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multi-source transfer learning with Graph Neural Network for excellent modelling the bioactivities of ligands targeting orphan G protein-coupled receptors

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 2588-2608页

作者： Huang, Shizhen Zheng, ShaoDong Chen, Ruiqi Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350116 Peoples R China Nanjing Renmian Integrated Circuit Co Ltd VeriMake Innovat Lab Nanjing 210088 Peoples R China

G protein-coupled receptors (GPCRs) have been the targets for more than 40% of the currently approved drugs. Although neural networks can effectively improve the accuracy of prediction with the biological activity, the result is undesirable in the limited orphan GPCRs (oGPCRs) datasets. To this end, we proposed multi-source transfer learning with Graph Neural Network, called MSTL-GNN, to bridge this gap. Firstly, there are three ideal sources of data for transfer learning, oGPCRs, experimentally validated GPCRs, and invalidated GPCRs similar to the former one. Secondly, the SIMLEs format GPCRs convert to graphics, and they can be the input of Graph Neural Network (GNN) and ensemble learning for improving prediction accuracy. Finally, our experiments show that MSTL-GNN remarkably improves the prediction of GPCRs ligand activity value compared with previous studies. On average, the two evaluation indexes we adopted, R2 and Root-mean-square deviation (RMSE). Compared with the state-of-the-art work MSTL-GNN increased up to 67.13% and 17.22%, respectively. The effectiveness of MSTL-GNN in the field of GPCR Drug discovery with limited data also paves the way for other similar application scenarios.

关键词： G protein-coupled receptors (GPCRs) multi-source transfer learning Graph Neural Network biological activity

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Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第6期62卷 1655-1672页

作者： Zhang, Fan Wu, Hanliang Guo, Yuxin Jinan Univ Guangzhou Peoples R China Guangzhou Liwan Dist Orthoped Hosp Guangzhou Peoples R China Guangzhou Inst Sci & Technol Guangzhou 510540 Peoples R China

Electroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects' motor imagery activities;this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usually time-consuming and labor-intensive, which makes it difficult to obtain sufficient labeled data from the new subject to train a new model. Moreover, the EEG signals of different individuals exhibit significant differences, leading to a significant drop in the performance of a model trained on the existing subjects when directly classifying EEG signals acquired from new subjects. Therefore, it is crucial to make full use of the EEG data of the existing subjects and the unlabeled EEG data of the new target subject to improve the MI classification performance achieved for the target subject. This research study proposes a semi-supervised multi-source transfer (SSMT) learning model to address the above problems;the model learns informative and domain-invariant representations to address cross-subject MI-EEG classification tasks. In particular, a dynamic transferred weighting schema is presented to obtain the final predictions by integrating the weighted features derived from multi-source domains. The average accuracies achieved on two publicly available EEG datasets reach 83.57% and 85.09%, respectively, validating the effectiveness of the SSMT process. The SSMT process reveals the importance of informative and domain-invariant representations in MI classification tasks, as they make full use of the domain-invariant information acquired from each subject.

关键词： Brain-computer interfaces Electroencephalogram Motor imagery multi-source transfer learning Semi-supervised learning Dynamic weighting

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Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 249卷

作者： Song, Dengwei Cheng, Yujie Zhou, An Lu, Chen Chong, Jin Ma, Jian Beihang Univ Inst Reliabil Engn Beijing Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing Peoples R China Sci & Technol Reliabil & Environm Engn Lab Beijing Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Contemporary Amperex Technol Co Ltd Ningde Fujian Peoples R China

Achieving highly accurate predictions based on less data with multiple formulations has become a significant challenge. Unlike the traditional prediction model that ignores the similarities and differences between multiformula batteries, the proposed multi-source transfer RUL prediction is an effective way to jointly transfer several samples and merge degradation information from multi-source domains, thereby taking full advantage of the information. Aiming at the problem of negative transfer and insufficient transfer when multi-source domains are available, dynamic time warping is employed to adaptively select transferable samples, ignoring the local individual differences. The least absolute shrinkage and selection operator are utilized to generate an optimal subset and allocate personalized weights. Then, an optimization strategy for the cycle life tests is proposed by automatically giving a reasonable threshold of stopping the test. The proposed method is verified based on an actual dataset with multiple formulations collected under different test conditions from a battery manufacturer. Experimental results show that the proposed method has high accuracy based on even a small amount of data. The systematic optimization strategy can significantly reduce the time and cost of the test.

关键词： Lithium-ion power battery multiple formulations Remaining useful life prediction Cycle life test optimization multi-source transfer learning

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multi-source transfer learning for Signal Detection over a Fading Channel with Co-channel Interference

Multi-source Transfer Learning for Signal Detection over a F...

