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Proteome-wide prediction of the mode of inheritance and molecular mechanisms underlying genetic diseases using structural interactomics

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iscience 2025年第7期28卷

作者： Ali Saadat Jacques Fellay School of Life Sciences Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Precision Medicine Unit Biomedical Data Science Center Lausanne University Hospital and University of Lausanne Lausanne Switzerland

Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, while autosomal recessive diseases are primarily linked to loss-of-function variants. In this study, we introduce a graph-of-graphs approach that leverages protein-protein interaction networks and high-resolution protein structures to predict the mode of inheritance of diseases caused by variants in autosomal genes and to classify dominant-associated proteins based on their functional effect. Our approach integrates graph neural networks, structural interactomics, and topological network features to provide proteome-wide predictions, thus offering a scalable method for understanding genetic disease mechanisms.

关键词： molecular interaction molecular network neural networks quantitative genetics

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Meteorological parameters for PV installations in Indonesia

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Many countries are still dependent on fossil fuels as their primary energy supply. However, there is a growing concern about the sustainability and the environmental issue of fossil fuels. Therefore, it is important to utilize renewable resources as the energy supply, such as solar energy. As a country with a tropical climate, Indonesia has a great potential to use solar energy, due to the abundance of sunlight exposure. The present study aims to analyze several meteorological parameters for the installation of photovoltaic (PV) technology in Jakarta, Indonesia. The meteorological data provided by the Ministry of Environment and Forestry, Indonesia from 2008-2021 were used at three different locations in Jakarta. data visualizations were used to show the difference between each variable in different locations. In addition, the statistical difference between the variables in the three locations was also examined using the Mann-Whitney U test. The test shows that all parameters are evenly distributed in the three locations except for a moderate difference in air temperature in the two locations. Furthermore, the monthly analysis of the parameters also indicates that the dry season, especially from August to September, would be the optimal time to utilize PV technology. Hence, this study would be useful to give preliminary insight regarding a site-specified assessment of PV plants in Jakarta based on local meteorological data.

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

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AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Back to science in Searching for SARS-CoV-2 Origins

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China CDC weekly 2023年第14期5卷 315-317页

作者： William J Liu Wenwen Lei Xiaozhou He Peipei Liu Qihui Wang Zhiqiang Wu Yun Tan Shuhui Song Gary Wong Jian Lu Jingkun Jiang Qiang Wei Mingkun Li Juncai Ma Xiaozhong Peng Yixue Li Baoxu Huang Yigang Tong Jun Han Guizhen Wu NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational MedicineRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina Beijing Institute of Genomics Chinese Academy of Sciencesand China National Center for BioinformationBeijingChina Laboratory of Molecular Virology and Immunology Institut Pasteur of ShanghaiChinese Academy of SciencesShanghaiChina State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina School of Environment Tsinghua UniversityBeijingChina National Pathogen Resource Center Chinese Center for Disease Control and Prevention(China CDC)BeijingChina Microbial Resource and Big Data Center Chinese National Microbiology Data Center(NMDC)Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Medical Molecular Biology Department of Molecular Biology and BiochemistryInstitute of Basic Medical SciencesMedical Primate Research CenterNeuroscience CenterChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijingChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina Bio-Med Big Data Center Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghaiChina China Animal Health and Epidemiology Center Qingdao CityShandong ProvinceChi

In recent decades,emerging and re-emerging human-infecting pathogens have been represented as huge threats to public health and have become a global concern(1).After outbreaks of two coronaviruses(CoVs),severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV),severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)became the first-known pandemic hastening CoV with tremendous wrecking to the world(2).The origin tracing of these emerging pathogens is of great significance in infectious disease prevention and control(3–4).The origin of SARS-CoV-2 remains elusive after the more than 3-year pandemic,though scientists around the world are making great *** the experience of studying many other infectious pathogens,origin tracing is systematic and time-consuming *** supposed origins of many infectious pathogens are still in debate,including SARS-CoV and human immunodeficiency virus,etc(5).

