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Proceedings of the 41st International Conference on Machine Learning

作者： Junyu Huang Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Department of Computer Science and Engineering State University of New York at Buffalo NY School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China and The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio Δ(the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time $O(\frac{ndk\log\log n}{\epsilon^2})$ with O(k/ε) centers opened and (1 + ε)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/ε)-approximate solution can be obtained in time $O(\frac{ndk\log \log n}{\epsilon^2} + dpoly(k, \frac{1}{\epsilon})\log(n\Delta))$ with (1 + ε)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem.

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An Alzheimer’s disease gene prediction method based on ensemble of genome-wide association study summary statistics

An Alzheimer’s disease gene prediction method based on ense...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jia-Hao Song Cui-Xiang Lin Hong-Dong Li School of Computer Science and Engineering Human Provincial Key Lab on Bioinformatics Central South University Changsha China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The hitherto unknown specific etiology of Alzheimer’s disease (AD) poses a challenge for its prevention, diagnosis and treatment. Although genome-wide association studies (GWAS) are currently making rapid progress in identifying genetic variants associated with AD, the pathogenic mechanisms of the genetic loci identified are largely unknown. Transcriptome-wide association studies (TWAS) are an important class of methods for predicting disease genes. TWAS can explore the association of genes with the disease in relevant tissues by integrating genome-wide genetic regulatory data from specific tissues and disease-associated GWAS summary statistics. We found that TWAS analysis using different GWAS summary statistics may produce inconsistent results. To address this issue, we used ensemble summary statistics for AD-associated gene prediction considering the complementary nature of different datasets and the comparative nature between the results generated from different datasets. The prediction results were compared and analyzed to identify AD associated genes. The predicted genes were validated. In case study of an individual genes, we identified a potential association between AZGP1 and AD disease by this method.

关键词： Pathology Genomics Prediction methods bioinformatics Alzheimer's disease Diseases

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 24

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xiaoliang Wu Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University & Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie PA USA Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY USA Hunan Provincial Key Lab on Bioinformatics Central South University & Xiangjiang Laboratory Changsha China

ISBN: (纸本)9798400704864

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17+ε)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3+ε)-approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4+ε)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems.

关键词： clustering fairness machine learning

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One-Step Synthesis of Tungsten-Doped Carbonated Polymer Dots with High Sensitivity to Fe (Iii) and Ph Environments

SSRN

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SSRN 2022年

作者： Han, Yushu Bao, Rui Yi, Jianhong Li, Hongdong Liu, Liang Huang, Shuyu Li, Zhaojie Zhang, Wenfu Min, Deqi Faculty of Material Science and Engineering Kunming University of Science and Technology Kunming650093 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha410083 China

Carbonized polymer dots (CPDs) have drawn a lot of attention in the past ten years because of their excellent selectivity and sensitivity. In this study, tungsten was doped into CPDs (W-CPDs) using a simple hydrothermal technique for the first time. With an excitation/emission maximum of 365/455 nm and excitation-independent emission, W-CPDs exhibit blue fluorescence and have an average particle diameter of 2.33 nm. Because of the dynamic quenching between Fe (III) and W-CPDs, there is a good linear relationship between the Fe (III) concentration and the limit of detection (LOD) of 0.012 μM for W-CPD fluorescence intensity. The intensity of the W-CPDs fluorescence is also found to be linearly correlated with pH values between 5.0 and 10.0. This technique enables quick detection of Fe (III) in drinking water and is expected to lower the risk of Fe (III) ingestion by humans. In addition, the creation of fluorescent flexible devices and information encryption are fascinating applications for W-CPDs. © 2022, The Authors. All rights reserved.

关键词： Potable water

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One-Step Synthesis of Tungsten-Doped Carbonated Polymer Dots with High Sensitivity to Fe (Iii) and Ph Environments

SSRN

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SSRN 2022年

关键词： Potable water

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Attention-based Memory Fusion Network for Clinical Outcome Prediction using Electronic Medical Records

Attention-based Memory Fusion Network for Clinical Outcome P...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Abdulrahman Al-Dailami Hulin Kuang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Faculty of Computers and Information Technology Sana’a University Sana’a Yemen

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recent methods of patient clinical outcome prediction focus on embedding the temporal time-series data by sequential data encoders without considering the dependency between the different variables and the static demographics data. To solve this problem and achieve better patient outcome prediction, we propose an attention-based memory fusion (AMF) network with Gated Recurrent Unit (GRU) (called GRU-AMFN) to model the dependency between the different time-series and static demographic data and extract effective personalized representation about the patient’s clinical health status. We evaluate our proposed GRU-AMFN method on eICU, a publicly available dataset, to validate its effectiveness for the in-hospital mortality prediction task. Experimental results demonstrate that our proposed method outperforms several state-of-the-art models for the in-hospital mortality prediction task. Ablation studies show the effectiveness of the proposed attention-based memory fusion module and the adaptive fusion module. Besides, our proposed method finds several static demographic and time-series features that are important for mortality prediction.