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IEEE International Conference on Communications (ICC)

作者： Zheng, Ziyan Tong, Xinyi Yu, Xinchun Xu, Xiangxiang Huang, Shao-Lun Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China MIT Dept Elect Engn & Comp Sci Cambridge MA USA

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

For signal detection tasks in wireless communications, most of the existing algorithms either ignore the co-channel interference or treat it as Gaussian noise, which may result in unsatisfactory accuracy when the interference is non-negligible and complex-distributed. When neural networks are motivated in the design of the detectors, difficulty arises in the training due to the fact that there are few accessible pilots in each packet. In this paper, we consider a data-driven detector based on multi-source transfer learning (MSTL) for signal detection in a fading channel with interference. The MSTL detector transfers channel knowledge of previous packets into the latest detection. In particular, we consider a linear combination of the pilots and historical symbols in the distribution space, and design the optimal combination coefficients based on the number of those symbols as well as distributions similarity. Numerical simulations on a Gauss-Markov flat Rayleigh fading channel with co-channel interference validate the advantages of our algorithms, compared with several existing training schemes including directly applying the fully connected deep neural network (FCDNN) and conventional linear minimum mean square error (LMMSE) detector.

关键词： Signal detection fading channel co-channel interference multi-source transfer learning neural network

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multi-source transfer learning Via multi-Kernel Support Vector Machine Plus for B-Mode Ultrasound-Based Computer-Aided Diagnosis of Liver Cancers

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第10期25卷 3874-3885页

作者： Zhang, Huili Guo, Lehang Wang, Dan Wang, Jun Bao, Lili Ying, Shihui Xu, Huixiong Shi, Jun Shanghai Univ Shanghai Inst Adv Commun & Data Sci Joint Int Res Lab Specialty Fiber Opt & Adv Commu Sch Commun & Informat EngnKey Lab Specialty Fibe Shanghai 200444 Peoples R China Shanghai Univ Sch Sci Dept Math Shanghai 200444 Peoples R China Tongji Univ Sch Med & Tumor Minimally Invas Treatment Ctr Ctr Ultrasound Diag & TreatmentCanc Ctr Dept Med UltrasoundShanghai Peoples Hosp 10 Shanghai 200072 Peoples R China

B-mode ultrasound (BUS) imaging is a routine tool for diagnosis of liver cancers, while contrast-enhanced ultrasound (CEUS) provides additional information to BUS on the local tissue vascularization and perfusion to promote diagnostic accuracy. In this work, we propose to improve the BUS-based computer aided diagnosis for liver cancers by transferring knowledge from the multi-view CEUS images, including the arterial phase, portal venous phase, and delayed phase, respectively. To make full use of the shared labels of paired of BUS and CEUS images to guide knowledge transfer, support vector machine plus (SVM+), a specifically designed transfer learning (TL) classifier for paired data with shared labels, is adopted for this supervised TL. A nonparallel hyperplane based SVM+ (NHSVM+) is first proposed to improve the TL performance by transferring the per-class knowledge from source domain to the corresponding target domain. Moreover, to handle the issue of multi-source TL, a multi-kernel learning based NHSVM+ (MKL-NHSVM+) algorithm is further developed to effectively transfer multi-source knowledge from multi-view CEUS images. The experimental results indicate that the proposed MKL-NHSVM+ outperforms all the compared algorithms for diagnosis of liver cancers, whose mean classification accuracy, sensitivity, and specificity are 88.18 +/- 3.16 %, 86.98 +/- 4.77 %, and 89.42 +/- 3.77%, respectively.

关键词： Cancer Liver Ultrasonic imaging Task analysis Support vector machines Kernel Imaging Nonparallel hyperplane support vector machine plus multiple kernel learning multi-source transfer learning ultrasound imaging liver cancer

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AG-MSTLN-EL: A multi-source transfer learning Approach to Brain Tumor Detection

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025年第1期38卷 245-261页

作者： Biradar, Shivaprasad Virupakshappa Sharnbasva Univ Dept Comp Sci & Engn Kalaburagi Karnataka India

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

关键词： Brain tumor multi-source transfer learning Visual Geometry Group ResNet GoogLeNet Ensemble learning Attention mechanism Support vector machine Magnetic resonance imaging

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multi-source transfer learning Based on the Power Set Framework

