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Clinical Informatics Approaches to Facilitate Cancer data Sharing

Yearbook of Medical Informatics 2023年第1期32卷 104-110页

作者： Aneja, Sanjay Avesta, Arman Xu, Hua Machado, Lucila Ohno Department of Therapeutic Radiology Yale School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation at Yale New Haven CT United States Department of Bioinformatics and Data Science Yale School of Medicine New Haven CT United States

OBJECTIVES: Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. METHODS: We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. RESULTS: Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. CONCLUSIONS: Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercia

关键词： cancer common data models data sharing encryption federated learning

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Pathway-Based Analysis Using SVM-RFE for Gene Selection and Classification 7th

Pathway-Based Analysis Using SVM-RFE for Gene Selection and ...

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7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023

作者： Rahman, Nurazreen Afiqah A. Nasarudin, Nurul Athirah Mohamad, Mohd Saberi Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medicine and Health Science United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819990177

The pathway-based analysis is one method for selecting and classifying genes by incorporating pathway information. Integration of pathway knowledge into microarray data significantly advances researchers in the analysis of complex diseases. Microarray data involves thousands of genes to be selected, and therefore, a suitable method to eliminate noisy and uninformative genes is needed. Selecting significant genes for a specific disease is crucial to identifying genes highly related to disease production. Previous research shows that using both pathway information and gene expression data is more significant in disease identification. Therefore, pathway-based analysis using Support Vector Machine Recursive Feature Elimination (SVM-RFE) is introduced in this research to identify significant genes associated with analyzing the targeted phenotype. The datasets involved in this research are lung cancer and gender dataset. The results from the proposed method performed better than previous work as the significant genes are selected from the highest rank of genes in the highest rank of the pathway. The performance of the proposed method was evaluated using 10-fold cross-validation in terms of accuracy. Finally, a biological validation was conducted on selected genes in the top 5 pathways based on biological literature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gene expression

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Digital Twin (DT) Smart City for Air Quality Management 8

Digital Twin (DT) Smart City for Air Quality Management

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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2024 IEEE International Conference on data and Software Engineering, ICoDSE 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Kurnianingrum, Dian Ratnapuri, Chyntia Ika Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331506407

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus. © 2024 IEEE.

关键词： Decoding

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data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors 7

Data mining for epidemiology: The correlation of typhoid fev...

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7th International Conference on Computer science and Computational Intelligence, CSCI 2022

作者： Asadi, Faisal Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： data mining

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Online performative gradient descent for learning nash equilibria in decision-dependent games 23

Online performative gradient descent for learning nash equil...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zihan Zhu Ethan X. Fang Zhuoran Yang Department of Statistical Science Duke University Department of Biostatistics and Bioinformatics Duke University Department of Statistics and Data Science Yale University

We study multi-agent games within the innovative framework of decision-dependent games, which establishes a feedback mechanism that population data reacts to agents' actions and further characterizes the strategic interactions among agents. We focus on finding the Nash equilibrium of decision-dependent games in the bandit feedback setting. However, since agents are strategically coupled, classical gradient-based methods are infeasible without the gradient oracle. To overcome this challenge, we model the strategic interactions by a general parametric model and propose a novel online algorithm, Online Performative Gradient Descent (OPGD), which leverages the ideas of online stochastic approximation and projected gradient descent to learn the Nash equilibrium in the context of function approximation for the unknown gradient. In particular, under mild assumptions on the function classes defined in the parametric model, we prove that the OPGD algorithm finds the Nash equilibrium efficiently for strongly monotone decision-dependent games. Synthetic numerical experiments validate our theory.

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Model of learning management system based on artificial intelligence in team-based learning framework 6

Model of learning management system based on artificial inte...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Pardamean, Bens Suparyanto, Teddy Cenggoro, Tjeng Wawan Sudigyo, Digdo Anugrahana, Andri Anugraheni, Indri Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Sanata Dharma University Elementary School Teacher Education Yogyakarta Indonesia Satya Wacana Christian University Elementary School Teacher Education Salatiga Indonesia

