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Single-sequence protein structure prediction using a language model and deep learning (Oct, 10.1038/s41587-022-01432-w, 2022)

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NATURE BIOTECHNOLOGY 2022年第11期40卷 1692-1692页

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Floristean, Christina Kharkar, Anant Roy, Koushik Rochereau, Charlotte Ahdritz, Gustaf Zhang, Joanna Church, George M. Sorger, Peter K. AlQuraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Nabla Bio Inc. Boston MA USA Department of Computer Science Columbia University New York NY USA Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New York NY USA Department of Systems Biology Columbia University New York NY USA Department of Systems Biology Harvard Medical School Boston MA USA

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

关键词： Computational biophysics Machine learning Protein structure predictions

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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End-to-end clinical temporal information extraction with multi-head attention 22

End-to-end clinical temporal information extraction with mul...

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22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023

作者： Miller, Timothy Bethard, Steven Dligach, Dmitriy Savova, Guergana Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School United States School of Information University of Arizona United States Department of Computer Science Loyola University Chicago United States

ISBN: (纸本)9781959429852

Understanding temporal relationships in text from electronic health records can be valuable for many important downstream clinical applications. Since Clinical TempEval 2017, there has been little work on end-to-end systems for temporal relation extraction, with most work focused on the setting where gold standard events and time expressions are given. In this work, we make use of a novel multi-headed attention mechanism on top of a pre-trained transformer encoder to allow the learning process to attend to multiple aspects of the contextualized embeddings. Our system achieves state of the art results on the THYME corpus by a wide margin, in both the in-domain and cross-domain settings. © 2023 Association for Computational Linguistics.

关键词： Learning systems

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Advances in 3D fusion of multimodal medical images: 3D reconstruction of bone, muscle, and ligament structures under load from radiographs and magnetic resonance imaging

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Research on Biomedical Engineering 2025年第1期41卷 1-35页

作者： da Silva, Daniel S. Astolfi, Rodrigo S. Jagatheesaperumal, Senthil K. Selvaraj, Priyadharsini Russel, Newlin Shebiah de Albuquerque, Victor Hugo C. Post-Graduation Program of Medical Surgical Sciences Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Teleinformatics Engineering Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Electronics and Communication Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India Department of Computer Science and Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India

Purpose: Foot and ankle pathologies are among the most prevalent conditions within the human locomotor system. Imaging examinations are crucial for diagnosing, treating, and achieving satisfactory functional outcomes for these conditions. However, conventional imaging exams often fail to capture all the necessary details for precise analysis, necessitating the use of multiple imaging modalities to evaluate bones, ligaments, cartilage, and other structures. Objective: This research aims to explore computational technologies and algorithms for 3D reconstruction and fusion of foot and ankle images, alongside the analysis, segmentation, classification, and evaluation techniques applied to these images. Methods: The study investigates different computational approaches for 3D reconstruction and multimodal image fusion, integrating X-ray and MRI data. Advanced segmentation and classification techniques are applied to enhance the precision of structural assessment. Additionally, we assess the availability and quality of databases for training and validating these models. Results: Our findings highlight a significant gap in research concerning these specific applications, as well as a shortage of suitable databases for 3D reconstruction and fusion of images from X-rays and MRI. Conclusion: The study emphasizes the need for dedicated datasets and further research to develop effective computational solutions for multimodal foot and ankle imaging. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： Magnetic resonance imaging

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational Engineering in Health and Medicine 2025年 13卷 275-285页

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science CambridgeMA United States Martinos Center for Biomedical Imaging CharlestownMA United States Harvard University Harvard Graduate Program in Biophysics CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology BostonMA United States Harvard Medical School BostonMA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： Neuroimaging

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Applications of Artificial Intelligence and Big Data for Strengthening the Role of Intelligence in Human Security 8

Applications of Artificial Intelligence and Big Data for Str...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Mubarok, Ahmad Shahrul Susandi, Armi Mantoro, Teddy State Intelligence College Medical Intelligence Post-Graduate Program Bogor Indonesia Sampoerna University Media-Tech Lab Departement of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

