we assume the existence of an observed confounder such that the short-term and long-term potential outcome variables have the same partial additive association with that confounder. Our third approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation. We propose influence function-based estimation strategies for each of our data fusion frameworks and study the robustness properties of the proposed estimators. © 2022, CC BY.

关键词： data fusion

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Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model

arXiv

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arXiv 2023年

作者： Zhang, Cheng Perezhogin, Pavel Gultekin, Cem Adcroft, Alistair Fernandez-Granda, Carlos Zanna, Laure Program in Atmospheric and Oceanic Sciences Princeton University PrincetonNJ08542 United States Courant Institute of Mathematical Sciences New York University New YorkNY10012 United States Center for Data Science New York University New YorkNY10011 United States

We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one particular machine learned parameterization (Guillaumin & Zanna, 2021) and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the numerical model. This includes trying the parameterization in a different flow regime from that in which it was trained, at different spatial resolutions, and with other differences, all to test generalization. We assess whether tuning is possible, which is a common practice in general circulation model development. We find the parameterization, without modification or special treatment, to be stable and that the action of the parameterization to be diminishing as spatial resolution is refined. We also find some limitations of the machine learning model in implementation: 1) tuning of the outputs from the parameterization at various depths is necessary;2) the forcing near boundaries is not predicted as well as in the open ocean;3) the cost of the parameterization is prohibitively high on CPUs. We discuss these limitations, present some solutions to problems, and conclude that this particular ML parameterization does inject energy, and improve backscatter, as intended but it might need further refinement before we can use it in production mode in contemporary climate models. © 2023, CC BY.

关键词： Parameterization

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Discrete Event Simulation to Evaluate Shelter Capacity Expansion Options for LGBTQ+ Homeless Youth 22

Discrete Event Simulation to Evaluate Shelter Capacity Expan...

引用

Proceedings of the Winter Simulation Conference

作者： Yaren Bilge Kaya Sophia Mantell Kayse Lee Maass Renata Konrad Andrew C. Trapp Geri L. Dimas Meredith Dank Mechanical and Industrial Engineering Northeastern University Boston MA USA School of Business Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA Marron Institute of Urban Management New York University New York NY USA

The New York City (NYC) youth shelter system provides housing, counseling, and other support services to runaway and homeless youth and young adults (RHY). These resources reduce RHY's vulnerability to human trafficking, yet most shelters are unable to meet demand. This paper presents a Discrete Event Simulation (DES) model of a crisis-emergency and drop-in center for LGBTQ+ youth in NYC, which aims to analyze the current operations and test potential capacity expansion interventions. The model uses data from publicly available resources and interviews with service providers and key stakeholders. The simulated shelter has 66 crisis-emergency beds, offers five different support services, and serves on average 1,399 LGBTQ+ RHY per year. The capacity expansion interventions examined in this paper are adding crisis-emergency beds and psychiatric therapists. This application of DES serves as a tool to communicate with policymakers, funders, and service providers---potentially having a strong humanitarian impact.

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Nuclear many-body effects on particle emission following muon capture on Si28 and Ca40

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Physical Review C 2023年第5期107卷 054314-054314页

作者： Futoshi Minato Tomoya Naito Osamu Iwamoto Nuclear Data Center Japan Atomic Energy Agency Tokai Ibaraki 319-1195 Japan RIKEN Nishina Center Wako Saitama 351-0198 Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan Department of Physics Graduate School of Science The University of Tokyo Tokyo 113-0033 Japan

Background: Muon captures on nuclei have provided us with plenty of knowledge of nuclear properties. Recently, this reaction attracts attention in electronics, because charged particle emissions following muon capture... 详细信息

关键词： Electron & muon capture Electroweak interactions in nuclear physics Lepton induced nuclear reactions Nuclear decay Nuclear many-body theory Nuclear structure & decays 20 ≤ A ≤ 38 39 ≤ A ≤ 58 Nuclear density functional theory

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