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Advances and applications of machine learning and intelligent optimization algorithms in genome‑scale metabolic network models

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Systems Microbiology and Biomanufacturing 2023年第2期3卷 193-206页

作者： Lidan Bai Qi You Chenyang Zhang Jun Sun Long Liu Hengyang Lu Qidong Chen Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence WuxiJiangsuChina Key Laboratory of Carbohydrate Chemistry and Biotechnology Ministry of EducationJiangnan UniversityWuxi 214122China Science Center for Future Foods Jiangnan UniversityWuxi 214122China

Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired ***,with the gradual cross-fertilization between computer science and bioinformatics fields,machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models(GSMMs)based on constrained optimization methods,and many high-quality related works have been ***,this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering,with special emphasis on ***,the development history of GSMMs is first ***,the analysis methods of GSMMs based on constraint optimization are ***,this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic *** addition,the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

关键词： Genome-scale metabolic models machine learning Intelligent optimization Metabolic engineering

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Tumor Micro-Environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-Slide Pathological Images

Tumor Micro-Environment Interactions Guided Graph Learning f...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Wei Shao YangYang Shi Daoqiang Zhang JunJie Zhou Peng Wan Ministry of Education College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However, most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs, which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact, tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME), and the detailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper, we considered the spatial interactions among tumor and its two major TME components (i.e., lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically, we firstly selected different types of patches as nodes to build graph for each WSI. Then, a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally, a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models, but also has good explainable ability for survival prediction.

关键词： Pathology Analytical models Organizations Predictive models Semisupervised learning Prediction algorithms pattern recognition

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Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing

arXiv

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arXiv 2025年

作者： Yuan, Fan Fang, Xiaoyuan Quan, Rong Li, Jing Bi, Wei Xu, Xiaogang Li, Piji College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Department of Computing The Hong Kong Polytechnic University Hong Kong The Chinese University of Hong Kong Hong Kong

Visual Commonsense Reasoning, which is regarded as one challenging task to pursue advanced visual scene comprehension, has been used to diagnose the reasoning ability of AI systems. However, reliable reasoning requires a good grasp of the scene’s details. Existing work fails to effectively exploit the real-world object relationship information present within the scene, and instead overly relies on knowledge from training memory. Based on these observations, we propose a novel scene-graph-enhanced visual commonsense reasoning generation method named G2, which first utilizes the image patches and LLMs to construct a location-free scene graph, and then answer and explain based on the scene graph’s information. We also propose automatic scene graph filtering and selection strategies to absorb valuable scene graph information during training. Extensive experiments are conducted on the tasks and datasets of scene graph constructing and visual commonsense answering and explaining, respectively. Experimental results and ablation analysis demonstrate the effectiveness of our proposed framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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Pathformer3D: A 3D Scanpath Transformer for 360° Images

arXiv

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arXiv 2024年

作者： Quan, Rong Lai, Yantao Qiu, Mengyu Liang, Dong College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics The Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen China

Scanpath prediction in 360° images can help realize rapid rendering and better user interaction in Virtual/Augmented Reality applications. However, existing scanpath prediction models for 360° images execute scanpath prediction on 2D equirectangular projection plane, which always result in big computation error owing to the 2D plane’s distortion and coordinate discontinuity. In this work, we perform scanpath prediction for 360° images in 3D spherical coordinate system and proposed a novel 3D scanpath Transformer named Pathformer3D. Specifically, a 3D Transformer encoder is first used to extract 3D contextual feature representation for the 360° image. Then, the contextual feature representation and historical fixation information are input into a Transformer decoder to output current time step’s fixation embedding, where the self-attention module is used to imitate the visual working memory mechanism of human visual system and directly model the time dependencies among the fixations. Finally, a 3D Gaussian distribution is learned from each fixation embedding, from which the fixation position can be sampled. Evaluation on four panoramic eye-tracking datasets demonstrates that Pathformer3D outperforms the current state-of-the-art methods. Code is available at https://***/lsztzp/ Pathformer3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Different Regional patterns in Gray Matter-based Age Prediction

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Neuroscience Bulletin 2023年第6期39卷 984-988页

作者： Nianming Zuo Tianyu Hu Hao Liu Jing Sui Yong Liu Tianzi Jiang Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100190China Chinese Institute for Brain Research Beijing102206China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing100190China Key Laboratory for NeuroInformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu625014China Queensland Brain Institute University of QueenslandBrisbaneQLD4072Australia

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional *** the span of adulthood,although the brain has matured from a neurobiological perspective,it is sti... 详细信息