The state, through the government, should ensure national security and stability through the policies taken. For this reason, fast and accurate information and data are needed to form the basis for policymaking. Intelligence actors have an essential role in gathering information to identify and prevent threats within and outside the country. Technological advances affect changes in threats and increase the classification of these threats. Covid-19 is a real example of the threat in this era because almost all countries feel its impact. In addition to affecting health, the Covid-19 pandemic also impacts the global economic sector. The economic impact is felt by small and medium industries, goods and services products, and also the tourism sector. The method that is used in this study is a literature review from various national and international journals. AI and Big Data can be used to track viruses in real time, collect adequate public health data, and integrate vaccine use. Also, AI and Big Data can provide local decision and policy-makers with informed, evidence-based predictions. the threat of the Covid-19 pandemic and other infectious diseases that disrupt public health and have a broad impact must be anticipated and addressed in order to achieve the safety and existence of the nation and state. The author wants to emphasize the importance of the role of intelligence in anticipating threats from the visible to the invisible. © 2022 IEEE.

关键词： Artificial intelligence

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Private Data Protection as Countermeasure Against Misuses of medical Intelligent Technology Data: A Literature Study 8

Private Data Protection as Countermeasure Against Misuses of...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Praseptiadi Dahlian Persadha, Pratama Susandi, Armi Mantoro, Teddy State Intelligence College Medical Intelligence Post-graduate Program Bogor Indonesia Sampoema University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

medical Technologies develop along with the development of the Industrial Revolution 4.0 (I.R. 4.0). In I.R. 4.0, a collaborative ecosystem is formed between cyber technology and automation technology to be able to create intelligent systems to facilitate various human activities, including activities related to health. The most important thing in the system is data since it is the basis for the development of various key icons technologies in the I.R. 4.0: Cloud Computing, Internet of Things (IoT), Big Data, Artificial Intelligence (Al), Augmented Reality, and Cyber Security. Unfortunately, with the variety of I.R. 4.0 products, the data produced by these products is also scattered everywhere. Misuse of this scattered data - which the owner may not even know where the data are stored - can harm the individual concerned, and even the very existence of the state. Healthcare services now are undergoing Digital Transformation in which these data were needed to build high-quality services. The Health Digital Transformation strategy changes the direction of health services to be simpler and easier to use by the public, increasing efficiency, and thus providing a higher quality health service system so that it can trigger better economic growth. On the regulatory aspect, The Private Data Protection Bill was proposed by the Indonesian government to protect the data of the Indonesian people. Literature studies are used to obtain knowledge between the three: technology development, digital transformation in healthcare, and private data protection. This study seeks how important private data protection regulation is to realize digitally transformed healthcare. The study conducted indicates Private Data Protection Bills have the means to guarantee the rights of the data owner and prevent their data (including medical intelligence data) to be misused. © 2022 IEEE.

关键词： Intelligent systems

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Low-Cost Air Box Application for Indoor Environment Classification and LSTM Microclimate Prediction 6

Low-Cost Air Box Application for Indoor Environment Classifi...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Hsun-Jui, Hu Yu-Hsien, Chiu Pei-Ching, Yang Chang Jung Christian University Master Degree Program in Information and Design Tainan Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798331518158

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage. © 2024 IEEE.

关键词： Indoor air pollution

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Analysis of the Cyber Attacks in Indonesia's Health Sector in Industry 4.0 Era 8

Analysis of the Cyber Attacks in Indonesia's Health Sector i...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Hidayat, Arif Mantoro, Teddy Susandi, Armi Indonesian State Intelligence College Medical Intelligence Post-Graduate Program Bogor Indonesia Sampoema University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

At this time the world has undergone considerable changes, where the world has entered the era of the industrial revolution 4.0. With these changes, there are many positive impacts or opportunities that can support human activities in the economic, political, socio-cultural, health, and other fields. And alsothe author tries to analyze about industry 4.0 era for Indonesia's health sector with SWOT Analysis to know about Strenght, Weaknesses, Opportunities, and Threats. However, the challenges of technological advances can pose a threat to countries that are not ready to facethem, one of which is cyber attacks that can threaten data security. Therefore, it is necessary to be prepared in terms of data security by cooperating with various parties involved in it. © 2022 IEEE.

关键词： Industry 4.0

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