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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Systematic transcriptomics analysis of calorie restriction and rapamycin unveils their synergistic interaction in prolonging cellular lifespan

Communications Biology

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Communications Biology 2025年第1期8卷 1-16页

作者： Yizhong Zhang Trishia Yi Ning Cheng Jovian Jing Lin Mohammad Alfatah Arshia Naaz Rajkumar Dorajoo Mingtong Gao Bioinformatics Institute (BII) Agency for Science Technology and Research (A*STAR) Singapore Singapore Healthy Longevity Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Centre for Healthy Longevity National University Health System Singapore Singapore Genome Institute of Singapore (GIS) Agency for Science Technology and Research (A*STAR) Singapore Singapore Department of Paediatrics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

Aging is a multifaceted biological process marked by the decline in both mitotic and postmitotic cellular function, often central to the development of age-related diseases. In the pursuit of slowing or even reversing the aging process, a prominent strategy of significant interest is calorie restriction (CR), also known as dietary restriction, and the potential influence of a drug called rapamycin (RM). Both CR and RM have demonstrated the capacity to extend healthspan and lifespan across a diverse array of species, including yeast, worms, flies, and mice. Nevertheless, their individual and combined effects on mitotic and postmitotic cells, as well as their comparative analysis, remain areas that demand a thorough investigation. In this study, we employ RNA-sequencing methodologies to comprehensively analyze the impact of CR, RM, and their combination (CR + RM) on gene expression in yeast cells. Our analysis uncovers distinctive, overlapping, and even contrasting patterns of gene regulation, illuminating the unique and shared effects of CR and RM. Furthermore, the transcriptional synergistic interaction of CR + RM is validated in extending the lifespan of both yeast and human cells.

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Mutation of Oryza sativa CORONATINE INSENSITIVE 1b(OsCOI1b) delays leaf senescence

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Journal of Integrative Plant Biology 2015年第6期57卷 562-576页

作者： Sang-Hwa Lee Yasuhito Sakuraba Taeyoung Lee Kyu-Won Kim Gynheung An Han Yong Lee Nam-Chon Paek Department of Plant Science Plant Genomics and Breeding InstituteResearch Institute of Agriculture and Life SciencesSeoul National University Interdisciplinary Program in Bioinformatics Seoul National University Department of Plant Molecular Systems Biotechnology Crop Biotech InstituteKyung Hee University Department of Agricultural Biotechnology Seoul National University

Jasmonic acid （JA） functions in plant development, including senescence and immunity. Arabidopsis thaliana CORONATINE INSENSITIVE 1 encodes a JA receptor and functions in the JA‐responsive signaling pathway. The Arabidopsis genome harbors a single COI gene, but the rice （Oryza sativa） genome harbors three COI homologs, OsCOI1a, OsCOI1b, and OsCOI2. Thus, it remains unclear whether each OsCOI has distinct, additive, synergistic, or redundant func-tions in development. Here, we use the oscoi1b‐1 knockout mutants to show that OsCOI1b mainly affects leaf senescence under senescence‐promoting conditions. oscoi1b‐1 mutants stayed green during dark‐induced and natural senescence, with substantial retention of chlorophylls and photosyn-thetic capacity. Furthermore, several senescence‐associated genes were downregulated in oscoi1b‐1 mutants, including homologs of Arabidopsis thaliana ETHYLENE INSENSITIVE 3 and ORESARA 1, important regulators of leaf senescence. These results suggest that crosstalk between JA signaling and ethylene signaling affects leaf senescence. The Arabidopsis coi1‐1 plants containing 35S：OsCOI1a or 35S：OsCOI1b rescued the delayed leaf senescence during dark incubation, sug-gesting that both OsCOI1a and OsCOI1b are required for promoting leaf senescence in rice. oscoi1b‐1 mutants showed significant decreases in spikelet fertility and grain weight, leading to severe reduction of grain yield, indicating that OsCOI1‐mediated JA signaling affects spikelet fertility and grain filling.

