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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017–2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years

关键词： Text mining Heuristic algorithms Multimedia systems Time series analysis Sociology Semantics Pipelines

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Multiple Imputation Method for High-Dimensional Neuroimaging data

arXiv

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arXiv 2023年

作者： Lu, Tong Chen, Chixiang Huang, Hsin-Hsiung Kochunov, Peter Hong, Elliot Chen, Shuo Department of Mathematics University of Maryland College Park United States Division of Biostatistics and Bioinformatics School of Medicine University of Maryland United States Department of Statistics and Data Science University of Central Florida United States Department of Psychiatry and Behavioral Science University of Texas Health Science Center United States Maryland Psychiatric Research Center School of Medicine University of Maryland United States

Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is therefore crucial to conduct appropriate statistical methods to address this issue. While multiple imputation is a popular technique for handling missing data, its application to neuroimaging data is hindered by high dimensionality and complex dependence structures of multivariate neuroimaging variables. To tackle this challenge, we propose a novel approach, named High dimensional Multiple Imputation (HIMA), based on Bayesian models. HIMA develops a new computational strategy for sampling large covariance matrices based on a robustly estimated posterior mode, which drastically enhances computational efficiency and numerical stability. To assess the effectiveness of HIMA, we conducted extensive simulation studies and real-data analysis using neuroimaging data from a Schizophrenia study. HIMA showcases a computational efficiency improvement of over 2000 times when compared to traditional approaches, while also producing imputed datasets with improved precision and stability. © 2023, CC BY.

关键词： Covariance matrix

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LSD 4.0:an improved database for comparative studies of leaf senescence

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Molecular Horticulture 2022年第1期2卷 303-306页

作者： Jie Cao Yang Zhang Shuya Tan Qi Yang Hou‑Ling Wang Xinli Xia Jingchu Luo Hongwei Guo Zhang Zhang Zhonghai Li National Engineering Research Center for Tree Breeding and Ecological Restoration College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijing 100083China National Genomics Data Center Beijing 100101China China National Center for Bioinformation Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China College of Life Sciences Peking UniversityBeijing 100871China Center for Bioinformatics Peking UniversityBeijing 100871China Department of Biology Institute of Plant and Food ScienceSouthern University of Science and Technology(SUSTech)Shenzhen 518055GuangdongChina

Leaf senescence is the final stage of leaf development and involves the active degradation and dynamic transfer of its cellular components to newly growing and storage tissues,which contributes to plant fitness(Gan and Amasino,1997;Lim et al.,2007).The genetic modification of leaf senescence has emerged as a promising strategy for improving nutritional traits and stress tolerance in plants(Rivero et al.,2007).Efforts to dissect the molecular mechanisms underpinning leaf senescence reveal that it is a highly coordinated process regulated by a large number of senescence-associated genes(SAGs)(Guo and Gan,2005;Lim et al.,2007).Functional studies of these SAG genes through reverse genetics strategies and identification of senescence-altered mutants through forward genetic screening have deepened the understanding of leaf senescence(Guo et al.,2021).

关键词： traits senescence emerged

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152P Nanopore sequencing of cfDNA captures key copy number alterations in lung adenocarcinoma: A non-invasive approach for prognosis, therapy, and subtyping

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ESMO Open 2024年 9卷 103891-103891页

作者： K. Roohollahi E. Post D. Vessies J. van den Berg D. Makarawung F. van Asch Renovaro Cube Amsterdam Netherlands Bioinformatics Renovaro Cube Amsterdam Netherlands Data Science Renovaro Cube Amsterdam Netherlands Oncology Dept. Renovaro Cube Amsterdam Netherlands

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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A Novel Method for Quantitative Evaluation of Anti-Infection Immune Defense Function and its Application

SSRN

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SSRN 2024年

作者： Tan, Zhen-Lin Luo, Tao Wu, Xiao-Jun Lei, Qian Chen, Jie Guo, An-Yuan Department of Laboratory Medicine West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Hubei Key Laboratory of Bioinformatics and Molecular-imaging College of Life Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China College of Life Sciences University of Chinese Academy of Sciences Beijing101408 China BGI Education Center University of Chinese Academy of Sciences Shenzhen518083 China

