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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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Covariate Adjustment in Randomized Experiments Motivated by Higher-Order Influence Functions

arXiv

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arXiv 2024年

作者： Zhao, Sihui Wang, Xinbo Liu, Lin Zhang, Xin School of Mathematical Sciences CMA-Shanghai Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University China Department of Bioinformatics and Biostatistics School of Life Sciences Shanghai Jiao Tong University China SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Global Biometrics & Data Management Pfizer Inc

Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, is a fundamental theoretical device for constructing rate-optimal causal-effect estimators from observational studies. However, the value of HOIF for analyzing well-conducted randomized controlled trials (RCTs) has not been explicitly explored. In the recent U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines on the practice of covariate adjustment in analyzing RCTs, in addition to the simple, unadjusted difference-in-mean estimator, it was also recommended to report the estimator adjusting for baseline covariates via a simple parametric working model, such as a linear model. In this paper, we show that a HOIF-motivated estimator for the treatment-specific mean has significantly improved statistical properties compared to popular adjusted estimators in practice when the number of baseline covariates p is relatively large compared to the sample size n. We also characterize the conditions under which the HOIF-motivated estimator improves upon the unadjusted one. Furthermore, we demonstrate that a novel debiased adjusted estimator proposed recently by Lu et al. is, in fact, another HOIF-motivated estimator in disguise. Numerical and empirical studies are conducted to corroborate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Controlled drug delivery

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Asymmetric epigenome maps of subgenomes reveal imbalanced transcription and distinct evolutionary trends in Brassica napus

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Molecular Plant 2021年第4期14卷 604-619页

作者： Qing Zhang Pengpeng Guan Lun Zhao Meng Ma Liang Xie Yue Li Ruiqin Zheng Weizhi Ouyang Shunyao Wang Hongmeijuan Li Ying Zhang Yong Peng Zhilin Cao Wei Zhang Qin Xiao Yuanling Xiao Tingdong Fu Guoliang Li Xingwang Li Jinxiong Shen National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China National Engineering Research Center of Rapeseed Huazhong Agricultural UniversityWuhan 430070China Agicultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricutural Big Data3D Genomics Research CenterCollge of InformaticsHuazhong Agricultural UniversityWuhan 430070China These authors contributed equally to this article.

The complexity of the epigenome landscape and transcriptional regulation is significantly increased during plant polyploidization,which drives genome evolution and contributes to the increased adaptability to diverse ***,a comprehensive epigenome map of Brassica napus is still *** this study,we performed integrative analysis of five histone modifications,RNA polymerase Ⅱ CCU-pancy,DNA methylation,and transcriptomes in two *** lines(2063A and B409),and established global maps of regulatory elements,chromatin states,and their dynamics for the whole genome(including the An and Cn subgenomes)in four tissue types(young leaf,flower bud,silique,and root)of these two *** 65.8% of the genome was annotated with different epigenomic *** with the Cn subgenome,the An subgenome possesses a higher level of active epigenetic marks and lower level of repressive epigenetic *** from subgenome-unique regions contribute to the major differences between the An and Cn *** histone modifications between homeologous gene pairs reflect their biased expression *** identified a novel bivalent chromatin state(with H3K4me1 and H3K27me3)in *** that is associated with tissue-specific gene ***,we observed that different types of duplicated genes have discrepant patterns of histone modification and DNA methylation ***,our findings provide a valuable epigenetic resource for allopolyploid plants.

关键词： Brassica napus epigenome ChlP-seq gene expression

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A systematic literature review of machine learning application in COVID-19 medical image classification 7

A systematic literature review of machine learning applicati...

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

作者： Daniel 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

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. © 2022 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Cancer Prediction Using Clinical and Genomic data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics data integration Medical services Predictive models data models bioinformatics Cancer Systematic literature review

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3D Transfer Learning for Lung Nodule Malignancy Binary Classification

3D Transfer Learning for Lung Nodule Malignancy Binary Class...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry 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: (数字)9798331508616

ISBN: (纸本)9798331508623

Many studies have focused on classifying lung nodules in CT scans, primarily utilizing 2D approaches. However, CT scans are inherently 3D representations, which presents challenges for traditional convolutional neural networks (CNNs) designed for 2D images. In this work, a novel combination of 3D transfer learning and batch oversampling was adopted to classify benign and malignant lung nodules using the Lung Nodule Analysis - Intelligent Systems in Medical Imaging (LUNA22-ISMI) dataset. The preprocessing pipeline consisted of excluding indeterminate labeled nodules, clamping patches to the lung window, extracting nodule regions based on diameter, and applying z-score normalization. Batch oversampling with augmentation was implemented for the training set. Multiple models pre-trained on ImageNet with weights adapted for 3D were evaluated using a five-fold cross-validation setup. Notably, EfficientNetB1 achieved the highest overall and balanced metrics on the test sets, surpassing 90% in sensitivity, specificity, precision, and accuracy, highlighting its potential for binary classification of lung nodules. https://***/nheryanto/luna22

关键词： Training Solid modeling Adaptation models Three-dimensional displays Lungs Computed tomography Transfer learning Windows Cancer Biomedical imaging

