检索结果-内蒙古大学图书馆

NetGO 3.0:Protein Language Model Improves Large-scale Functional Annotations

Genomics, Proteomics & bioinformatics 2023年第2期21卷 349-358页

作者： Shaojun Wang Ronghui You Yunjia Liu Yi Xiong Shanfeng Zhu Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China School of Life Sciences Fudan UniversityShanghai 200433China Department of Bioinformatics and Biostatistics Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Artificial Intelligence Laboratory Shanghai 200232China Shanghai Qi Zhi Institute Shanghai 200030China MOEKey Laboratoryof Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai 200433China Shanghai Key Laboratory of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm Fudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Shanghai 200433China

As one of the state-of-the-art automated function prediction(AFP)methods,NetGO 2.0 integrates multi-source information to improve the ***,it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated ***,protein language models have been proposed to learn informative representations[e.g.,Evolutionary Scale Modeling(ESM)-1b embedding] from protein sequences based on ***,we represented each protein by ESM-1b and used logistic regression(LR)to train a new model,LR-ESM,for *** experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO ***,by incorporating LR-ESM into NetGO 2.0,we developed NetGO 3.0 to improve the performance of AFP extensively.

关键词： Protein function prediction Web service Protein language model Learning to rank Large-scale multi-label learning

来源：评论

学校读者我要写书评

暂无评论

A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility

arXiv

引用

arXiv 2024年

作者： Batool, Muniba Azam, Naveed Ahmed Zhu, Jianshen Haraguchi, Kazuya Zhao, Liang Akutsu, Tatsuya Discrete Mathematics and Computational Intelligence Laboratory Department of Mathematics Quaid-i-Azam University Pakistan Discrete Mathematics Laboratory Department of Applied Mathematics and Physics Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Kyoto University Kyoto606-8306 Japan Bioinformatics Center Institute for Chemical Research Kyoto University Uji611-0011 Japan

Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors, multiple linear regression (MLR) and mixed integer linear programming (MILP). Selected descriptors based on a forward stepwise procedure enabled the simplest regression model, MLR, to achieve significantly good prediction accuracy compared to the existing approaches, achieving the accuracy in the range [0.7191, 0.9377] for 29 diverse datasets. By simulating these descriptors and learning models as MILPs, we inferred mathematically exact and optimal compounds with the desired AS, prescribed structures, and up to 50 non-hydrogen atoms in a reasonable time range [6, 1204] seconds. These findings indicate a strong correlation between the simple graph-theoretic descriptors and the AS of compounds, potentially leading to a deeper understanding of their AS without relying on widely used complicated chemical descriptors and complex machine learning models that are computationally expensive, and therefore difficult to use for inference. An implementation of the proposed approach is available at https://***/ku-dml/mol-infer/tree/master/AqSol. © 2024, CC BY.

关键词： Solubility

来源：评论

学校读者我要写书评

暂无评论

EEG-Based Emotion Recognition Using Convolutional Neural Network with Functional Connections 5th

EEG-Based Emotion Recognition Using Convolutional Neural Net...

引用

5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wang, Hongbo Liu, Ke Qi, Feifei Deng, Xin Li, Peiyang Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Internet Finance and Information Engineering Guangdong University of Finance Guangzhou510521 China School of Bioinformatics Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9789811623356

Emotion recognition plays a vital role in Brain-Computer Interaction. To extract and employ the inherent information implied by functional connections among EEG electrodes, we propose a multichannel EEG emotion recognition method using convolutional neural network (CNN) with functional connectivity as input. Specifically, the phase synchronization indices are employed to compute the EEG functional connectivity matrices. Then a CNN is proposed to effectively extract the classification information of these functional connections. The experimental results based on the DEAP and SEED datasets validate the superior performance of the proposed method, compared with the input of raw EEG data. The code of the proposed model is available at https://***/deep-bci/ERBCPSI. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

TP-ML: A Machine-Learning-Based Tool to Identify Threonine Proteases Using Sequence-Derived Optimal Features

