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Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

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Yearbook of Medical informatics 2022年第1期31卷 106-115页

作者： Tang, Alice Woldemariam, Sarah Roger, Jacquelyn Sirota, Marina Bakar Computational Health Sciences Institute UCSF San Francisco CA United States Graduate Program in Bioengineering UCSF San Francisco CA United States School of Medicine UCSF San Francisco CA United States Graduate Program in Biological and Medical Informatics UCSF San Francisco CA United States Department of Pediatrics UCSF San Francisco CA United States

Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion. Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years. Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine. Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all. © 2022 IMIA and Georg Thieme Verlag KG.

关键词： Bioinformatics genomics health equity precision medicine

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KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context

arXiv

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arXiv 2024年

作者： Wang, Xiaonan Yeo, Jinyoung Lim, Joon-Ho Kim, Hansaem Interdisciplinary Graduate Program of Linguistics and Informatics Yonsei University Korea Republic of Department of Artificial Intelligence Yonsei University Korea Republic of Tutorus Labs Korea Republic of

Large language models (LLMs) have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture. Copyright © 2024, The Authors. All rights reserved.

关键词： Translation (languages)

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Peak Prediction in Time Series: Comparing Approaches for Energy High-Load Prediction

Peak Prediction in Time Series: Comparing Approaches for Ene...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rodrigo Kruger Abdullah Mueen Vinicius M. A. Souza Graduate Program in Informatics Pontifícia Universidade Católica do Paraná (PPGIa/PUCPR) Curitiba Brazil The University of New Mexico (UNM) Albuquerque USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Peaks in time series represent significant events in the measurements of a phenomenon over time, such as a sudden increase in the sales of an item on a specific day of the week. Predicting peaks in advance can support decision-making in various domains. For example, predicting demand for healthcare services helps adjust nurses’ work schedules in a hospital. This paper proposes a time series mining task named peak prediction, from which most applications that manage resources based on expected demand can benefit. We investigate various approaches, such as conventional machine learning and deep learning methods, to build global and individual models for peak prediction in time series. We evaluate these approaches in a load demand problem on smart meter energy consumption. In this task, a model predicts the customer’s maximum daily energy consumption in the following days (7 days ahead) and identifies which day it will occur. Accurate peak demand predictions for the consumers in a region help utility companies make informed decisions regarding capacity planning, load balancing, and integrating renewable energy sources. Since we are interested in two target variables simultaneously (i.e., peak magnitude and peak position), we evaluated whether multi-target approaches improve single-target models’ performance by correlating both targets in the learning process. Our experimental evaluation considers a dataset comprising three years of daily energy consumption from 1,757 customers. The results show the potential of individual machine learning models induced by simple algorithms such as Linear Regression and XGBoost and multi-target methods’ lack of contribution (or even negative impact) for the performance.

关键词： Deep learning Energy consumption Machine learning algorithms Time series analysis Linear regression Predictive models Prediction algorithms

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology, IConNECT 2023

作者： Sirait, Tamsir Hasudungan Lumban Gaol, Ford Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology. © 2023 IEEE.

关键词： Information management

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Finding the Important Elements Linked to Integration Problems in the Use of Enterprise Resource Planning Systems 9

Finding the Important Elements Linked to Integration Problem...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Sardjono, Wahyu Kristin, Desi Maya Pranata, Dewi Sagita Bina Nusantara University BINUS Graduate Program Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia King's College London Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences Department of Informatics WC2R 2LS United Kingdom

ISBN: (纸本)9798350390025

Organizations face an urgent need to become faster, more efficient, and more successful in their commercial operations, given the current state of technological evolution and the arrival of Industry 4.0. Many businesses have adopted the idea of enterprise resource planning (ERP) systems to achieve this goal. The ERP system is expected to increase worker productivity, improve business performance to maximize profitability and profits, and provide accurate and up-to-date data and information to management and stakeholders. This research aims to find new variables related to integration problems in the use of ERP systems. Using the SPSS statistical program, the data collected will be examined using quantitative exploratory factor analysis (EFA). Data processing then includes collecting demographic information from respondents, reliability testing, conducting the Kaiser-Meyer-Olkin (KMO) test, testing using an anti-image matrix, and deriving new factors using a rotation component matrix. This research seeks to identify new elements underlying ongoing integration problems in ERP system implementation. The research results show several reasons for underlying integration problems and offer in-depth information to management that can be used to analyze and evaluate the ERP system in their company appropriately in the future so that the company's performance becomes superior to the competition. © 2024 IEEE.

关键词： Profitability

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Evaluating an e-Learning Platform at graduate School Based on User Experience Evaluation Technique 3

Evaluating an e-Learning Platform at Graduate School Based o...

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3rd International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2020

作者： Abubakari, M.S. Nurkhamid Hungilo, G. Department of Electronics Engineering Education Postgraduate Program Universitas Negeri Yogyakarta Indonesia Department of Informatics Engineering Graduate Program University Atma Jaya Yogyakarta Indonesia

This study was aimed to evaluate the e-learning platform in a graduate school by using User Experience Questionnaire (UEQ). The data were collected from 65 respondents and analyzed using an excel data analysis tool. The results indicate that all six scales of the instrument are consistent except the novelty scale. Also, the calculated means of the scales are slightly positive as all values are greater than 0.8 compared to the UEQ benchmark. Although the respondents show a little positive impression with the platform, all scales means are below average category except efficiency scale which is above average as compared to the benchmark. This indicates that the platform needs more improvement in all aspects of user experience for it to be at least in a good category. The findings discussed in this paper are valuable for all academic stakeholders in various educational institutions which implement interactive e-learning systems as well as practitioners of user experience and developers of e-learning systems. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Learning systems

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A Machine Learning Application for Smart Government: Dashboards for Effective Taxation of Property Taxes

A Machine Learning Application for Smart Government: Dashboa...

