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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Rouzbeh Hasheminezhad Moses Boudourides Ulrik Brandes Social Networks Lab ETH Zürich Zürich Switzerland SPS Master's in Data Science Online Program Northwestern University Evanston IL U.S.A.

ISBN: (数字)9781728110561

ISBN: (纸本)9781728110578

We report on computational experiments testing the robustness of scale-free networks. The stylized fact that such networks are robust under random failure but sensitive to targeted attack originates from experiments on instances generated by preferential attachment. We find that these are not representative but rather outliers: they are significantly more fragile under targeted attack than random scale-free networks with the exact same degree sequence. To show that they are, however, not extreme in this respect, we also present two generators producing scale-free networks with the same degree sequence that are even more fragile than the corresponding preferential-attachment networks or more robust than even random graphs. Additionally, we present a new result on Hamiltonian realizability of scaling degree sequences.

关键词： Analytical models Social networking (online) Computational modeling Robustness Generators Testing

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The Impact of Bike-Sharing Ridership on Air Quality: A Scalable data science Framework

The Impact of Bike-Sharing Ridership on Air Quality: A Scala...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Diane Woodbridge Nina Hua Victoria Suarez Rebecca Reilly Philip Trinh Paul Intrevado University of San Francisco Data Science Program University of San Francisco

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

This research explores the relationship between daily air quality indicator (AQI) values and the daily intensity of bike-share ridership in New York City. The authors designed and deployed a distributed data science framework on which to process and run Elastic Net, Random Forest Regression, and Gradient Boosted Regression Trees. Nine gigabytes of CitiBike ridership data, along with one gigabyte of air quality indicator (AQI) data were employed. All machine learning algorithms identified bike-share ridership intensity as either the most important or the second most important feature in predicting future daily AQIs. The authors also empirically demonstrated that although a distributed platform was necessary to ingest and pre-process the raw 10 gigabytes of data, the actual execution time of all three machine learning algorithms on cleaned, joined, and aggregated data was far faster on a local, commodity computer than on its distributed counterpart.

关键词： Air quality Distributed databases Servers Urban areas Cloud computing Vegetation Indexes

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Impact of COVID-19 on deaths from respiratory diseases: Panel data evidence from Chile

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Infection Ecology and Epidemiology 2022年第1期12卷 2023939页

作者： Barría-Sandoval, Claudia Ferreira, Guillermo Méndez, Angie Toffoletto, María Cecilia Nursing School Universidad de Las Américas Concepción Chile Faculty of Nursing Universidad de Concepción Concepción Chile Department of Statistics Universidad de Concepción Concepción Chile ANID - Millennium Science Initiative Program - Millennium Nucleus Center for the Discovery of Structures in Complex Data Santiago Chile Nursing School Universidad de Las Américas Santiago Chile

The COVID-19 pandemic has relegated pathologies that were previously commonplace to a secondary context. Therefore, it is necessary to study the evolution of these diseases in the presence or absence of COVID-19. Objective: The present study had the following objectives: 1. to evaluate the relationship between the COVID-19 epidemic and the possible decrease in death from respiratory disease (DRd) in Chile;and 2. to study the relationships between meteorological variables and severity of COVID-19 with respect to DRd from January 2018 to February 2021. Methods: The variable number of DRds in Chile was analyzed considering the monthly records of meteorological variables (temperature, precipitation and humidity) in each region of Chile and the severity of COVID-19 to evaluate the mortality trend before and after the pandemic. For this, different nonobservable heterogeneity models for panel data were used. Results: The variables that affect DRd include the number of deaths from COVID-19, which led to a decrease in DRd (negative effect) when increased, the number of patients with COVID-19 in the intensive care unit (ICU), which led to an increase in DRd (positive effect) when increased, and the minimum temperature, which had a negative effect on DRd. These results are supported by the application of panel regression with one-way random-effects models. Conclusion: This study revealed a reduction in the number of DRds other than COVID-19 during the pandemic in Chile. This could be explained by the sanitary measures applied by the Ministry of Health of Chile in relation to mobility restrictions and social distancing, among others. Therefore, DRd decreased in accordance with the appearance of the COVID-19 pandemic. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： panel data regression analysis Respiratory diseases SARS-CoV-2

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Scalable Real-time Prediction and Analysis of San Francisco Fire Department Response Times

Scalable Real-time Prediction and Analysis of San Francisco ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Xu Lian Sarah Melancon Jon-Ross Presta Adam Reevesman Brian Spiering Diane Woodbridge Data Science Program University of San Francisco University of San Francisco

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Predicting the San Francisco Fire Department (SFFD) emergency response time in real-time is critical for both callers and the SFFD. In this paper, we leverage machine learning and distributed computing to attempt a solution to this problem. While driving time can be estimated using the Google Maps API, there are many more factors that affect the response time. We obtained a publicly available SFFD call history dataset, using BigQuery, containing information about the location of the station, the location of the incident, and the time the call was received. We combined features from this dataset with driving time estimates from the Google Maps Distance Matrix API. Our dataset was over a gigabyte, calling for a distributed framework to efficient training of the machine learning models. We fit Linear Regression, Decision Tree Regression, and Random Forest Regression models in the Apache Spark machine learning library (MLlib) on an Amazon Web Services (AWS) Elastic MapReduce cluster. We benchmarked training time for each model on different cluster sizes.

