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lncRNA-disease association prediction based on optimizing measures of multi-graph regularized matrix factorization

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年 1-16页

作者： Yao, Bin Song, Yunzhong Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Peoples R China Henan Int Joint Lab Direct Drive & Gen Intelligent Jiaozuo Peoples R China

In this paper, we propose a novel lncRNA-disease association prediction algorithm based on optimizing measures of multi-graph regularized matrix factorization (OM-MGRMF). The method first calculates the semantic similarity of diseases, the functional similarity of lncRNAs, and the Gaussian similarity of both. It then constructs a new lncRNA-disease association matrix by using the K-nearest-neighbor (KNN) algorithm. Finally, the objective function is constructed through the utilization of ranking measures and multi-graph regularization constraints. This objective function is iteratively optimized by an adaptive gradient descent algorithm. The experimental results of OM-MGRMF outperform those of classical methods in both K-fold cross-validation.

关键词： lncRNA-disease associations prediction matrix factorization similarity network fusion multi-graph regularization

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Anomaly-aware symmetric non-negative matrix factorization for short text clustering

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KNOWLEDGE AND INFORMATION SYSTEMS 2025年第2期67卷 1481-1506页

作者： Li, Ximing Guan, Yuanyuan Fu, Bo Luo, Zhongxuan Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China Changchun Univ Sci & Technol Dept Comp Sci & Technol Changchun Peoples R China Liaoning Normal Univ Sch Comp & Informat Technol Liaoning Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China Dalian Univ Technol Sch Software Technol Dalian Peoples R China Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaonin Dalian Peoples R China

Short text clustering is a significant yet challenging task, where short texts generated from the Internet are extremely sparse, noisy, and ambiguous. The sparse nature makes traditional clustering methods, e.g.,k-means family and topic modeling, much less effective. Fortunately, recent arts of document distance, e.g., word mover's distance, and document representation, e.g., BERT, can accurately measure the similarities of short texts, especially their nearest neighbors. Inspired by those arts and observations, we induce short text clusters by directly factorizing the informative affinity matrix of nearest neighbors into the product of the cluster assignment matrix, following the intuition that neighboring short texts tend to be assigned to the same cluster. However, due to the noisy nature of short texts, many of them can be regarded as outliers or near outliers, resulting in many noisy neighboring similarities within the affinity matrix. To further alleviate this problem, we enhance the affinity matrix factorization by (1) incorporating a sparse noisy matrix to directly capture noisy neighboring similarities and (2) regularizing the cluster assignment matrix by & ell;2,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _{2,1}$$\end{document} norm to eliminate hard-to-clustering short texts (called pseudo-outliers), so as to indirectly neglect noisy neighboring similarities corresponding to them. After this factorization for pre-clustering, we train a classifier over the resulting clusters and adopt it to assign each pseudo-outlier to one cluster finally. We call this novel clustering method as anomaly-aware symmetric non-negative matrix factorization (A2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{ams

关键词： Short text clustering Anomaly detection Affinity matrix matrix factorization

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Source apportionment of PM2.5 using dispersion normalized positive matrix factorization (DN-PMF) in Beijing and Baoding, China

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JOURNAL OF ENVIRONMENTAL SCIENCES 2025年 155卷 395-408页

作者： Ryoo, Ilhan Kim, Taeyeon Ryu, Jiwon Cheong, Yeonseung Moon, Kwang-joo Jeon, Kwon-ho Hopke, Philip K. Yi, Seung-Muk Park, Jieun Seoul Natl Univ Grad Sch Publ Hlth Seoul 08826 South Korea Natl Inst Environm Res Climate & Air Qual Res Dept Global Environm Res Di Incheon 22689 South Korea Univ Rochester Sch Med & Dent Dept Publ Hlth Sci & Environm Med Rochester NY 14642 USA Clarkson Univ Inst Sustainable Environm Potsdam NY 13699 USA Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02215 USA

