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A systematic literature review of machine learning application in COVID-19 medical image classification

Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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Mutual dependency between lncRNA LETN and protein NPM1 in controlling the nucleolar structure and functions sustaining cell proliferation

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细胞研究（英文版） 2021年第6期31卷 664-683页

作者： Xianteng Wang Xiaolin Hu Wanlu Song Hui Xu Zhengtao Xiao Rongyao Huang Qingran Bai Fan Zhang Yongzhen Chen Yu Liu Jianhuo Fang Xin Li Qin Shen Haitao Zhao Xuerui Yang MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences Tsinghua UniversityBeijing 100084China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Beijing 100084China Brain and Spinal Cord Innovative Research Center of Tongji Hospital School of Life Sciences and TechnologyTongji University and Frontier Science Research Center for Stem Cells of Ministry of EducationShanghai 200092China Department of Liver Surgery Peking Union Medical College HospitalChinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC)Beijing 100730China

Fundamental processes such as ribosomal RNA synthesis and chromatin remodeling take place in the nucleolus,which is hyperactive in fast-proliferating *** sophisticated regulatory mechanism underlying the dynamic nucleolar structure and functions is yet to be fully *** present study uncovers the mutual functional dependency between a previously uncharacterized human long non-coding RNA,which we renamed LETN,and a key nucleolar protein,***,being upregulated in multiple types of cancer,LETN resides in the nucleolus via direct binding with *** plays a critical role in facilitating the formation of NPM1 pentamers,which are essential building blocks of the nucleolar granular component and control the nucleolar *** of LETN or NPM1 led to similar and profound changes of the nucleolar morphology and arrest of the nucleolar functions,which led to proliferation inhibition of human cancer cells and neural progenitor ***,this inter-dependency between LETN and NPM1 is associated with the evolutionarily new variations of NPM1 and the coincidental emergence of LETN in higher *** propose that this human-specific protein-lncRNA axis renders an additional yet critical layer of regulation with high physiological relevance in both cancerous and normal developmental processes that require hyperactive nucleoli.

关键词： Cell division Long non-coding RNAs

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Analysis of Acoustic Features in Gender Identification Model for English and Bahasa Indonesia Telephone Speeches

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Procedia Computer Science 2019年 157卷 199-204页

作者： Muhamad Fitra Kacamarga Tjeng Wawan Cenggoro Arif Budiarto Reza Rahutomo Bens Pardamean Eureka.ai Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the most interesting topics in auditory problem is determining gender of the speaker. In recent years, machine learning has gained significant attentions as a way to build a classifier from labeled data which also can be implemented to build a gender classifier. In this study we develop gender classifier using two different datasets with different languages, English and Bahasa Indonesia. Each data from both datasets is represented by 20 acoustic features. Multi Layer Perceptron (MLP) is used to build the classification model using all these features and trained only on English dataset. This model is evaluated in both dataset to get the performance matrices consist of accuracy, AUROC, precision and recall. Ultimately, using this model we also identify and compare important features from both dataset to see the different characteristics of English and Bahasa Indonesia speeches.

关键词： Gender Identification Acoustic Feature Speech Recognition

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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The keyword-based and semantic-driven data matching approach for assisting structuralizing the textual clinical documents

The keyword-based and semantic-driven data matching approach...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Chi-Huang Chen Xiao-Ou Ping Zi-Jun Wang Sheau-Ling Hsieh Li-Chin Chen Yi-Ju Tseng Ching-Wei Hsu Feipei Lai Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronic and Bioinformatics National Taiwan University Taipei Taiwan EECS International Graduate Program Institute IT Center National Chiao Tung University Hsinchu Taiwan

The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data retrieval from the non-structuralized textual clinical documents such as radiology reports, pathology reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction verification interface, clinicians can extract and verify the matched information semi-automatically. The extracted data can be filled into predefined case-oriented templates. The structuralized data can be stored back into the clinical data warehouse for further analyzing. Moreover, the case-oriented templates can support collecting corresponding extracted data for various researches.