关键词： Correlation Predictive models Logic gates Market research Feature extraction Data models Data mining

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Integrating Multi-scale Feature Representation and Ensemble Learning for Schizophrenia Diagnosis

Integrating Multi-scale Feature Representation and Ensemble ...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Manna Xiao Hulin Kuang Jin Liu Yan Zhang Yizhen Xiang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Psychiatry The Second Xiangya Hospital Central South University Changsha Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Resting-state functional magnetic resonance imaging (rs-fMRI) images have been widely used for diagnosis of schizophrenia. With rs-fMRI, most existing schizophrenia diagnostic methods have revealed schizophrenia’s functional abnormalities from the following three scales, i.e., regional neural activity alterations, functional connectivity abnormalities and brain network dysfunctions. However, many schizophrenia diagnosis methods do not consider the fusion of features from the three scales. In this study, we propose a schizophrenia diagnostic method based on multi-scale feature representation and ensemble learning. Firstly, features including the three scales (region, connectivity and network) are extracted from rs-fMRI images using the brainnetome atlas. For each scale, feature selection, i.e., least absolute shrinkage and selection operator, is applied to identify effective sub-features related to schizophrenia classification by a grid search. Then the selected sub-features of each scale are input to support vector machine with linear kernel to classify schizophrenia patients and healthy controls respectively. To further improve the schizophrenia diagnostic performance, an ensemble learning framework named E-RCN is proposed to average the probabilities obtained by the classifiers of each scale in decision level. By leave-one-out cross-validation on the center for biomedical research excellence dataset (COBRE), our proposed method achieves encouraging diagnosis performance, outperforming several state-of-the-art methods. In addition, ranked by the occurence frequency of each brain region within the leave-one-out cross-validation experiments, some brain regions related to schizophrenia, i.e., thalamus and middle temporal gyrus, and important elaborate subregions, i.e., Tha_L_8_8, MTG_L_4_4 and MTG_R_4_4, are found.

关键词： Support vector machines Mental disorders Neural activity Functional magnetic resonance imaging Feature extraction Thalamus Ensemble learning

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MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

arXiv

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arXiv 2022年

作者： Tian, Xu Liu, Jin Kuang, Hulin Sheng, Yu Wang, Jianxin The Hunan Provincal Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China

Accurately detecting Alzheimer’s disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multitask learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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BEA-SegNet: Body and Edge Aware Network for Medical Image Segmentation

BEA-SegNet: Body and Edge Aware Network for Medical Image Se...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hulin Kuang Yixiong Liang Ning Liu Jin Liu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (纸本)9781665429825

Medical image segmentation is a fundamental step for diagnosis and prognosis. This study proposes a new body and edge aware network for automated 2D medical image segmentation (called BEA-SegNet). The proposed BEA-SegNet consists of a shared encoder, a body and edge decouple (BEdecouple) module, two parallel decoders for body and edge segmentation. In the encoder and decoders, short-term multi-scale concatenation (STMSC) modules are utilized to implement multi-scale representation. We design a BEdecouple module to decouple the convolutional features into the body and edge features, making the proposed method be body and edge aware. The body and edge decoders utilize Bedecouple modules in each level to learn more effective features for the body and edge segmentation respectively, and their outputs are fused to generate the final segmentation. Besides, the body and edge supervision are applied to improve the final segmentation. The proposed BEA-SegNet is trained and evaluated on the International Skin Imaging Collaboration challenge 2018 dataset (ISIC2018). Experimental results show that the proposed BEA-SegNet achieves an average Dice similarity coefficient of 90.3% and an average Hausdorff distance of 15.9 for the skin lesion segmentation task and outperforms five benchmarks for skin lesion segmentation.

关键词： Image segmentation Image edge detection Conferences Benchmark testing Skin Decoding Lesions

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Dbfilm: A Novel Dual-Branch Frequency Improved Legendre Memory Forecasting Model for Coagulant Dosage Determination

SSRN

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SSRN 2023年

作者： Xia, Sibo Zhu, Hongqiu Zhang, Ning Cheng, Fei Li, Yonggang Zhou, Can School of Automation Central South University Changsha410083 China Key Laboratory of Industrial Intelligence and Systems Central South University Ministry of Education Changsha410083 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China

Abstract: In the process of drinking water preparation, the amount of coagulant added directly affects the quality of water and drinking safety. However, the coagulation process is influenced by many environmental factors, and the process of determining the dosage is complex and variable. The sedimentation reaction is severely delayed, making it difficult to establish a real-time coagulant dosage determination model. To address this issue, this study proposes a frequency-improved Legendre memory model based on deep learning. It has a dual-branch structure and accurately predicts the dosage of coagulant by learning the characteristic information in the historical time series. In the data instantiation module of the model, a local response mechanism is introduced to accurately describe the original distribution of the data by activating local information within the channel. In addition, the model adopts a dual-branch structure for parallel computation, capturing both local and global information of the sequence, and extracting richer information features from the historical data. With a lower computational cost, the model compensates for the loss of information in low-rank approximation operations, establishing longer time series mapping relationships. Experimental results verify that our proposed model has excellent performance, achieving a maximum improvement of 20.3% over the baseline model in a real coagulant dosage dataset, and achieving optimal performance in multiple multi-step prediction tasks on a public dataset. © 2023, The Authors. All rights reserved.

关键词： Potable water

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