关键词： Time factors Cloud computing Machine learning Medical services Google Linear regression Fires

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Channel-Annotated Deep Learning for Enhanced Interpretability in EEG-Based Seizure Detection

SSRN

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SSRN 2024年

作者： Wong, Sheng Fung Simmons, Anj Rivera-Villicana, Jessica Barnett, Scott Sivathamboo, Shobi Perucca, Piero Ge, Zongyuan Kwan, Patrick Kuhlmann, Levin O’Brien, Terence J. Applied Artificial Intelligence Institute Deakin University BurwoodVIC Australia School of Computing Technologies RMIT University Australia Department of Neuroscience Central Clinical School Monash University MelbourneVIC Australia Department of Neurology Alfred Health MelbourneVIC Australia Department of Medicine The Royal Melbourne Hospital The University of Melbourne ParkvilleVIC Australia Department of Neurology The Royal Melbourne Hospital ParkvilleVIC Australia Epilepsy Research Centre Department of Medicine Austin Health The University of Melbourne HeidelbergVIC Australia Bladin-Berkovic Comprehensive Epilepsy Program Austin Health HeidelbergVIC Australia Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia eResearch Centre

Currently, electroencephalogram (EEG) provides critical data to support a diagnosis of epilepsy through the identification of seizure events. The review process is undertaken by clinicians or EEG specialists and is labour-intensive, especially for longterm EEG recordings. Deep learning (DL) has been proposed to automate and expedite the seizure review and annotation process, providing superior performance when compared to traditional machine learning (ML) methods. However, DL algorithms lack interpretability which is a crucial factor for clinical adoption. Consequently, the "black-box" nature of these DL algorithms limits the transparency of these algorithms, preventing clinicians from having knowledge of how the predictions are derived. In this study, we propose a novel two-block seizure detection algorithm that leverages the channel-annotation of the EEG recordings in the TUH EEG Seizure Corpus (TUSZ) based on the likelihood of seizure activities on each channel. This method allows direct interpretation of the EEG segment without requiring any further interpretability or Explainable Artificial Intelligence (XAI) methods during the prediction phase. Further, we adopted an interpretable method for explaining decisions made by the seizure detection algorithms, identifying channels that influence the final predictions. This DL approach utilizing CNN, transformer and MLP achieved an AUC of 0.92 for the seizure detection task, comparable with other state-of-the-art algorithms. Our algorithm was further validated on a separate continuous EEG dataset achieving an AUC of 0.82. Additionally, we also evaluated the reliability and efficacy of our XAI method on predicted seizure events, achieving a sensitivity of 0.59 in accurately localizing channels with seizure activities. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Cholesterol allosteric modulation of the oxytocin receptor

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Biophysical Journal 2025年第12期124卷 1930-1943页

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity

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Translational Psychiatry 2023年第1期13卷 1-11页

作者： Smith, Sydney E. Kosik, Eena L. van Engen, Quirine Kohn, Jordan Hill, Aron T. Zomorrodi, Reza Blumberger, Daniel M. Daskalakis, Zafiris J. Hadas, Itay Voytek, Bradley Neurosciences Graduate Program University of California San Diego La Jolla CA United States Department of Cognitive Science University of California San Diego La Jolla CA United States Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Department of Psychiatry School of Medicine University of California San Diego La Jolla CA United States Cognitive Neuroscience Unit School of Psychology Deakin University Geelong VIC Australia Department of Psychiatry Central Clinical School Monash University Melbourne VIC Australia Temerty Centre for Therapeutic Brain Intervention Centre for Addiction and Mental Health University of Toronto Toronto ON Canada Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States Kavli Institute for Brain and Mind University of California San Diego La Jolla CA United States

Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical “slowing” of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST. © 2023, The Author(s).