关键词： shaped continuously pattern

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Multimodal Brain Imaging Fusion for the White-Matter Fiber Architecture in the Human Brain

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Neuroscience Bulletin 2022年第5期38卷 561-564页

作者： Ming Song Zhengyi Yang Tianzi Jiang National Laboratory of Pattern Recognition Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China Brainnetome Center Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China University of the Chinese Academy of Sciences Beijing 100049China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China Key Laboratory for Neuroinformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 625014China The Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia

Uncovering white-matter fiber architecture,particularly that of the human brain,is one of the major and most recent interests in the neuroscience community[1].Recently,a novel analysis method for quantifying white-mat... 详细信息

关键词： fiber white resolution

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Consistent Brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes 4th

Consistent Brain Age Difference in Childhood Autism Spectrum...

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4th International Workshop on Human Brain and Artificial intelligence, HBAI 2024

作者： Sun, Fangling Liang, Chuang Shao, Wei Fu, Zening Zhang, Daoqiang Jiang, Rongtao Qi, Shile Calhoun, Vince D. Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Georgia State University Institute of Technology Emory University AtlantaGA United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9789819640003

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and behavior as well as structural abnormalities regarding brain development. The difference between neuroimaging-predicted brain age and the chronological age (predicted age difference;PAD) is a potential biomarker reflecting individual differences in brain developmental trajectories. However, current findings of PAD in ASD are inconsistent due to the biases introduced by different prediction models. Here, 6 classic 3-dimensional convolutional neural network (3D-CNN) models were used to assess brain age to identify consistent and reliable PAD and its interpretable features for ASD. No significant PAD differences between ASD and typically developing controls (TDC) were observed, but PAD differences in younger age and subtypes were found. Occlusion sensitivity analysis showed that default mode network (DMN) and salience network (SAN) drove the atypical brain developmental trajectories in ASD. Our findings were replicable across 6 CNN models, showing the promise of neuroimaging targets for exploring atypical developmental patterns in ASD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Convolutional Neural Network-Based Multimodal Image Information Fusion for Moisture Damage Assessment of Cultural Heritage Buildings

SSRN

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SSRN 2024年

作者： Wang, Fuzhi Huang, Jizhong Fu, Yu Department of Civil Engineering Shanghai University Shanghai200444 China Institute for the Conservation of Cultural Heritage Shanghai University Shanghai200444 China Key Laboratory of Silicate Cultural Relics Conservation Shanghai University Ministry of Education Shanghai200444 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Water is an essential factor causing the deterioration of historical and cultural heritage, and identifying and quantifying moisture damage is essential for conserving cultural heritage buildings. This study combines infrared thermography (IRT) and microwave technology to investigate moisture-induced diseases and the variation of the spatial distribution of water content in the Yungang Grottoes of China, a World Heritage Site. Information on building humidity distribution was extracted and combined from IRT and microwave information. Furthermore, multi-feature image fusion combines different types of monitoring information through the Convolutional Neural Network (CNN), which is used to diagnose moisture distribution and content accurately. The features obtained from different non-destructive methods were then jointly analyzed using statistical analysis methods such as absolute contrast, noise ratio (SNR), and spatial stability to quantify the artifact moisture defect data, thus improving the accuracy and range of features for detecting water-damaged pathologies. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Precise Modulation Strategies for Transcranial Magnetic Stimulation:Advances and Future Directions

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Neuroscience Bulletin 2021年第12期37卷 1718-1734页

作者： Gangliang Zhong Zhengyi Yang Tianzi Jiang Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Key Laboratory for NeuroInformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China

Transcranial magnetic stimulation(TMS)is a popular modulatory technique for the noninvasive diagnosis and therapy of neurological and psychiatric ***,current modulation strategies are only modestly *** literature provides strong evidence that the modulatory effects of TMS vary depending on device components and stimulation *** differential effects are important when designing precise modulatory strategies for clinical or research *** in TMS have been accompanied by advances in combining TMS with neuroimaging techniques,including electroencephalography,functional nearinfrared spectroscopy,functional magnetic resonance imaging,and positron emission *** studies appear particularly promising as they may not only allow us to probe affected brain areas during TMS but also seem to predict underlying research directions that may enable us to precisely target and remodel impaired cortices or ***,few precise modulation strategies are available,and the long-term safety and efficacy of these strategies need to be ***,we review the literature on possible technologies for precise modulation to highlight progress along with limitations with the goal of suggesting future directions for this field.

关键词： Transcranial magnetic stimulation Modulation strategies Precise stimulation target Coil location Individual treatment paradigm

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