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IEEE International Smart Cities Conference (ISC2)

作者： Lara S. Furtado Virginia Queiroz Cecília Carvalho Filipe F. Damasceno Antonio A. F. Oliveira Vladia Pinheiro Masters in City Science University of Fortaleza Fortaleza Brazil Graduate Program of Applied Informatics University of Fortaleza Fortaleza Brazil Secretariat of Finance of the Municipality of Fortaleza Fortaleza Brazil

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

Smart Government initiatives aim to enhance governmental services by strategically applying digital technologies. Property tax assessment, a crucial aspect of government revenue, often faces challenges due to outdated methods and complex evaluation models. The procedures to estimate property values are time consuming since mass estimation must evaluate dozens of building features, some of which may not completely influence market values. For government appraisers, making sense of millions of spatial data is cumbersome and prone to error. This paper proposes a comprehensive framework integrating Machine Learning (ML), dashboards, and Geographic Information Systems to address these challenges. By leveraging ML algorithms, interactive dashboards, and geospatial data, tax agencies can streamline property assessment processes, improve accuracy, and facilitate real-time decision-making. A case study in Fortaleza, Brazil, demonstrates the framework's effectiveness in enhancing land value assessment and property taxation within the Smart Governance context. This paper provides insights into the benefits and challenges of implementing such a framework and underscores its potential to revolutionize property tax assessment practices.

关键词： Machine learning algorithms Smart cities Government Evaluation models Finance Machine learning Spatial databases Real-time systems Geospatial analysis Faces

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Systematic Mapping Review on Log Preparation for Process Mining

Systematic Mapping Review on Log Preparation for Process Min...

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International Conference on Computer Supported Cooperative Work in Design

作者： Luiz Fernando Puttow Southier Sheila Cristiana de Freitas Adriano Pizzini Eduardo Alves Portela Santos Edson Emilio Scalabrin Graduate Program in Informatics (PPGIa) Pontifícia Universidade Catôlica do Paraná Curitiba Brazil CEPPAD - UFPR Curitiba Brazil

Process Mining (PM) is a research discipline that helps organizations track and optimize processes to support their business. Further, it focuses on providing process analysis techniques and tools, and several of its applications have been described in the literature. The start point for PM is using event logs generated by information systems to analyze processes. These event logs need to be extracted from databases and prepared for use because the quality of the event logs used as input is critical to the success of any PM effort. In this article, we present a systematic mapping review to provide the reader with highlights of the state-of-the-art techniques for event log preparation. Based on the retrieved studies, we identified six main categories of log preparation techniques: extraction, cleaning, repair, non-adequate granularity, quality evaluation, and privacy. The results are explored quantitatively and qualitatively. All results are made available through spreadsheets and charts. We believe this paper is a starting point for researchers to identify the studies that would help them prepare event logs for PM.

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Evaluation of Various Transformer Field Conditions Using Cross-Combined Frequency Response Analysis to Identify Specific Deformity 4

Evaluation of Various Transformer Field Conditions Using Cro...

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4th International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2023

作者： Sutardi, Bagas Maulana Wulung, Ario Prakoso, Muhammad Helmi Pudiatmoko, Ary Abd Azis Reskiawan, M. Khayam, Umar Suwarno Pln Corporate University Graduate Program PLN-ITB Jakarta Indonesia Transformation and Ceo Office Pln Head Office Jakarta Indonesia UP3B Kalbar Pln UIP3BK Pontianak Indonesia School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350318678

In HV equipment, the most critical and valuable components used is transformers. Power transformers should go through standard diagnostic tests to identify deterioration or to avoid the emergence of defects that might possibly cause severe failure. Frequency Response Analysis already proven to be a promising tool in diagnostic techniques that make use of this understanding to find such changes. FRA has so far been used to detect deformation in transformers by observing changes in the frequency response curve, despite a limited understanding of what caused the changes in the initial case. Several FRA methods have been implemented recently all around the world, primarily for transformer deformation detection and diagnosis. Nonetheless, there is now an increasing amount of research focused on determining specifically the type and degree of deformation in a transformer by looking at its FRA response curve. It is very important to refer to the guidance given in the recently published IEC, IEEE, SPLN, and CIGRE standards to ensure the accuracy of the analysis of the frequency response results. An approach for transformer diagnostics is discussed in this research by doing a comparative analysis with four study cases related to various physical phenomena and examining the cross-combined between standards and field FRA results. Four FRA measurement cases are taken from the experience in PLN, namely: Tayan (water contamination), Sanggau (transformers relocation), Sambas (rebuilding transformer foundation), and Cianjur (earthquake). The evaluation results indicate that the IEEE and IEC diagnostic standards have an assessment that is closest to the actual condition that is influenced by variations in wide band frequency measurement. © 2023 IEEE.

关键词： Power transformers

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