Fine particulate matter (PM2.5) samples were collected in two neighboring cities, Beijing and Baoding, China. High-concentration events of PM2.5 in which the average mass concentration exceeded 75 mu g/m(3) were frequently observed during the heating season. Dispersion Normalized Positive matrix factorization was applied for the source apportionment of PM2.5 as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities. Secondary nitrate had the highest contribution for Beijing (37.3 %), and residential heating/biomass burning was the largest for Baoding (27.1 %). Secondary nitrate, mobile, biomass burning, district heating, oil combustion, aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period (2019.01.10-2019.08.22) (Mann-Whitney Rank Sum Test P < 0.05). In case of Baoding, soil, residential heating/biomass burning, incinerator, coal combustion, oil combustion sources showed significant differences. The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients. Conditional Bivariate Probability Function (CBPF) was used to identify the inflow directions for the sources, and joint-PSCF (Potential Source Contribution Function) was performed to determine the common potential source areas for sources affecting both cities. These models facilitated a more precise verification of city-specific influences on PM2.5 sources. The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.

关键词： Source apportionment Dispersion normalized positive matrix factorization Adjacent cities Inter-city impact Source location Heating season Air quality management

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CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction

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BMC BIOINFORMATICS 2025年第1期26卷 1-18页

作者： Tang, Xianfang Hou, Yawen Meng, Yajie Wang, Zhaojing Lu, Changcheng Lv, Juan Hu, Xinrong Xu, Junlin Yang, Jialiang Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan 430200 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Changsha Med Univ Coll Tradit Chinese Med Changsha 410000 Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Geneis Beijing Co Ltd Beijing 100102 Peoples R China

The process of new drug development is complex, whereas drug-disease association (DDA) prediction aims to identify new therapeutic uses for existing medications. However, existing graph contrastive learning approaches typically rely on single-view contrastive learning, which struggle to fully capture drug-disease relationships. Subsequently, we introduce a novel multi-view contrastive learning framework, named CDPMF-DDA, which enhances the model's ability to capture drug-disease associations by incorporating diverse information representations from different views. First, we decompose the original drug-disease association matrix into drug and disease feature matrices, which are then used to reconstruct the drug-disease association network, as well as the drug-drug and disease-disease similarity networks. This process effectively reduces noise in the data, establishing a reliable foundation for the networks produced. Next, we generate multiple contrastive views from both the original and generated networks. These views effectively capture hidden feature associations, significantly enhancing the model's ability to represent complex relationships. Extensive cross-validation experiments on three standard datasets show that CDPMF-DDA achieves an average AUC of 0.9475 and an AUPR of 0.5009, outperforming existing models. Additionally, case studies on Alzheimer's disease and epilepsy further validate the model's effectiveness, demonstrating its high accuracy and robustness in drug-disease association prediction. Based on a multi-view contrastive learning framework, CDPMF-DDA is capable of integrating multi-source information and effectively capturing complex drug-disease associations, making it a powerful tool for drug repositioning and the discovery of new therapeutic strategies.

关键词： Drug-disease association prediction Contrastive learning matrix factorization Multiple contrastive views

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A QoS Prediction Framework via Utility Maximization and Region-Aware matrix factorization

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第2期18卷 557-571页

作者： Chen, Xia Du, Yugen Tang, Guoxing Chen, Fan Luo, Yingwei Wang, Hanting East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China

With the surge of Web services, users are more concerned about Quality-of-Service (QoS) information when choosing Web services with similar functionalities. Today, effectively and accurately predicting QoS values is a tough challenge. Typically, traditional methods only use the QoS values provided by users to predict the missing QoS values, ignoring the arbitrariness of some users in providing observed QoS values and failing to consider the existence of anomalous QoS values with contingencies caused by some unstable Web services. Taking into account the above, this article proposes HyLoReF-us, a new framework for QoS prediction. HyLoReF-us uses the user reputation to measure the trustworthiness of users and the service reputation to measure the stability of web services. First, considering the utility generated by the invocation between users and Web services, HyLoReF-us employs a Logit model to calculate the user reputation and service reputation. Second, after combining the location information of users and services, as well as their reputations, HyLoReF-us obtains QoS predictions through an improved matrix factorization (MF) model. Finally, a series of experiments were conducted on the standard WS-DREAM dataset. Experimental results show that HyLoReF-us outperforms current state-of-the-art or baseline methods at matrix Densities (MD) from 5% to 30%.

