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LDA Topic Modeling for bioinformatics Terms in arXiv Documents

Procedia Computer Science 2024年 245卷 229-238页

作者： Andrea Stevens Karnyoto Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "Computer Science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology; thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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Semi-automated meningioma segmentation with bounding boxes

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Procedia Computer Science 2024年 245卷 583-590页

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores.

关键词： Bounding boxes Meningioma Semi-automated segmentation

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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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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens 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 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

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

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael 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 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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Machine Learning Approach for Single Nucleotide Polymorphism Selection in Genetic Testing Results 8

Machine Learning Approach for Single Nucleotide Polymorphism...

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

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Isnan, Mahmud Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Lactose intolerance is a type of digestive problem that may threaten the population because milk and dairy products compose of nutrients that are essential for human body. Genetic tests possess a great potential to detect lactose intolerance as it can be used in children and even infants. However, a new approach to analyze the genetic test results is needed to elucidate the Single Nucleotide Polymorphisms (SNPs) that are related to lactose intolerance. In this work, we utilized the machine learning based feature selection to select the SNPs associated with lactose tolerance trait from genotyping samples of direct-to-customer (DTCG genetic tests, obtained from the public database. Recursive Feature Elimination (RFE) with XGBoost model was used to perform feature selection. We also compared three different models, such as XGBoost, support vector machine (SVM), and random forest (RF) for training the selected features. Our findings revealed that 20 SNPs (out of 3501) were chosen, with rs4394668 as the most important variables (F-score 0.009). Furthermore, when compared to the RF and SVM models, the XGBoost model had the highest accuracy (0.87). Further studies should be undertaken to elucidate how the selected SNPs may lead to the lactose intolerance trait. © 2023 The Authors. Published by Elsevier B.V.

关键词： feature selection genetic test lactose intolerance SNP

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Database Design for Indonesian Scholarship Recommender Systems 7

Database Design for Indonesian Scholarship Recommender Syste...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Elwirehardja, Gregorius Natanael Jason Dominic, Nicholas Pardamean, Bens BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Following the evolution of technology, researchers have conducted several studies to propose reliable scholarship recommender systems. However, few have explored the data storage systems, which are highly beneficial to collect huge volumes of real data in training the recommender systems. In this paper, a database design for collecting such data was proposed, equipped with integer encodings for categorical variables and a method to normalize variables with varying ranges. Using Connoly and Begg's database design method, the final design consisted of a normalized Entity Relationship Diagram (ERD) and data dictionaries. The design was also constructed to support usages of various data for determining scholarship recipients, including education history, achievements, and organizational experiences. The proposed design can be implemented on information systems to allow easier information access for scholarships in Indonesia. © 2022 IEEE.

关键词： Recommender systems

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SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

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IEEE Transactions on Consumer Electronics 2023年第3期69卷 250-260页

作者： Chen, Tsai-Min Tsai, Yuan-Hong Tseng, Huan-Hsin Liu, Kai-Chun Chen, Jhih-Yu Huang, Chih-Han Li, Guo-Yuan Shen, Chun-Yen Tsao, Yu National Taiwan University and Academia Sinica Graduate Program of Data Science Taipei106319 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan Taiwan Artificial Intelligence Academy Foundation Program of Taiwan AI Academy New Taipei22065 Taiwan Artificial Intelligence Foundation Technology Development Center New Taipei24158 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan National Taiwan University Department of Mathematics Taipei10617 Taiwan

A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances. © 1975-2011 IEEE.

关键词： Electrocardiography

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Evaluating the Effectiveness of Transformer Models for Molecular Property Prediction: A Review

Evaluating the Effectiveness of Transformer Models for Molec...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Alyssa Imani Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Transformer models, originally successful in natural language processing, are now being applied to chemical and biological studies, excelling in areas such as molecular property prediction, material science, and drug discovery. BERT, a Transformer-based model, has become foundational in cheminformatics, particularly for QSAR (Quantitative Structure-Activity Relationship) modeling and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluations in drug discovery. However, achieving higher accuracy often requires designing more complex models, which can compromise their interpretability. This posing a challenge for researchers who need to understand the reasoning behind the predictions. The trade-off between accuracy and interpretability presents a critical challenge in applying black box models to real-world problems in cheminformatics. This work compares Transformer-based models with traditional machine learning and deep learning approaches, focusing on both interpretability and performance. The goal is to highlight the strengths and limitations of each method, offering insights into their optimal use in drug discovery and material science.

关键词： Accuracy Toxicology Reviews Biological system modeling Predictive models Transformers Natural language processing Drug discovery Metabolism Information technology

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Web Information System Design for Fast Protein Post-Translational Modification Site Prediction 7

Web Information System Design for Fast Protein Post-Translat...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Elwirehardja, Gregorius Natanael Dominic, Nicholas Pardamean, Bens BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Research Center Bina Nusantara University Bioinformatics and Data Science Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

In the field of bioinformatics, the protein Post-Translational Modification (PTM) site prediction has been widely studied and Web Information Systems (WIS) has been deployed by researchers for this task. Through a literature review and benchmarking process, we identified the requirements which included quick predictions, efficient memory usage, and input validations. However, no detailed designs have been proposed so far, which may have contributed to some requirements not being implemented in some of the websites. Therefore, we propose a detailed WIS conceptual design, which can be used for predicting the sites of multiple PTM types, equipped with a validation algorithm and compared the usage of various string searching algorithms as well as file storage formats. Experiment results showed that the linear search algorithm is the fastest for this task and storing the protein data in npz format when performing multi-PTMs site prediction can assist in reducing memory usage. The proposed design can be implemented into user-friendly web tools that are both efficient in speed and memory usage in future studies. © 2022 IEEE.

关键词： Forecasting

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