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Machine learning applications for therapeutic tasks with genomics data

arXiv

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arXiv 2021年

作者： Huang, Kexin Xiao, Cao Glass, Lucas M. Critchlow, Cathy W. Gibson, Greg Sun, Jimeng Health Data Science Program Harvard University Boston02120 United States Analytics Center of Excellence IQVIA CambridgeMA02139 United States Center for Observational Research Amgen Thousand OaksCA91320 United States Center for Integrative Genomics Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science Carle's Illinois College of Medicine University of Illinois at Urbana-Champaign Urbana61820 United States

The genome contains instructions for building the function and structure, and guiding the evolution of molecules and organisms. Recent high-throughput techniques allow the generation of a vast amount of genomics data. However, the path of transforming genomics data into tangible therapeutics is filled with obstacles. We observe that genomics data alone are insufficient but rather require investigation of its interplay with data such as compounds, proteins, electronic health records, images, texts, etc. To make sense of these complex data, machine learning techniques are often utilized for identifying patterns and drawing insights from data. In this review, we study twenty-two genomics applications of machine learning that can enable faster and more efficacious therapeutic development, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. Challenges remain, including technical issues such as learning under different contexts given specific low resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

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Computational modeling of potential milciclib derivatives inhibitor-CDK2 binding through global docking and accelerated molecular dynamics simulations

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Informatics in Medicine Unlocked 2022年 33卷

作者： Khanam, Mushira Moin, Abu Tayab Ahmed, Kazi Ahsan Patil, Rajesh B. Ripon Khalipha, Abul Bashar Ahmed, Nafisa Bagchi, Rajat Ullah, Md Asad Ferdoush, Jannatul Islam, Saiful Rudra, Bashudev Department of Genetic Engineering and Biotechnology Faculty of Biological Sciences University of Chittagong Chattogram Bangladesh Department of Biochemistry and Molecular Biology Life Science Faculty Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Department of Biochemistry and Molecular Biology Faculty of Agriculture Bangladesh Agricultural University Mymensingh Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Pharmaceutical Chemistry Sinhgad Technical Education Society's Sinhgad College of Pharmacy Vadgaon (Bk) Maharashtra India Department of Pharmacy Life Science Faculty Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Department of Biotechnology and Genetic Engineering Faculty of Biological Sciences Jahangirnagar University Savar Dhaka Bangladesh Department of Pharmacology BGC Trust Medical College Chattogram Bangladesh Bangladesh Council of Scientific and Industrial Research (BCSIR) Chattogram Laboratories Chattogram Bangladesh

Hepatocellular carcinoma (HCC) is the most common malignant condition of the liver that occurs as a result of uncontrolled cellular proliferation after a series of disruptions at cell cycle regulatory checkpoints in the normal cell. Due to the lack of appropriate therapeutics or remedial treatment methods, new treatment strategies against HCC need to be developed. Cyclin dependent kinases (CDKs) are required to control the cell cycle and apoptosis, but their overexpressionis critical in the progression of cancer and is often expressed in HCC. Thus, CDKs are considered a promising class of target-defined therapy for HCC. Milciclib is a potential candidate for HCC which exhibits inhibitory activity against CDK2 leading to cell cycle arrest and apoptosis of tumor cells. Herein, we have halogenated the parent drug milciclib to improve its efficacy against CDK2. The primary structure of milciclib (D) was modified with F, Cl and,CF₃ groups. The frontier molecular orbital features, binding affinity, non-bonded interaction and the pharmacokinetic parameters were analyzed for milciclib and its derivatives. We also performed molecular docking and extended molecular dynamics simulation studies to study the binding interactions and binding affinity more closely. Our computational investigation showed the derivatives D-F and D-CF₃ have significant chemical reactivity, the best binding affinity, nonbonding interactions, and improved pharmacokinetic properties compared to the parent drug milciclib. Molecular dynamics analysis and MM-PBSA calculations indicated that D-Cl had a slightly more stable conformation and higher binding affinity compared to D-CF₃. © 2022 The Authors

关键词： Anticancer drug Halogenated derivatives Hepatocellular carcinoma Milciclib Molecular docking Molecular dynamics simulation Non-bond interactions

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Asymmetric partisan voter turnout games

arXiv

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arXiv 2020年

作者： Guage, Cameron Fu, Feng Department of Mathematics Dartmouth College Hanover NH03755 United States Program in Quantitative Social Science Dartmouth College Hanover NH03755 United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth LebanonNH03756 United States

Since Downs proposed that the act of voting is irrational in 1957, myriad models have been proposed to explain voting and account for observed turnout patterns. We propose a model in which partisans consider both the instrumental and expressive benefits of their vote when deciding whether or not to abstain in an election, introducing an asymmetry that most other models do not consider. Allowing learning processes within our electorate, we analyze what turnout states are rationalizable under various conditions. Our model predicts comparative statics that are consistent with voter behavior. Furthermore, relaxing some of our preliminary assumptions eliminates some of the discrepancies between our model and empirical voter behavior. Copyright © 2020, The Authors. All rights reserved.

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