关键词： Quality of service Reliability Predictive models Accuracy Prediction algorithms Computational modeling IP networks Data models Computer bugs Web service Quality of Service QoS prediction reputation matrix factorization service computing service computing

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TphPMF: A microbiome data imputation method using hierarchical Bayesian Probabilistic matrix factorization

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PLOS COMPUTATIONAL BIOLOGY 2025年第3期21卷 e1012858页

作者： Han, Xinyu Song, Kai Qingdao Univ Sch Math & Stat Qingdao Peoples R China

In microbiome research, data sparsity represents a prevalent and formidable challenge. Sparse data not only compromises the accuracy of statistical analyses but also conceals critical biological relationships, thereby undermining the reliability of the conclusions. To tackle this issue, we introduce a machine learning approach for microbiome data imputation, termed TphPMF. This technique leverages Probabilistic matrix factorization, incorporating phylogenetic relationships among microorganisms to establish Bayesian prior distributions. These priors facilitate posterior predictions of potential non-biological zeros. We demonstrate that TphPMF outperforms existing microbiome data imputation methods in accurately recovering missing taxon abundances. Furthermore, TphPMF enhances the efficacy of certain differential abundance analysis methods in detecting differentially abundant (DA) taxa, particularly showing advantages when used in conjunction with DESeq2-phyloseq. Additionally, TphPMF significantly improves the precision of cross-predicting disease conditions in microbiome datasets pertaining to type 2 diabetes and colorectal cancer.

关键词： matrix factorization

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LDPMF: Local differential privacy enhanced matrix factorization for advanced recommendation

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KNOWLEDGE-BASED SYSTEMS 2025年 309卷

作者： Li, Xiang Zhou, Wang Ul Haq, Amin Khan, Shakir Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Riyadh Saudi Arabia

Currently, Recommender Systems are widely applied in various industries, the primary objective of which is to utilize the user-item interaction records to enhance the recommendation accuracy and user experience. However, with information overload, the variety of items recommended by recommender systems has increased dramatically, leading to a surge in the number of user ratings. The ensuing problem is that privacy leakage may become particularly prominent when recommender systems acquire items and ratings, since that user's behavior records may contain certain sensitive information, which can be accessible easily. To cope with these challenges, differential privacy preservation techniques have become powerful tools to solve the privacy leakage problem in recommender systems. However, these methods still face issues such as the balance between privacy protection and utility, attacks and inferential threats. In this article, we propose a novel local differential privacy based recommender system referred to as LDPMF, to enhance the performance of privacy preservation and robustness of recommendation interference. In practice, LDPMF aims to simultaneously protect ratings and the rated items. By employing a stochastic process with a dimensionality reduction technique, we successfully decrease the perturbation error, and reduce the error generated by updating iterations through stabilizing the noise radius. In addition, we introduce a factor for stabilizing the perturbation gradient for LDPMF. In practice, we conduct performance evaluation for LDPMF over two real-life datasets: MovieLens and LibimSeTi. The experimental results indicate that LDPMF significantly outperforms the compared approaches under strong privacy requirements.

关键词： matrix factorization Differential privacy Recommender system Theoretical analysis

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A novel weighted pseudo-labeling framework based on matrix factorization for adverse drug reaction prediction

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BMC BIOINFORMATICS 2025年第1期26卷 1-21页

作者： Chen, Junheng Han, Fangfang He, Mingxiu Shi, Yiyang Cai, Yongming Guangdong Pharmaceut Univ Sch Med Informat & Engn Guangzhou 510006 Peoples R China NMPA Key Lab Technol Res & Evaluat Pharmacovigilan Guangzhou 510006 Peoples R China Guangdong Prov Tradit Chinese Med Precis Med Big D Guangzhou 510006 Peoples R China