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InDRI: Intelligent Diagnosis Radiology Images 6

InDRI: Intelligent Diagnosis Radiology Images

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Ritonga, Imelda Liana Gultom, Roby Pahala Januario Lubis, Elida Afni Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Clinical Pathology Unit Rumah Sakit Imelda Pekerja Indonesia Medan20239 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

The need for an early screening and Computer-Aided Diagnosis (CAD) system based on Artificial Intelligence (AI) for the field of radiology is essential to realize considering the large impact of lung diseases globally. However, developing AI requires a lot of data, which is a challenge because data in the health sector tends to be limited in quantity and has disparities between diagnostic categories (data imbalance). To overcome this issue, DINO ViT models was trained with the publicly available COVID-19 Radiography database in our previous study. The model was subsequently implemented into a web application, with this study focusing on the development of a prototype for the application. The study found this application can be useful for medical personnel and doctors in carrying out early screening and CAD. Meanwhile, it also highlighted the general unfamiliarity with AI applications among medical staff, emphasizing the need for increased education and training on AI's role in healthcare. © 2024 IEEE.

关键词： Lung cancer

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Fossil Image Identification using Deep Learning Ensembles of data Augmented Multiviews

arXiv

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arXiv 2023年

作者： Hou, Chengbin Lin, Xinyu Huang, Hanhui Xu, Sheng Fan, Junxuan Shi, Yukun Lv, Hairong Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing China Fuzhou Institute of Data Technology Fuzhou China College of Physics and Information Engineering Fuzhou University Fuzhou China School of Earth Sciences and Engineering Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing China

1. Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labelled fossil images are often limited due to fossil preservation, conditioned sampling and expensive and inconsistent label annotation by domain experts, which pose great challenges to training deep learning-based image classification models. 2. To address these challenges, we follow the idea of the wisdom of crowds and propose a multiview ensemble framework, which collects Original (O), Grey (G) and Skeleton (S) views of each fossil image reflecting its different characteristics to train multiple base models, and then makes the final decision via soft voting. 3. Experiments on the largest fusulinid dataset with 2400 images show that the proposed OGS consistently outperforms baselines (using a single model for each view), and obtains superior or comparable performance compared to OOO (using three base models for three the same Original views). Besides, as the training data decreases, the proposed framework achieves more gains. While considering the identification consistency estimation with respect to human experts, OGS receives the highest agreement with the original labels of dataset and with the re-identifications of two human experts. The validation performance provides a quantitative estimation of consistency across different experts and genera. 4. We conclude that the proposed framework can present state-of-the-art performance in the fusulinid fossil identification case study. This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work. Notably, the result, which shows more performance gains as train set size decreases or over a smaller imbalance fossil dataset, suggests the potential application to identify rare fossil images. The proposed framework also demonstrates its pot

关键词： Image classification

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Ontology-based representation and analysis of conditional vaccine immune responses using Omics data 14

Ontology-based representation and analysis of conditional va...

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14th International Conference on Biomedical Ontologies, ICBO 2023

作者： Huffman, Anthony Ong, Edison Brunson, Tim Sanati, Nasim Zheng, Jie Masci, Anna Maria Wu, Guanming He, Yongqun Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States GlaxoSmithKline Rixensart1330 Belgium Oregon Health & Science University PortlandOR United States Department of Genetics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Office of Data Science National Institute of Environmental Health Science DurhamNC United States Unit of Laboratory Animal Medicine University of Michigan Ann ArborMI United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Center for Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States

ImmPort, the world’s largest repository of immunology data, includes many vaccine immune response datasets. ImmPort maps the metadata of these studies to ontology and database schema. As of February 28, 2023, our ImmPort data analysis identified 6.258 immune exposures using 47 vaccines in 4,607 human subjects, and 324 cohort studies from the ImmPort. We hypothesized that an integrative ontological representation of the data from these studies would enhance our understanding and analysis of these ImmPort vaccine studies, and with ontological classification and tools such as VIGET, we could further study the effects of different conditions such as vaccine types and host biological sex on the vaccine response gene expression profiles. Our Vaccine Ontology (VO) analysis classified these 37 vaccines into bacterial, viral, and protozoan vaccine types with different vaccine properties. The ImmPort metadata types were modeled with the Vaccine Investigation Ontology (VIO). Our new ontology-based pipeline extracted vaccine response data from the ImmPort database, annotated them based on ontology, obtained corresponding gene expression data from the GEO, and performed consistent omics data analysis. Our use case found gene profiles shared and differed from live and killed inactivated influenza vaccines. Furthermore, our Omics data analysis using the VIGET tool found that female and male human subjects have differential host responses for influenza vaccines. For example, our study showed much stronger early female responses to influenza vaccination than males, and males was able to show active immune responses at a later stage. Interestingly, the female (but not male) human subject group also showed significantly enriched neutrophil degranulation at Day 3 after influenza vaccination;however, males (but not females) displayed significantly enriched neutrophil degranulation at Day 14 after influenza vaccination. These mechanisms have been used to find differences between the gene

关键词： Ontology

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

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Procedia Computer Science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

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