关键词： Jasmonic acid leaf senescence OsCOI1b rice stay-green

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Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential

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Genome Biology 2025年第1期26.0卷 1-25页

作者： Bao, Pengfei Wang, Taiwei Liu, Xiaofan Xing, Shaozhen Ruan, Hanjin Ma, Hongli Tao, Yuhuan Zhan, Qing Belmonte-Reche, Efres Qin, Lizheng Han, Zhengxue Mao, Minghui Li, Mengtao Lu, Zhi John MOE Key Laboratory of Bioinformatics State Key Lab of Green Biomanufacturing Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China Institute for Precision Medicine Tsinghua University Beijing 100084 China Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program School of Life Sciences Tsinghua University Beijing China Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China National Clinical Research Center for Dermatologic and Immunologic Diseases (Ministry of Science & Technology) MOE Key Laboratory of Rheumatology and Clinical Immunology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Beijing 100730 China Academy for Advanced Interdisciplinary Studies (AAIS)and Sciences Joint Graduate Program (PTN) Peking University Beijing China Department of Oral and Maxillofacial & Head and Neck Oncology Beijing Stomatological Hospital Capital Medical University Beijing China Centre for Genomics and Oncological Research (GENYO) Avenida de La Ilustración 114 Granada 18016 Spain Department of Biochemistry and Molecular Biology II Faculty of Pharmacy University of Granada Granada Spain Instituto de Investigación Biosanitaria Ibs.GRANADA Hospital Virgen de Las Nieves Granada Spain The Center for Regeneration Aging and Chronic Diseases School of Basic Medical Sciences Tsinghua University Beijing China

Background: Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications. Results: We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients’ plasma that enable cancer detection and are informative of cancer types and metastasis. Conclusions: Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy. © The Author(s) 2025.

关键词： Cell-free RNA CfRNA fragment Liquid biopsy biomarker Noninvasive cancer detection Peak calling SncRNA Tissue-of-origin

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Towards High Generalization Performance on Electrocardiogram Classification

Towards High Generalization Performance on Electrocardiogram...

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2021 Computing in Cardiology, CinC 2021

作者： Han, Hyeongrok Park, Seongjae Min, Seonwoo Choi, Hyun-Soo Kim, Eunji Kim, Hyunki Park, Sangha Kim, Jinkook Park, Junsang An, Junho Lee, Kwanglo Jeong, Wonsun Chon, Sangil Ha, Kwonwoo Han, Myungkyu Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of HUINNO Co. Ltd Seoul Korea Republic of LG AI Research Seoul Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of Department of Biological Sciences Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665479165

Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. The characteristics of ECG vary from dataset to dataset for various reasons (i.e., hospital, race, etc.). Therefore, it is important for a model to have high generalization performance consistently over all datasets. In this paper, as part of the PhysioNet / Computing in Cardiology Challenge 2021, we present a model developed to classify cardiac abnormalities from 12 lead and reduced-lead ECGs. In particular, to upgrade our previous model for improving generalization performance, we newly adopt constant-weighted cross-entropy loss, additional features, Mixup augmentation, and squeeze/excitation block, OneCycle learning rate scheduler, which are selected via evaluation of generalization performance using leave-one-dataset-out cross-validation setting. With the present model, our DSAILSNU team has received challenge scores of 0.55, 0.58, 0.58, 0.57 and 0.57 (ranked 2nd, 1st, 1st, 2nd, 2nd out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set, respectively. The present model achieves higher generalization performance over all versions of the hidden test set than the model submitted last year. © 2021 Creative Commons.

关键词： Deep learning

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Computational approach to identify enzymes that are potential therapeutic candidates for psoriasis

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Enzyme research 2011年第1期2011卷 826784-826784页

作者： Park, Daeui Jeong, Hyoung Oh Kim, Byoung-Chul Ha, Young Mi Young Chung, Hae Interdisciplinary Research Program of Bioinformatics and Longevity Science Pusan National University Kumjeong-Gu Busan 609-735 South Korea Molecular Inflammation Research Center for Aging Intervention (MRCA) College of Pharmacy Pusan National University Kumjeong-Gu Busan 609-735 South Korea