ImmuDef is a novel algorithm for precise, quantitative assessment of anti-infection immune defense based on RNA-seq data. Using enrichment scores of immunologic signature gene sets, we defined three feature sets for bacteria, viruses, and fungi infection. Three normalized scores based on modified Manhattan distances were defined as immune scores (IS) to evaluate immunity against these infections. Tested on 3,202 samples across 39 datasets, ImmuDef distinguished well between healthy individuals and patients with AIDS, COVID-19, sepsis, tuberculosis (AUC: 0.71-1.00). It accurately predicted survival and death outcomes in critical sepsis (AUC = 0.89) and severe COVID-19 (AUC = 0.84). In hepatitis B patients, IS was significantly lower in those with active HBV replication and negatively correlated with ALT levels. ImmuDef offers a novel approach for precise measurement of anti-infection immunity, aiding in disease prognosis prediction and individual health status ***: Financial support was provided by the National Natural Science Foundation of China (32370717) and the 1.3.5 project for disciplines of excellence from West China Hospital of Sichuan University (ZYYC23007).Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this *** Approval: This study was approved by Ethics Committee on Biomedical Research West China Hospital of Sichuan University (Reference: HX-IRB-AF-21-V3.0). © 2024, The Authors. All rights reserved.

关键词： COVID-19

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database Design for Pre-Eclampsia Case Administration

Database Design for Pre-Eclampsia Case Administration

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

作者： Faisal Asadi Gregorius Natanael Elwirehardja Joko Pebrianto Trinugroho Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia.

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scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data (vol 15, 1629, 2024)

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NATURE COMMUNICATIONS 2024年第1期15卷 1629页

作者： Tang, Songming Cui, Xuejian Wang, Rongxiang Li, Sijie Li, Siyu Huang, Xin Chen, Shengquan Nankai University School of Mathematical Sciences and LPMC Tianjin China (GRID:grid.216938.7) (ISNI:0000 0000 9878 7032) Tsinghua University MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST Department of Automation Beijing China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) University of Virginia Department of Computer Science Charlottesville USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X) Nankai University School of Statistics and Data Science Tianjin China (GRID:grid.216938.7) (ISNI:0000 0000 9878 7032) Beijing Institute of Radiation Medicine Beijing Key Laboratory for Radiobiology Department of Radiation Biology Beijing China (GRID:grid.506261.6) (ISNI:0000 0001 0706 7839)

Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement ***-cell chromatin accessibility sequencing (scCAS) data suffers from high sparsity and dimensionality. Here, authors propose an accurate and interpretable computational framework for enhancing scCAS data that considers cell-to-cell similarity.

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Meta-analysis of Traditional Chinese Medicine Lianhua Qingwen in the Treatment of Coronavirus Disease 2019

Journal of anesthesia and translational medicine

引用

Journal of anesthesia and translational medicine 2023年第2期2卷 20-26页

作者： Qian Xu Kaicheng Song Sean P Clifford Maiying Kong Jiapeng Huang Biometrics and Data Science Fosun Pharma Beijing 100026 PR China. Department of Bioinformatics and Biostatistics SPHIS University of Louisville Louisville KY 40202 USA. Department of Anesthesiology Peking Union Medical College Hospital Chinese Academy of Medical Science No.1 Shuaifuyuan Wangfujing Dongcheng District Beijing 100730 PR China. Department of Anesthesiology & Perioperative Medicine University of Louisville 530 South Jackson Street Louisville KY 40202 USA.

The rapid global spread of Coronavirus disease 2019 (COVID-19) has seriously threatened human life and health. Effects of traditional Chinese medicine, Lianhua Qingwen, combined with western medicine remains controversial for treatment of COVID-19. Evidence to support use of Lianhua Qingwen in COVID-19 is lacking. In this study, we systematically reviewed literature on the use of Lianhua Qingwen in COVID-19, and we performed meta-analysis to assess the effect of Lianhua Qingwen in COVID-19 management. We found that when combined with western medicine in the treatment of COVID-19 patients, Lianhua Qingwen may shorten duration of fever, reduce adverse events, decrease rate of conversion to severe disease, and improve symptom recovery and chest radiographic signs of pneumonia.

关键词： COVID-19 Lianhua Qingwen Meta-analysis Traditional medicine

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