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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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CollaboNet: Collaboration of deep neural networks for biomedical named entity recognition

arXiv

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arXiv 2018年

作者： Yoon, Wonjin So, Chan Ho Lee, Jinhyuk Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. Results: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. Conclusions: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

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

作者： Hidayat, Alam Ahmad Budiarto, Arif Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia 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

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

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Improvement of Jakarta's air quality during large scale social restriction 2

Improvement of Jakarta's air quality during large scale soci...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Rahutomo, R. Purwandari, K. Sigalingging, J.W.C. Pardamean, B. Information System 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 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather Data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Diversity and Evolution of RNA Recognition Motifs: A Case Study on the Utility of RdRp in the phylum Duplornaviricota

Diversity and Evolution of RNA Recognition Motifs: A Case St...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mikyung Je Myeongji Cho Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Lab. of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy.

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Interactions between species introduce spurious associations in microbiome studies

arXiv

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arXiv 2017年

作者： Menon, Rajita Ramanan, Vivek Korolev, Kirill S. Department of Physics Boston University BostonMA United States BRITE Bioinformatics REU Program Boston University BostonMA United States Graduate Program in Bioinformatics Boston University BostonMA United States Department of Biology and Computer Science Swarthmore College SwarthmorePA United States

Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease. Copyright © 2017, The Authors. All rights reserved.

关键词： Association reactions

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Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

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Journal of Pharmaceutical Analysis 2023年第7期13卷 691-693页

作者： Dan Li Zhou Fang Qiang Shi Nicholas Zhang Binsheng Gong Weida Tong Ahmet F.Coskun Joshua Xu Division of Bioinformatics and Biostatistics National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGA30332USA Machine Learning Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Division of Systems Biology National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Interdisciplinary Bioengineering Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA

Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.

关键词： money utilizing testing

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SPLITS: A new program for predicting split and intron-containing tRNA genes at the genome level

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In Silico Biology 2006年第5期6卷 411-418页

作者： Sugahara, Junichi Yachie, Nozomu Sekine, Yasuhiko Soma, Akiko Matsui, Motomu Tomita, Masaru Kanai, Akio Institute for Advanced Biosciences Keio University Tsuruoka 997-0017 Japan Department of Environmental Information Keio University Fujisawa 252-8520 Japan Bioinformatics Program Graduate School of Media and Governance Keio University Fujisawa 252-8520 Japan Department of Life Science College of Science Rikkyo University Toshima-ku Tokyo 171-8501 3-34-1 Nishiikebukuro Japan

In the archaea, some tRNA precursors contain intron(s) not only in the anticodon loop region but also in diverse sites of the gene (intron-containing tRNA or cis-spliced tRNA). The parasite Nanoarchaeum equitans, a member of the Nanoarchaeota kingdom, creates functional tRNA from separate genes, one encoding the 5'-half and the other the 3'-half (split tRNA or trans-spliced tRNA). Although recent genome projects have revealed a huge amount of nucleotide sequence data in the archaea, a comprehensive methodology for intron-containing and split tRNA searching is yet to be established. We therefore developed SPLITS, which is aimed at searching for any type of tRNA gene and is especially focused on intron-containing tRNAs or split tRNAs at the genome level. SPLITS initially predicts the bulge-helix-bulge splicing motif (a well-known, required structure in archaeal pre-tRNA introns) to determine and remove the intronic regions of tRNA genes. The intron-removed DNA sequences are automatically queried to tRNAscan-SE. SPLITS can predict known tRNAs with single introns located at unconventional sites on the genes (100%, tRNAs with double introns (85.7%), and known split tRNAs (100%). Our program will be very useful for identifying novel tRNA genes after completion of genome projects. The SPLITS source code is freely downloadable at http://***/. © 2006 IOS Press. All rights reserved.

关键词： Bulge-helix-bulge splicing motif Genome analysis Intron-containing tRNA Software Split tRNA tRNAscan-SE

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