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 103-103页

作者： Song, Bingbing Pan, Jianhan Qu, Qiaoli Li, Zexin Jiangsu Normal Univ Xuzhou 221116 Peoples R China

transfer learning is a great technology that can leverage knowledge from label-rich domains to address problems in similar domains that lack labeled data. Most previous works focus on single-source transfer, assuming the source domain contains sufficient labeled data and is close to the target domain. However, in practical applications, this assumption is hardly met, and labeled data exist in different domains. To improve the adaptability of transfer learning models for multi-source scenarios, many existing methods utilize the commonality and specificity across source domains. They either map all source domains with the target domain into a common feature space for knowledge transfer or combine multiple classifiers trained on pairs of each source and target to form a target classifier. However, the correlations across multiple source domains that can bring significant impacts on learning performance are ignored. In light of this, we propose a novel multi-source transfer learning method based on the power set framework (PSF-MSTL). First, PSF-MSTL constructs a power set framework that enables different source domains to be interrelated. Second, PSF-MSTL makes the source-domain framework integral and able to provide complementary knowledge using a dual-promotion strategy. Additionally, PSF-MSTL is formulated as an optimization problem, and an iterative algorithm is presented to address it. Finally, we conduct extensive experiments to show that PSF-MSTL can outperform many advanced multi-source transfer learning methods.

关键词： multi-source transfer learning Power set framework Non-negative matrix tri-factorization Knowledge fusion

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AN OBJECT-LEVEL multi-source transfer learning METHOD INTEGRATING OPTICAL AND SAR FEATURES: A CASE STUDY OF GAOFEN, ZIYUAN, AND SENTINEL-1 SATELLITES

AN OBJECT-LEVEL MULTI-SOURCE TRANSFER LEARNING METHOD INTEGR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Qin, Xingli Wu, Bingfang Zeng, Hongwei Zhang, Miao Tian, Fuyou Cao, Yupei Liu, Yazhou Chinese Acad Sci Aerosp Informat Res Inst Key Lab Remote Sensing & Digital Earth Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Shandong Land Grp Digital Technol Co Ltd Zaozhuang 277000 Peoples R China

ISBN: (纸本)9798350360332;9798350360325

In remote sensing earth observation applications, there is often a trade-off between automated processing and improved observation accuracy. A key issue in this contradiction is the underutilization of rich knowledge embedded in historical remote sensing data. To address this, this paper proposes an object-level multi-source transfer learning method (OMSTL) that effectively leverages the knowledge in historical remote sensing data for unsupervised land object discrimination. This method effectively integrates multi-source heterogeneous features and represents land object knowledge, enabling reliable knowledge transfer. multi-source transfer learning utilizes knowledge from remote sensing images at different times and locations, significantly enhancing transfer stability. The method's effectiveness and potential were demonstrated through experiments using five optical remote sensing images (GF-1B, GF-1D, GF-6, GF-7, ZY-3) and their corresponding SAR images (Sentinel-1).

关键词： Unsupervised Classification Domain Adaptation multi-source transfer learning Object-Based Image Analysis Remote Sensing Big Data

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multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 162卷 108095-108095页

作者： Yang, Bin Xu, Songci Lei, Yaguo Lee, Chi-Guhn Stewart, Edward Roberts, Clive Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian 710049 Peoples R China Univ Toronto Ctr Maintenance Optimizat & Reliabil Engn Toronto ON M5S 3G8 Canada Univ Birmingham Ctr Railway Res & Educ Birmingham B15 2TT W Midlands England

Most of the current successes of deep transfer learning-based fault diagnosis require two assumptions: 1) the health state set of source machines should overlap that of target machines;2) the number of target machine samples is balanced across health states. However, such assumptions are unrealistic in engineering scenarios, where target machines suffer from fault types that are not seen in source machines and the target machines are mostly in a healthy state with only occasional faults. As a result, the diagnostic knowledge from source machines may not cover all fault types of target machines nor address imbalanced target samples. Therefore, we propose a framework, called a multi-source transfer learning network (MSTLN), to aggregate and transfer diagnostic knowledge from multiple source machines by combining multiple partial distribution adaptation sub-networks (PDA-Subnets) and a multi-source diagnostic knowledge fusion module. The former weights target samples by counter-balancing factors to jointly adapt partial distributions of source and target pairs, and the latter releases negative effects due to discrepancy among multiple source machines and further fuses diagnostic decisions output from multiple PDA-Subnets. Two case studies demonstrate that MSTLN can reduce the misdiagnosis rate and obtain better transfer performance for imbalanced target samples than other conventional methods.

关键词： Intelligent fault diagnosis Rotating machines multi-source transfer learning Deep transfer learning Partial domain adaptation

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multi-source transfer learning of time series in cyclical manufacturing

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第3期31卷 777-787页

作者： Zellinger, Werner Grubinger, Thomas Zwick, Michael Lughofer, Edwin Schoener, Holger Natschlaeger, Thomas Saminger-Platz, Susanne Johannes Kepler Univ Linz Dept Knowledge Based Math Syst Linz Austria Software Competence Ctr Hagenberg GmbH Hagenberg Im Muhlkreis Austria

This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions. [GRAPHICS] .

关键词： transfer learning multi-source transfer learning Regression Domain generalization Domain adaptation

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