Psoriasis is well known as a chronic inflammatory dermatosis. The disease affects persons of all ages and is a burden worldwide. Psoriasis is associated with various diseases such as arthritis. The disease is characterized by well-demarcated lesions on the skin of the elbows and knees. Various genetic and environmental factors are related to the pathogenesis of psoriasis. In order to identify enzymes that are potential therapeutic targets for psoriasis, we utilized a computational approach, combining microarray analysis and protein interaction prediction. We found 6,437 genes (3,264 upregulated and 3,173 downregulated) that have significant differences in expression between regions with and without lesions in psoriasis patients. We identified potential candidates through protein-protein interaction predictions made using various protein interaction resources. By analyzing the hub protein of the networks with metrics such as degree and centrality, we detected 32 potential therapeutic candidates. After filtering these candidates through the ENZYME nomenclature database, we selected 5 enzymes: DNA helicase (RUVBL2), proteasome endopeptidase complex (PSMA2), nonspecific protein-tyrosine kinase (ZAP70), I-kappa-B kinase (IKBKE), and receptor protein-tyrosine kinase (EGFR). We adopted a computational approach to detect potential therapeutic targets;this approach may become an effective strategy for the discovery of new drug targets for psoriasis. © 2011 Daeui Park et al.

关键词： MICROBIAL enzymes PSORIASIS SKIN diseases PROTEIN-protein interactions PROTEIN microarrays BIOCHEMISTRY

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A probabilistic model for pathway-guided gene set selection

A probabilistic model for pathway-guided gene set selection

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Kim, Inyoung Lee, Sangseon Kim, Youngkuk Namkoong, Hugh Kim, Sun Artificial Intelligence Institute Seoul National University Seoul Korea Republic of Institute of Computer Technology Seoul National University Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of Institute of Engineering Research Seoul National University Seoul Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665401265

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation. © 2021 IEEE.

关键词： Gene expression

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S100A4 released from highly bone-metastatic breast cancer cells plays a critical role in osteolysis

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Bone research 2019年第4期7卷 424-436页

作者： Haemin Kim Bongjun Kim Sang Il Kim Hyung Joon Kim Brian YRyu Junho Chung Zang Hee Lee Hong-Hee Kim Department of Cell and Developmental Biology BK21 Program and DRISeoul National UniversitySeoulKorea Department of Biochemistry and Molecular Biology Seoul National University College of MedicineSeoulKorea Cancer Research Institute Seoul National University College of MedicineSeoulKorea Department of Cancer Biology Seoul National University College of MedicineSeoulKorea Department of Oral Physiology BK21 PLUS Projectand Dental and Life Science InstituteSchool of DentistryPusan National UniversityYangsanKorea Interdisciplinary Program in Bioinformatics Seoul National University College of Natural SciencesSeoulKorea Department of Biomedical Sciences Seoul National University Graduate SchoolSeoulKorea

Bone destruction induced by breast cancer metastasis causes severe complications,including death,in breast cancer *** between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element that drives tumor progression and ***-derived factors play fundamental roles in this form of *** identify soluble factors released from cancer cells in bone metastasis,we established a highly bone-metastatic subline of MDA-MB-231 breast cancer *** subline(mtMDA)showed a markedly elevated ability to secrete S100A4 protein,which directly stimulated osteoclast formation via surface receptor *** S100A4 stimulated osteoclastogenesis in vitro and bone loss in *** medium from mtMDA cells in which S100A4 was knocked down had a reduced ability to stimulate ***,the S100A4 knockdown cells elicited less bone destruction in mice than the control knockdown *** addition,administration of an anti-S100A4 monoclonal antibody(mAb)that we developed attenuated the stimulation of osteoclastogenesis and bone loss by mtMDA in *** together,our results suggest that S100A4 released from breast cancer cells is an important player in the osteolysis caused by breast cancer bone metastasis.

关键词： S100A4 breast metastatic

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BioBERT: A pre-trained biomedical language representation model for biomedical text mining

arXiv

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arXiv 2019年

作者： Lee, Jinhyuk Yoon, Wonjin Kim, Sungdong Kim, Donghyeon Kim, Sunkyu So, Chan Ho Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Clova AI Research Naver Corp Seong-Nam13561 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation: We make the pre-trained weights of BioBERT freely available at https://github. com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://***/dmis-lab/ biobert. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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TargetNet: Functional microRNA target prediction with deep neural networks

arXiv

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arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

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