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Organoid morphology-guided classification for oral cancer reveals prognosis

Cell reports. Medicine 2025年 102129页

作者： Mi Rim Lee Sumin Kang Jonghyun Lee Sun-Young Kong Youngwook Kim Yu-Sun Lee Hye Won Shon Gyeongmin Kang Jiyoung Lee Suk Min Youn Da Woon Kwack Joo Yong Park Soung Min Kim Wonyoung Choi Jong-Ho Lee Dongkwan Shin Ik-Jae Kwon Sung-Woen Choi Yun-Hee Kim Molecular Imaging Branch Division of Convergence Technology Research Institute of National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea Department of Cancer Biomedical Science National Cancer Center Graduate School of Cancer Science and Policy 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea. Bioinformatics Branch Division of Cancer Data Science Research Institute of National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea. Department of Cancer Biomedical Science National Cancer Center Graduate School of Cancer Science and Policy 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea Targeted Therapy Branch Division of Rare and Refractory Cancer Research Institute of National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea Department of Laboratory Medicine National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea. Molecular Imaging Branch Division of Convergence Technology Research Institute of National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea. Oral Oncology Clinic Research Institute and Hospital National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea. Department of Oral and Maxillofacial Surgery Seoul National University Dental Hospital Dental Research Institute School of Dentistry Seoul National University 101 Daehak-ro Jongno-gu Seoul 03080 Republic of Korea. Center for Clinical Trials Research Institute and Hospital National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea Cancer Molecular Biology Branch Division of Cancer Biology Research Institute of National Cancer Center 323 Ilsan-ro Ilsandong-gu Goyang Gyeonggi-do 10408 Republic of Korea. Oral Oncology Clinic Research Institute and Hospital National Cancer Center 323 Ilsan-ro Ilsandong-gu Goya

Oral cancer is an aggressive malignancy with a survival rate below 50% in advanced stages due to low mutation rates, lack of molecular subtypes, and limited treatment targets. This study presents a pioneering approach to classifying oral cancer subtypes based on the morphology of patient-derived organoids (PDOs) and proposes a therapeutic strategy. We establish 76 cancer and 81 normal PDOs. For cancer PDOs, both manual classification and AI-based scoring are utilized to categorize them into three distinct subtypes: normal-like, dense, and grape-like. These subtypes correlate with unique transcriptomic profiles, genetic mutations, and clinical outcomes, with patients harboring dense and grape-like organoids exhibiting poorer prognoses. Furthermore, drug response assessments of 14 single agents and cisplatin combination therapies identify a synergistic treatment approach for resistant subtypes. This study highlights the potential of integrating morphology-based classification with genomic and transcriptomic analyses to refine oral cancer subtyping and develop effective treatment strategies.

关键词： combination therapy morphology analysis oral cancer organoids

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Health Assistant Wearable-Based data science System Model: A Pilot Study 3

Health Assistant Wearable-Based Data Science System Model: A...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Budiarto, Arif Febriana, Trisna Suparyanto, Teddy Caraka, Rezzy Eko Pardamean, Bens Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Indonesia Faculty of Science National University of Malaysia School of Mathematical Science Bangi43000 Malaysia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The rapid development of Information and Communication Technology (ICT) has an impact on the innovation of new technology that gives benefits for health. One of its latest development is wearable device technology. Nowadays, the growth of this technology market is very positive. But unfortunately the growth of this technology is not followed by the availability of supporting applications that provide maximum benefits for users by utilizing personal health and activity data. This paper is intended to provide a report of our initial research in developing a health assistant solution that utilizes wearable device generated data. The whole research process is executed using Design Thinking approach that put the users as the main consideration. This initial research is started by designing a field research that can potentially gathered users' data generated from wearable device. A series of statistical and machine learning techniques are presented to analyze time series data to create a model. Finally, to implement the model, a mobile application prototype is also proposed. The outcomes from this initial research will be used to develop a fully working solution. © 2018 IEEE.

关键词： Learning systems

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Deep learning for imbalance data classification using class expert generative adversarial network

arXiv

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arXiv 2018年

作者： Fanny Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Designing an Energy Tracker and Smart Home Solutions Using Design Thinking Methodology 10

Designing an Energy Tracker and Smart Home Solutions Using D...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hiererra, Siti Elda Chandra, Victor Ivana, Jane Wibowo, Jessica Lynn Asadi, Faisal School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Business Analytics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

As energy consumption rises, finding ways to reduce energy usage in homes is crucial. This paper presents a case study using design thinking to create a daily energy tracker that helps users reduce energy consumption. We conduct user research, define the problem, brainstorm solutions (mobile app, smart thermostat, smart plug, physical tracker), and test prototypes. A mobile app that tracks energy usage in real-time and offers personalized tips emerges as the most promising solution. We develop and launch the app, partnering with an energy company. This case study showcases the effectiveness of design thinking in creating a user-centered solution for reducing energy consumption. © 2023 IEEE.