ISBN: (纸本)9781665449373

The presence of various innovations and technology of Artificial Intelligence (AI) has now begun to be adopted by Indonesia in many aspects including in the industrial sector. The utilization of AI also can offer a new paradigm in learning and teaching strategies. The development of learning strategies continues also performed to obtain the variations of learning that involve student activity. Team-Based Learning (TBL) is one of the teaching strategies that can improve the quality of the learning process and student activity in groups. By starting to integrate digital-based learning resources in the learning and teaching process, the TBL strategy can potentially be an alternative to new learning strategies that are relevant to be applied in the era of the educational revolution 4.0. The main focus of this research is to develop and evaluate the application of AI in a digital-based TBL strategy that will be implemented in the form of personalized learning about student learning styles. This research was developed based on the learning strategy development of the research method. However, this article more focuses on developing the AI application design that will be applied. The application design was developed based on the agile development method, especially SCRUM. The result of this study is an additional feature that was implemented on a Learning Management System (LMS) based on the Modular Object-Oriented Dynamic Learning Environment (MOODLE). © 2021 IEEE.

关键词： Students

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Design of Water Information Management System in Palm Oil Plantation 1

Design of Water Information Management System in Palm Oil Pl...

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1st International Conference on Computer science and Artificial Intelligence, ICCSAI 2021

作者： Krisdiarto, Andreas Wahyu Julianto, Eddy Wisnubhadra, Irya Suparyanto, Teddy Sudigyo, Digdo Pardamean, Bens Institute of Agriculture STIPER Faculty of Agricultural Technology Yogyakarta55282 Indonesia Atma Jaya Yogyakarta University Faculty of Industrial Engineering Yogyakarta55281 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

The water level on peatlands is a critical factor in the production of oil palm plantations on peatlands because oil palm requires water but should not be inundated. The optimal water depth from the surface should be controlled from 70 to 80 cm by opening or closing the drain gate. Currently, most measurements are made with a piezometer. Then the opening and closing of the sluice gate at the end of the primary channel are done manually. In many cases, the distance between the plantation block and the floodgate is quite far and is limited by inaccessible infrastructure (roads), so opening or closing take a lot of time and money. Currently, a water level measurement system has been designed automatically using a microcontroller. This study aims to develop a design of a drainage management information system in oil palm plantations. The information system includes the water level of peatlands data in oil palm plantations taken from the water level sensor in the drainage system. The system uses the data from the water level sensor to manage the drainage system's sluice gates. This system was developed using the SDLC waterfall method. The final result of this research was a design of a water information management system that can regulates automatic and real-time peatland oil palm plantations water level. © 2021 IEEE.

关键词： Water levels

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Selection for Cheaper Amino Acids Drives Nucleotide Usage at the Start of Translation in Eukaryotic Genes

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Genomics, Proteomics & bioinformatics 2021年第6期19卷 949-957页

作者： Na L.Gao Zilong He Qianhui Zhu Puzi Jiang Songnian Hu Wei-Hua Chen Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute for Computer Science and Cluster of Excellence on Plant Sciences Heinrich Heine UniversityD-40225 DuesseldorfGermany CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and EngineeringBeihang UniversityBeijing 100191China University of Chinese Academy of Sciences Beijing 100049China

Coding regions have complex interactions among multiple selective forces,which are manifested as biases in nucleotide *** studies have revealed a decreasing GC gradient from the 5′-end to 3′-end of coding regions in various *** confirmed that this gradient is universal in eukaryotic genes,but the decrease only starts from the~25th *** trend is mostly found in nonsynonymous(ns)sites at which the GC gradient is universal across the eukaryotic *** GC contents at ns sites result in cheaper amino acids,indicating a universal selection for energy efficiency toward the N-termini of encoded *** a genome,the decreasing GC gradient is intensified from lowly to highly expressed genes(more and more protein products),further supporting this *** reveals a conserved selective constraint for cheaper amino acids at the translation start that drives the increased GC contents at ns *** GC contents can facilitate transcription but result in a more stable local secondary structure around the start codon and subsequently impede translation ***,the GC gradients at four-fold and two-fold synonymous sites vary across *** could decrease or increase,suggesting different constraints acting at the GC contents of different codon sites in different *** study reveals that the overall GC contents at the translation start are consequences of complex interactions among several major biological processes that shape the nucleotide sequences,especially efficient energy usage.

关键词： Macroevolution Prioritization of selective forces Energy efficiency Transcription Translation initiation

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data annotation system for intelligent energy conservator in smart building 3

Data annotation system for intelligent energy conservator in...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Chandra, Bloomest Jansen Rahutomo, Reza Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department College of Letters and Science University of Wisconsin MadisonWI53706 United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Historic preservation

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