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Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

Neural Regeneration Research 2025年第11期20卷 3215-3216页

作者： Zhenghua Xu Miao Yu Yuhang Song School of Health Sciences and Biomedical Engineering Hebei University of TechnologyTianjinChina Department of Computer Science University of OxfordOxfordUK

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human *** enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

关键词： assignment learning enable

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 167页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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Improvement of Prior Image for Metal Artifact Reduction of Computed Tomography

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Journal of Shanghai Jiaotong University (science) 2023年 1-6页

作者： Sun, Wenwu Zhuang, Tiange Chen, Siping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shenzhen University Guangdong Shenzhen518060 China

It is not easy to reduce the metal artifacts of computed tomography images. However, the pixel values inside the metal artifact regions vary smoothly, while those on the borders of the metal and the bone regions vary sharply. When the Canny operation by adaptive thresholding is conducted on the raw image, the almost continuous edges can be formed obviously on the borders of the metal and the bone regions, but this kind of information cannot be formed for the metal artifact regions. In this paper, by searching the closed areas formed by the border edges of the bone regions in the Canny image, the metal artifact regions, which are very difficult to discriminate only by intensity thresholding, can be excluded effectively. A novel prior image-based method is thus developed for metal artifact reduction. The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image. The method is suitable for the clinical application. © 2023, Shanghai Jiao Tong University.

关键词： computerized tomography

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A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

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computer Methods in Biomechanics and biomedical engineering 2025年 1-13页

作者： Zhang, Shuyu Li, Zhiyu Peng, Weili Chen, Yuanyuan Wu, Yao Machine Intelligence Lab College of Computer Science Sichuan University Chengdu China National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu China

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liver disease deep learning mass spectrometry pre-processing

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A positive and unlabeled learning framework based on extreme learning machine for drug-drug interactions discovery

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 1-12页

作者： Bi, Xin Ma, He Li, Jianhua Ma, Yuliang Chen, Deyang Sino-Dutch Biomedical and Information Engineering School Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China

Drug-drug interactions (DDIs) lead to Adverse Drug Reactions (ADRs) in most cases, which increase medical costs tremendously, and may cause medical negligence or even fatal accidents. Many researchers introduce binary classification methods into DDIs discovery task to avoid expensive and inefficient experimental trials. However, we find that drug-drug pairs without DDI labels cannot be simply viewed as negative samples in practice, since unobserved DDIs may be discovered from unlabeled pairs. Therefore, different from traditional positive-negative classification, in this paper, we treat DDIs discovery as a positive-unlabeled learning (PU learning) problem, in which each drug-drug pair is either a positive sample (labeled as DDI) or an unlabeled one (labeled as non-DDI). We propose a PU learning framework based on Extreme Learning Machine (ELM) named PU-ELM, which consists of two components, namely reliable negative extraction and classifier learning. To improve the quality of reliable negative set, we propose a negative extraction method named OC-ELM-RN (One-Class ELM for Reliable Negative extraction). As to the learning module, we first apply the semi-supervised learning strategy in PU-ELM (denoted as PU-ELM-SS) to achieve extremely fast learning speed, and then we propose an entropy based weighted learning method named PU-ELM-EW (PU-ELM with Entropy Weighted learning) to improve the PU learning performance. Extensive experiments are conducted on a synthetic dataset with varied settings. We also apply PU-ELM to a real-world drug dataset. The results indicate that PU-ELM achieves better DDIs discovery ability compared with state-of-the-art methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Extraction

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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Pharos: A Transparent and Steerable Visualization Recommendation System

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Journal of Computing science and engineering 2023年第1期17卷 1-12页

作者： Chang, Youli L’Yi, Sehi Kim, Young Taek Song, Hyunjoo Kim, Bohyoung Seo, Jinwook Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Department of Biomedical Informatics Harvard Medical School BostonMA United States School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Division of Biomedical Engineering Hankuk University of Foreign Studies - Global Campus Yongin Korea Republic of

We propose Pharos, a novel visualization recommendation system that makes use of several provenance data sources and multi-perspective overviews to boost the transparency and steerability of the recommendation engine. Pharos helps a user understand recommendation contexts along with the user’s analysis progress in three complementary overviews. Pharos also serves categorized and scalable recommendations, either expanding or narrowing a user’s analysis scope. Based on provenance data and explicit user annotations (i.e., bookmarked or excluded visualizations), Pharos dynamically updates a recommendation list. According to the provided context, a user can steer the recommendation direction by filtering recommended candidates and rearranging them via the weight controller of the similarity measure on the recommendation engine. We showed how Pharos helped users understand and steer visualization recommendations through two comparative user studies. © 2023. The Korean Institute of Information Scientists and Engineers

关键词： Recommender systems

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

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RBA: A Detection Method for Offensive Language in Chinese 24

RBA: A Detection Method for Offensive Language in Chinese

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2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Wang, Linya Mou, Shiyu Liu, Peize School of Computer Science and Engineering Southwest Minzu University China Engineering Faculty Biomedical Engineering Department University of Malaya Malaysia

ISBN: (纸本)9798400709876

As social media continues to gain popularity, offensive language on the Internet is on the rise, and how to effectively detect it has become a hot spot in the academic community. Due to the strong context-dependence of Chinese offensive text, the feature information is challenging to extract, and the training is susceptible to noise interference. Therefore, we propose a Chinese offensive language detection model integrating RoBERTa-base-cold, BiLSTM and adversarial training. The model generates dynamic word vectors containing offensive features by segmenting the text with RoBERTa-base-cold, then adds noise perturbations to these output vectors to generate adversarial samples and sends these adversarial samples to BiLSTM to enhance model robustness through adversarial training in the face of noise interference. Experiments are conducted on the COLDataset dataset, and the results show that this method is more effective than other modelling methods in detecting Chinese offensive speech. © 2024 ACM.

关键词： Speech recognition

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