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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular data

ADVISor: Automatic Visualization Answer for Natural-Language...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Can Liu Yun Han Ruike Jiang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Beijing China

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.

关键词： Annotations Natural languages Pipelines Neural networks data visualization Tools data mining

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach.

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Towards efficient local causal structure learning

arXiv

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arXiv 2021年

作者： Yang, Shuai Wang, Hao Yu, Kui Cao, Fuyuan Wu, Xindong The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China The Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100102 China

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

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A study on improved mrmf algorithm on uncertain graph 2nd

A study on improved mrmf algorithm on uncertain graph

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2nd International Cognitive Cities Conference, IC3 2019

作者： Wen, Kejie Yang, Juan Liu, Wei Yang, Yanchun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (纸本)9789811561122

With the increased size and density of graph, the performance of the space decomposition-based algorithm (SDBA) decreases rapidly in terms of the most reliable maximum flow problem (MRMF). To solve this problem, the double filter space decomposition-based algorithm (DF-SDBA) is proposed by applying the probability filter and the cut-set filter. First, among the intervals to be processed in the process of space decomposition, DF-SDBA algorithm uses the probability constraint to filter out the intervals which own smaller probability of the lower boundary distribution than the current maximum flow, reducing the number of iterations effectively. Then the cut-set constraint is applied to unspecified intervals. According to the rule that all the edges in the cut-sets must exist in the upper bound of each interval, the unspecified intervals are filtered out, cutting down the number of partition intervals once again. The experimental results reveal that compared with the SDBA algorithm, the DF-SDBA algorithm can reduce the number of the intervals needed to be divided can obtain better performance. © Springer Nature Singapore Pte Ltd 2020.

关键词： Flow graphs

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Parallel generated method of transcriptional regulatory networks

Parallel generated method of transcriptional regulatory netw...

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作者： Liu, Shuai Ta, Na Lu, Mengye Liu, Gaocheng Bai, Weiling Li, Wenhui College of Computer Science Inner Mongolia University Hohhot010012 China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot010012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China

Generated method of transcriptional regulatory networks remains an important research in biology. Many approaches have been proposed to construct transcriptional regulatory networks. However, with the increase of ChIP-seq and RNA-seq data, the speed of constructing transcriptional regulation networks is still a challenge. Moreover, parallel computing lacks application in constructing gene regulatory networks through the analysis of the relationships between transcription factors (TFs) and target genes (TGs). Therefore, in this paper, a parallel generated method of transcriptional regulatory networks was proposed. First, two datasets, Michigan Cancer Foundation – 7 (MCF-7) and Cardiomyocytes (CM) were applied. Then, a parallel method was used to generate transcriptional regulatory network with their transcription factors (TFs) and target genes (TGs). Finally, experimental results showed that 61% regulatory relations in MCF-7 were validated in the Gene Expression Omnibus (GEO), while 29% results needed further experimental verification. Besides, 56% regulatory relations in CM were consistent with GEO, while 33% results were not yet verified. Furthermore, speed of parallel algorithm was faster than traditional serial algorithm in generating transcriptional regulatory networks. © 2018 John Wiley & Sons, Ltd.

关键词： Transcription factors

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Molecular Determinants of Orthosteric–allosteric Dual Inhibition of PfHT1 by Computational Assessment

arXiv

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arXiv 2025年

作者： Kong, Decheng Ren, Jinlong Li, Zhuang Shan, Guangcun Wang, Zhongjian Zhang, Ruiqin Huang, Wei Dou, Kunpeng College of Physics and Optoelectronic Engineering Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Co. Ltd Chengdu610095 China Department of Physics City University of Hong Kong 999077 Hong Kong Engineering Research Center of Advanced Marine Physical Instruments and Equipment of Education Ministry of China Key Laboratory of Optics and Optoelectronics of Qingdao Ocean University of China Qingdao266100 China School of Flexible Electronics Sun Yat-sen University Shenzhen518107 China Institute of Precision Instrument and Quantum Sensing Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

To overcome antimalarial drug resistance, carbohydrate derivatives as selective PfHT1 inhibitor have been suggested in recent experimental work with orthosteric and allosteric dual binding pockets. Inspired by this promising therapeutic strategy, herein, molecular dynamics simulations are performed to investigate the molecular determinants of co-administration on orthosteric and allosteric inhibitors targeting PfHT1. Our binding free energy analysis capture the essential trend of inhibitor binding affinity to protein from published experimental IC50 data in three sets of distinct characteristics. In particular, we rank the contribution of key residues as binding sites which categorized into three groups based on linker length, size of tail group, and sugar moiety of inhibitors. The pivotal roles of these key residues are further validated by mutant analysis where mutated to nonpolar alanine leading to reduced affinities to different degrees. The exception was fructose derivative, which exhibited a significant enhanced affinity to mutation on orthosteric sites due to strong changed binding poses. This study may provide useful information for optimized design of precision medicine to circumvent drug-resistant Plasmodium parasites with high efficacy. © 2025, CC BY-SA.

关键词： Binding sites

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I-mRMR: Incremental max-relevance, and min-redundancy feature selection 3rd

I-mRMR: Incremental max-relevance, and min-redundancy featur...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Redundancy

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Deep Reinforcement Learning with Transformers for Text Adventure Games

Deep Reinforcement Learning with Transformers for Text Adven...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Yunqiu Xu Ling Chen Meng Fang Yang Wang Chengqi Zhang Centre for Artificial Intelligence University of Technology Sydney Sydney Australia Tencent Robotics X Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology China

ISBN: (数字)9781728145334

ISBN: (纸本)9781728145341

In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.

关键词： Games Generators Task analysis Reinforcement learning Logic gates Buildings Computer architecture

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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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