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on computational Biology and bioinformatics 2025年第3期22卷 984-996页

作者： Ahmad Firoz Adeel Malik Nitin Mahajan Le Thi Phan Hani S. H. Mohammed Ali Chang-Bae Kim Balachandran Manavalan Department of Biological Sciences Faculty of Science King Abdulaziz University Jeddah Saudi Arabia Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology King Abdulaziz University Jeddah Saudi Arabia Institute of Intelligence Informatics Technology Sangmyung University Seoul South Korea Wugen St. Louis MO USA Computational Biology and Bioinformatics Laboratory College of Biotechnology and Bioengineering Department of Integrative Biotechnology Sungkyunkwan University Suwon South Korea Department of Biotechnology Sangmyung University Seoul South Korea

Threonine proteases (TPs) are enzymes vital for several biological processes and diseases including Alzheimer’s disease and cancer. Their potential to target and degrade proteins intracellularly makes them valuable for various therapeutic and industrial applications. However, traditional experimental methods for identifying and characterizing novel TPs are exhaustive, time-consuming, and expensive. To address this, we developed TP-ML, a support vector machine-based prediction tool that can differentiate TP from non-TP sequences. We generated a benchmark dataset and calculated the physicochemical and compositional features using primary amino acid sequences. Subsequently, a comparison was made between the two feature selection approaches to identify the optimal feature sets from the original encodings. These optimal features were then used to train five different machine-learning classifiers, each assessed independently. TP-ML was selected as the best model showing consistent performance during cross-validation and independent evaluation, and achieved an accuracy of 0.934 and 0.888, respectively. We anticipate TP-ML to be a powerful tool for identifying TPs, aiding in their experimental characterization and industrial application exploration.

关键词： Enzymes Protein sequence Biotechnology bioinformatics computational biology Encoding Training Peptides Electronic mail Biological system modeling

来源：评论

学校读者我要写书评

暂无评论

Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

引用

29th International Conference on Research in computational Molecular Biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Inference for Camera Calibration in Light Microscopy Under Circular Motion

Probabilistic Inference for Camera Calibration in Light Micr...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yuanhao Guo Fons J. Verbeek Ge Yang Computational Biology and Machine Intelligence National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences LIACS Bioinformatics and Imaging Leiden University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address these issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.

关键词： Cameras Three-dimensional displays Calibration Probabilistic logic Microscopy Solid modeling

来源：评论

学校读者我要写书评

暂无评论

PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

引用

Journal of Molecular Biology 2025年第15期437卷 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein Data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. Data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Protein Folding Prediction based on Long Short-Term Memory Networks: A Preliminary Investigation and Analysis

A Novel Approach to Protein Folding Prediction based on Long...

引用

2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Hattori, Leandro Takeshi Benitez, Cesar Manuel Vargas Gutoski, Matheus Aquino, Nelson Marcelo Romero Lopes, Heitor Silverio Bioinformatics and Computational Intelligence Laboratory Federal University of Technology-Paraná Curitiba Brazil

ISBN: (纸本)9781509060146

The Protein Folding Problem (PFP) is considered one of the most important open challenges in Biology and bioinformatics. Long Short-Term Memory (LSTM) methods have risen recently, achieving the state-of-art performance for several bioinformatics problems such as, protein secondary and tertiary protein structure prediction. This paper describes the application of a novel approach based on the LSTM networks to the PFP using a coarse-grained model of proteins. An specific encoding scheme for representing protein folding states is also presented. The proposed approach was evaluated by means of several experiments with a dataset of protein folding, which was obtained by Molecular Dynamics simulations. We also propose a novel method for evaluating the performance of the approach based on measures used in bioinformatics. Furthermore, a new analysis method for protein folding pathways is presented. Results suggest that the proposed approach is able to learn the protein fold transitions. Also, it is promising for the research areas related to bioinformatics and computational intelligence. © 2018 IEEE.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

引用

Clinical Breast Cancer 2025年

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：