关键词： prevention respiratory acute

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber 3

AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in ...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Rahutomo, Reza Mahesworo, Bharuno Cenggoro, Tjeng Wawan Budiarto, Arif Suparyanto, Teddy Surya Atmaja, Don Bosco Samoedro, Bayu Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Palm oil

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TMB相关免疫浸润调控评分(MOTIF)预测免疫治疗响应并指导联合增效

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science Bulletin 2024年第6期69卷 803-822页

作者：钱政宇潘艺芊李薛鑫陈衍行吴灏祥刘泽先 Martin Kosar Jiri Bartek 王梓贤徐瑞华 Department of Medical Oncology Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Provincial Clinical Research Center for CancerResearch Unit of Precision Diagnosis and Treatment for Gastrointestinal CancerChinese Academy of Medical SciencesGuangzhou 510060China Science for Life Laboratory Division of Genome BiologyDepartment of Medical Biochemistry and BiophysicsKarolinska InstituteStockholm S-17121Sweden Department of General Surgery The Fourth Affiliated HospitalChina Medical UniversityShenyang 110032China Bioinformatics Platform Sun Yat-sen University Cancer CenterGuangzhou 510060China Zhejiang University-University of Edinburgh Institute Zhejiang University School of MedicineHaining 314400China Edinburgh Medical School Biomedical SciencesCollege of Medicine and Veterinary MedicineThe University of EdinburghEdinburgh EH11LTUK Danish Cancer Society Research Center Copenhagen DK-2100Denmark Laboratory of Artificial Intelligence and Data Science Sun Yat-sen University Cancer CenterGuangzhou 510060China

Patients with high tumor mutational burden(TMB)levels do not consistently respond to immune checkpoint inhibitors(ICIs),possibly because a high TMB level does not necessarily result in adequate infiltration of CD8^(+)T *** bulk ribonucleic acid sequencing(RNA-seq)data from 9311 tumor samples across 30 cancer types,we developed a novel tool called the modulator of TMB-associated immune infiltration(MOTIF),which comprises genes that can determine the extent of CD8^(+)T cell infiltration prompted by a certain TMB *** confirmed that MOTIF can accurately reflect the integrity and defects of the cancer-immunity *** analyzing 84 human single-cell RNA-seq datasets from 32 types of solid tumors,we revealed that MOTIF can provide insights into the diverse roles of various cell types in the modulation of CD8^(+)T cell *** pretreatment RNA-seq data from 13 ICI-treated cohorts,we validated the use of MOTIF in predicting CD8^(+)T cell infiltration and ICI *** the components of MOTIF,we identified EMC3 as a negative regulator of CD8^(+)T cell infiltration,which was validated via in vivo ***,MOTIF provided guidance for the potential combinations of programmed death 1 blockade with certain immunostimulatory drugs to facilitate CD8^(+)T cell infiltration and improve ICI efficacy.

关键词： Tumor mutational burden Immunotherapy Cancer-immunity cycle Treatment efficacy prediction CD8^(+)T cell infiltration Combination therapy

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Systematic literature review: Accelerate the rice production for global food security

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AIP Conference Proceedings 2023年第1期2594卷

作者： N. Dominic T. W. Cenggoro B. Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

By 2050, about 70% of extra food production is needed to feed the world's population. As a majority staple food, the expansion of rice production should be accelerated. This systematic literature review aims to discover the factors that influence rice yields through a methodology that is partitioned into four main stages, i.e., query entry into multiple database sources, article title filtering, abstract filtering, and final article selection. The results show that genetics, irrigation system management, and farmers’ long-evolved local knowledge or experiences are the three big factors that elevate the rice production rates. Nevertheless, global warming is a serious challenge that should be deeply considered due to its great impact on reducing rice yields by up to 14.5%. Thus, the development of genetically modified rice varieties needs to be escalated while still maintaining other exogenous factors. This study contributes to providing a broader perspective for decision-makers and other relevant experts to accelerate rice production to achieve global food security.

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