关键词： Data mining Semantics Pattern matching Tumors Radiology Pathology Cancer

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Experimental modeling of Alzheimer's disease: Translational lessons from cross-taxon analyses

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第5期21卷 e70273页

作者： Konstantin B Yenkoyan Maria M Kotova Kirill V Apukhtin David S Galstyan Tamara G Amstislavskaya Tatyana Strekalova Murilo S de Abreu Vergine A Chavushyan Lee Wei Lim Longen Yang Denis D Rosemberg Allan V Kalueff Neuroscience Laboratory COBRAIN Center Yerevan State Medical University after M. Heratsi Yerevan Armenia. Neuroscience Program Sirius University of Science and Technology Sochi Russia. Institute of Translational Biomedicine St. Petersburg State University St. Petersburg Russia. Institute of Experimental Medicine Almazov National Medical Research Centre Ministry of Healthcare of Russian Federation St. Petersburg Russia. Scientific Research Institute of Neurosciences and Medicine Novosibirsk Russia. Research Institute of General Pathology and Pathophysiology Moscow Russia. Graduate Program in Health Sciences Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil. Department of Biosciences and Bioinformatics School of Science Xi'an Jiaotong-Liverpool University Suzhou China. Suzhou Municipal Key Laboratory of Neurobiology and Cell Signaling School of Science Xi'an Jiaotong-Liverpool University Suzhou China. Laboratory of Experimental Neuropsychobiology Department of Biochemistry and Molecular Biology Natural and Exact Sciences Center Federal University of Santa Maria Santa Maria Brazil. Graduate Program in Biological Sciences: Toxicological Biochemistry Federal University of Santa Maria Santa Maria Brazil. The International Zebrafish Neuroscience Research Consortium (ZNRC) Slidell Louisiana USA.

Alzheimer's disease (AD) is a severely debilitating neurodegenerative disease with a rapidly increasing global prevalence, poorly understood causes, and no efficient treatments. Experimental models are valuable for studying AD pathogenesis, including amyloid beta and tau accumulation, synaptic dysfunction, and neuroinflammation. While no model fully reproduces the disease, we take an evolutionary biology approach to discuss available models across taxa, from mammals (rodents, primates) to zebrafish, Drosophila melanogaster, and Caenorhabditis elegans. Evaluating their strengths and limitations provides insight into disease mechanisms and may refine research strategies for improved diagnostics and therapeutic screening. Traditional models have significantly contributed to AD research, yet their translational limitations highlight the need for physiologically relevant alternatives. Integrating humanized rodent models, zebrafish, organoids, and induced pluripotent stem cell-based systems-along with advances in bioengineering and genetic editing-may offer a more comprehensive framework to bridge the gap between preclinical research and clinical application. HIGHLIGHTS: Experimental models across rodents, primates, zebrafish, fruit flies, and worms provide key insights into Alzheimer's disease (AD). Cross-taxon comparisons assess strengths and weaknesses in AD models. Evolutionary biology approaches refine experimental strategies for AD research. Diverse animal models improve understanding of AD pathogenesis. Cross-species models enhance diagnostics and therapeutic strategy development.

关键词： Alzheimer's disease animal models cross‐taxon analyses evolutionary psychiatry translation medicine

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

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Procedia Computer Science 2023年 227卷 46-54页

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Mahmud Isnan Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 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.

关键词： lactose intolerance SNP genetic test feature selection

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Handling Data Sparseness in Gene Network Reconstruction

Handling Data Sparseness in Gene Network Reconstruction

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IEEE Symposium on Computational Intelligence and bioinformatics and Computational Biology (CIBCB)

作者： G.B. Bezerra T.V. Barra F.J. von Zuben L.N. de Castro Laboratory of Bioinformatics and Bio-Inspired Computing (LBiC) Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program on Informatics Catholic University of Santos Sao Paulo Brazil

One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is necessary to give much more precedence to generalization, rather than specificity, otherwise, any attempt will be fated to overfitting. In this paper we address this issue by focusing on data sparseness and noisy information, and propose a density estimation technique that achieves regularized curves when data is scarce. We first compare the proposed method with the EM algorithm for mixture models on density estimation problems. Next, we apply the method, together with Bayesian networks, on realistic simulations of static gene networks, and compare the obtained results with the standard discrete Bayesian network model. We intend to demonstrate that adopting a discrete approach is not justifiable when only a small amount of information is available.

关键词： Intelligent networks Bayesian methods Genetics bioinformatics Computer networks Reverse engineering Cellular networks Noise robustness Laboratories Data engineering

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Database Design for Pre-Eclampsia Case Administration 8

Database Design for Pre-Eclampsia Case Administration

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Asadi, Faisal Elwirehardja, Gregorius Natanael Trinugroho, Joko Pebrianto Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

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. © 2023 IEEE.

关键词： Medical informatics

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： Machine learning

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