关键词： Energy utilization

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RiceENCODE:A comprehensive epigenomicdatabase as a rice Encyclopedia of DNA Elements

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Molecular Plant 2021年第10期14卷 1604-1606页

作者： Liang Xie Minghao Liu Lun Zhao Kai Cao Peng Wang Wenhao Xu Wing-Kin Sung Xingwang Li Guoliang Li National Key Laboratory of Crop Genetic Improvement Hubei HongshanLaboratoryHuazhong Agricultural University1 Shizishan StreetHongshanDistrictWuhan 430070HubeiChina Agricultural Bioinformatics Key Laboratory of Hubei Province and HubeiEngineering Technology Research Center of Agricultural Big Data 3DGenomics Research CenterHuazhong Agricultural University1 ShizishanStreetHongshan DistrictWuhan 430070HubeiChina Department of Computer Science National University of SingaporeSingapore 117417Singapore Department of Computational and Systems Biology Genome Institute ofSingaporeSingapore 138672Singapore

Dear Editor,Rice(Oryza sativa)is one of the most important crops in the worldand a common model plant for genomic *** genomesofXian/IndicaandGeng/Japonicahave been completelysequenced and annotated with accurate genome *** the past few years,epigenomic information,including DNAmethylation,histone modification,and chromatin accessibility,has been characterized in theXian/IndicaandGeng/Japonicage-nomes(Zhao et al.,2020).Quite a few rice three-dimensionalgenome studies have been published in the meantime(Zhaoet al.,2019).However,it is still a big challenge for many groupsthat lack dedicated bioinformatic personnel or sufficientcomputational resources to utilize such epigenetic data.

关键词： sativa Oryza crops

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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Software Engineering and Information Technology (ICoSEIT), International Conference on

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

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images.

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Quality assessment of Affymetrix GeneChip data using the EM algorithm and a naïve Bayes classifier

Quality assessment of Affymetrix GeneChip data using the EM ...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Howard, Brian E. Sick, Beate Perera, Imara Yang, Ju Im Winter-Sederoff, Heike Heber, Steffen Bioinformatics Research Center North Carolina State University Raleigh NC United States Institute of Data Analysis and Process Design Zurich University of Applied Science Winterthur Winterthur Switzerland Department of Plant Biology North Carolina State University Raleigh NC United States Department of Computer Science North Carolina State University Raleigh NC United States

ISBN: (纸本)1424415098

Recent research has demonstrated the utility of using supervised classification systems for automatic identification of low quality microarray data. However, this approach requires annotation of a large training set by a qualified expert. In this paper we demonstrate the utility of an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and naïve Bayes classification. On our test set, this system exhibits performance comparable to that of an analogous supervised learner constructed from the same training data. ©2007 IEEE.

关键词： Maximum principle

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Leveraging ChemBERTa Robustness in Drug-Drug Interaction Classification via Molecular Decomposition

Leveraging ChemBERTa Robustness in Drug-Drug Interaction Cla...

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International Seminar on research of Information Technology and Intelligent Systems (ISRITI)

作者： Christian Kenneth Alyssa Imani Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Understanding drug-drug interactions (DDIs) can benefit us by enhancing drug effectiveness and preventing side effects that arise from consuming multiple drugs administered together. However, as the number of novel drugs increases, predicting potential DDIs becomes more challenging. Previous studies have used the molecular language model ChemBERTa to predict potential DDIs, but the predictions were only tested on unseen drugs and not dissimilar drugs, while also performing poorly. In this study, we demonstrate that a simple molecular preprocessing method using BRICS decomposition can enhance ChemBERTa robustness in predicting potential DDIs for unseen-dissimilar drugs. Specifically, we employ fragmentation with a minimum fragment size of 5 (BRICS-5), which balances the number of fragments and decomposition speed. Using this method, we were able to improve ChemBERTa robustness, reflected by an approximate increase of 1.5% in recall and 2.2% in the f1-score compared to non-decomposed molecules. Our findings suggest that this preprocessing method could be useful in the development of future AI-based approaches for solving challenging molecular tasks across different settings and various model architectures.

关键词： Drugs Seminars Accuracy data preprocessing Predictive models Robustness data models Intelligent systems Information technology

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Systematic Literature Review on Metaheuristic Algorithms in SNP Analysis for Diseases

Systematic Literature Review on Metaheuristic Algorithms in ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho 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

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies.

关键词： Macular degeneration Filtering databases Metaheuristics Time measurement Internet Drug discovery Intelligent systems Genetic algorithms Systematic literature review

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression

Systematic Literature Review of Transformer Model Implementa...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Kenjovan Nanggala Gregorius Natanael Elwirehardja 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

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities.

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