Adverse drug reactions (ADRs) are among the global public health events that seriously endanger human life and cause high economic burdens. Therefore, predicting the possibility of their occurrence and taking early and effective response measures is of great significance. Constructing a correlation matrix between drugs and their adverse reactions, followed by effective correlation data mining, is one of the current strategies to predict ADRs using accessible public data. Since the number of known ADRs in real-world data is far less than the number of their unknown counterparts, the drug-ADR association matrix is very sparse, which greatly affects the classification performance of machine learning methods. To effectively address the problem of sparsity, we proposed a novel weighted pseudo-labeling framework that mines potential unknown drug-ADR pairs by integrating multiple weighted matrix factorization (MF) models and treating them as pseudo-labeled drug-ADR pairs. Pseudo-labeled data is added to the training set, and the MF model is fine-tuned to improve the classification performance. To prevent overfitting to easily found pseudo-labels and improve the quality of pseudo-labels, a novel weighting approach for pseudo-labels was adopted. This paper reproduces the baselines under the same experimental conditions to evaluate the performance of the proposed method on sparse data from the Side Effect Resource (SIDER) database. Experimental results showed that our method outperformed other baselines in the Area Under Precision-Recall and F1-scores and still maintained the best performance in sparser scenarios. Furthermore, we conducted a case study, and the results showed that our proposed framework efficiently predicted ADRs in the real world.

关键词： Adverse drug reaction prediction Weighted pseudo-labeling matrix factorization Semi-supervised learning

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Robust unsupervised feature selection based on matrix factorization with adaptive loss via bi-stochastic graph regularization

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APPLIED INTELLIGENCE 2025年第1期55卷 1-22页

作者： Song, Xiangfa Henan Univ Sch Comp & Informat Engn Kaifeng 475001 Henan Peoples R China

Unsupervised feature selection (UFS) has gained increasing attention and research interest in various domains, such as machine learning and data mining. Recently, numerous matrix factorization-based methods have been widely adopted for UFS. However, the following issues still exist. First, most methods based on matrix factorization use the squared Frobenius-norm to measure the loss term, making them sensitive to outliers. Although using l2,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \varvec{l}_{\varvec{2,1}} $$\end{document}-norm-based loss improves the robustness, it is sensitive to small loss. Second, most existing matrix factorization-based methods utilize a fixed graph to preserve the local structure of data, making them fail to capture the intrinsic local structure accurately. To address the above drawbacks, we present a novel robust UFS model named matrix factorization with adaptive loss via bi-stochastic graph regularization (MFALBS). Specifically, MFALBS decomposes the projected data points into two orthogonal matrices to facilitate discriminative feature selection. Meanwhile, MFALBS utilizes an adaptive loss function for measuring the loss term in order to enhance model robustness. Moreover, MFALBS adopts an adaptive learning strategy for optimal bi-stochastic graph learning in order to improve local structure preservation ability. Finally, an optimization algorithm is proposed to solve the proposed model. Experimental results on real-world datasets show the effectiveness and superiority of MFALBS.

关键词： Unsupervised feature selection matrix factorization Adaptive loss Bi-stochastic graph

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Distributed Differentially Private matrix factorization for Implicit Data via Secure Aggregation

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IEEE TRANSACTIONS ON COMPUTERS 2025年第2期74卷 705-716页

作者： Luo, Chenhong Wang, Yong Zhang, Yanjun Zhang, Leo Yu Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Griffith Univ Sch Informat & Commun Technol Southport Qld 4111 Australia Chongqing Univ Posts & Telecommun Sch Econ & Management Chongqing 400065 Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW 2007 Australia Griffith Univ Sch Informat & Commun Technol Southport Qld 4111 Australia

Implicit feedback data has become the primary choice for building recommendation models due to its abundance and ease for collection in the real world. The strong generalization capability and high computational efficiency of matrix factorization make it one of the principal models for constructing recommender systems. Recommenders have to collect vast amounts of user data for model training, which poses a significant threat to user privacy. Most of the current privacy enhancing recommendation systems mainly focus on explicit feedback data, and there are limited studies dedicated to the privacy protection of implicit recommender. To bridge the existing research gap, this paper designs a distributed differentially private matrix factorization for implicit feedback data in scenarios where the recommender is not trusted. Our mechanism not only eliminates the assumption of a trusted recommender, but also achieves the same accuracy as CDP-based privacy-preserving MF model. We prove that our mechanism satisfies (& varepsilon;,delta)-CDP. The experimental results on three public datasets confirm that the proposed mechanism can achieve high recommendation quality.

关键词： Privacy Recommender systems Differential privacy Accuracy Data models Servers Computational modeling Training Protection Noise matrix factorization differential privacy Bayesian personalized ranking implicit data recommendation

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