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Article ; Online: A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High-Dimensional and Incomplete Data.

Li, Weiling / Wang, Renfang / Luo, Xin

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: High-dimensional and incomplete (HDI) data are frequently encountered in big date-related applications for describing restricted observed interactions among large node sets. How to perform accurate and efficient representation learning on such HDI data ... ...

Abstract	High-dimensional and incomplete (HDI) data are frequently encountered in big date-related applications for describing restricted observed interactions among large node sets. How to perform accurate and efficient representation learning on such HDI data is a hot yet thorny issue. A latent factor (LF) model has proven to be efficient in addressing it. However, the objective function of an LF model is nonconvex. Commonly adopted first-order methods cannot approach its second-order stationary point, thereby resulting in accuracy loss. On the other hand, traditional second-order methods are impractical for LF models since they suffer from high computational costs due to the required operations on the objective's huge Hessian matrix. In order to address this issue, this study proposes a generalized Nesterov-accelerated second-order LF (GNSLF) model that integrates twofold conceptions: 1) acquiring proper second-order step efficiently by adopting a Hessian-vector algorithm and 2) embedding the second-order step into a generalized Nesterov's acceleration (GNA) method for speeding up its linear search process. The analysis focuses on the local convergence for GNSLF's nonconvex cost function instead of the global convergence has been taken; its local convergence properties have been provided with theoretical proofs. Experimental results on six HDI data cases demonstrate that GNSLF performs better than state-of-the-art LF models in accuracy for missing data estimation with high efficiency, i.e., a second-order model can be accelerated by incorporating GNA without accuracy loss.
Language	English
Publishing date	2023-10-13
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3321915
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Book ; Online: Representation Learning on Event Stream via an Elastic Net-incorporated Tensor Network

Yang, Beibei / Li, Weiling / Fang, Yan

2024

Abstract: Event cameras are neuromorphic sensors that capture asynchronous and sparse event stream when per-pixel brightness changes. The state-of-the-art processing methods for event signals typically aggregate events into a frame or a grid. However, events are ... ...

Abstract	Event cameras are neuromorphic sensors that capture asynchronous and sparse event stream when per-pixel brightness changes. The state-of-the-art processing methods for event signals typically aggregate events into a frame or a grid. However, events are dense in time, these works are limited to local information of events due to the stacking. In this paper, we present a novel spatiotemporal representation learning method which can capture the global correlations of all events in the event stream simultaneously by tensor decomposition. In addition, with the events are sparse in space, we propose an Elastic Net-incorporated tensor network (ENTN) model to obtain more spatial and temporal details about event stream. Empirically, the results indicate that our method can represent the spatiotemporal correlation of events with high quality, and can achieve effective results in applications like filtering noise compared with the state-of-the-art methods. Comment: 7 pages, 3 figure
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2024-01-15
Publishing country	us
Document type	Book ; Online
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Article ; Online: ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data.

Li, Weiling / Malhotra, Raunaq / Wu, Steven / Jha, Manjari / Rodrigo, Allen / Poss, Mary / Acharya, Raj

IEEE/ACM transactions on computational biology and bioinformatics

2024 Volume PP

Abstract: We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a ... ...

Abstract	We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH [1] based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations. We evaluated our pipeline on both simulated and real viral quasispecies data from HIV (and real data from SARS-COV-2). Experimental results show that ViPRA-Haplo, although still an overestimation in the number of true contigs, outperforms the existing tool, PEHaplo, providing up to 9% better genome coverage on HIV real data. In addition, ViPRA-Haplo also retains higher diversity of the viral population as demonstrated by the presence of a higher percentage of contigs less than 1000 base pairs (bps), which also contain k-mers with counts less than 100 (representing rarer sequences), which are absent in PEHaplo. For SARS-CoV-2 sequencing data, ViPRA-Haplo reconstructs contigs that cover more than 90% of the reference genome and were able to validate known SARS-CoV-2 strains in the sequencing data.
Language	English
Publishing date	2024-03-07
Publishing country	United States
Document type	Journal Article
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2024.3374595
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Book ; Online: Second-order Symmetric Non-negative Latent Factor Analysis

Li, Weiling / Luo, Xin

2022

Abstract: Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set. The undirected network representation task can be efficiently addressed by a symmetry non-negative latent factor (SNLF) model, ... ...

Abstract	Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set. The undirected network representation task can be efficiently addressed by a symmetry non-negative latent factor (SNLF) model, whose objective is clearly non-convex. However, existing SNLF models commonly adopt a first-order optimizer that cannot well handle the non-convex objective, thereby resulting in inaccurate representation results. On the other hand, higher-order learning algorithms are expected to make a breakthrough, but their computation efficiency are greatly limited due to the direct manipulation of the Hessian matrix, which can be huge in undirected network representation tasks. Aiming at addressing this issue, this study proposes to incorporate an efficient second-order method into SNLF, thereby establishing a second-order symmetric non-negative latent factor analysis model for undirected network with two-fold ideas: a) incorporating a mapping strategy into SNLF model to form an unconstrained model, and b) training the unconstrained model with a specially designed second order method to acquire a proper second-order step efficiently. Empirical studies indicate that proposed model outperforms state-of-the-art models in representation accuracy with affordable computational burden. Comment: 6 pages
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-03-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks

Zhong, Yurong / Xie, Zhe / Li, Weiling / Luo, Xin

2023

Abstract: An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e.g., a protein interaction network from a bioinformatics application. A Symmetric High-Dimensional ...

Abstract	An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e.g., a protein interaction network from a bioinformatics application. A Symmetric High-Dimensional and Incomplete (SHDI) matrix can smoothly illustrate such an UWN, which contains rich knowledge like node interaction behaviors and local complexes. To extract desired knowledge from an SHDI matrix, an analysis model should carefully consider its symmetric-topology for describing an UWN's intrinsic symmetry. Representation learning to an UWN borrows the success of a pyramid of symmetry-aware models like a Symmetric Nonnegative Matrix Factorization (SNMF) model whose objective function utilizes a sole Latent Factor (LF) matrix for representing SHDI's symmetry rigorously. However, they suffer from the following drawbacks: 1) their computational complexity is high; and 2) their modeling strategy narrows their representation features, making them suffer from low learning ability. Aiming at addressing above critical issues, this paper proposes a Multi-constrained Symmetric Nonnegative Latent-factor-analysis (MSNL) model with two-fold ideas: 1) introducing multi-constraints composed of multiple LF matrices, i.e., inequality and equality ones into a data-density-oriented objective function for precisely representing the intrinsic symmetry of an SHDI matrix with broadened feature space; and 2) implementing an Alternating Direction Method of Multipliers (ADMM)-incorporated learning scheme for precisely solving such a multi-constrained model. Empirical studies on three SHDI matrices from a real bioinformatics or industrial application demonstrate that the proposed MSNL model achieves stronger representation learning ability to an SHDI matrix than state-of-the-art models do. Comment: arXiv admin note: text overlap with arXiv:2306.03647
Keywords	Computer Science - Machine Learning
Subject code	004
Publishing date	2023-06-06
Publishing country	us
Document type	Book ; Online
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Book ; Online: Proximal Symmetric Non-negative Latent Factor Analysis

Zhong, Yurong / Xie, Zhe / Li, Weiling / Luo, Xin

A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

2023

Abstract: An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, ... ...

Abstract	An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, existing models fail in either modeling its intrinsic symmetry or low-data density, resulting in low model scalability or representation learning ability. For addressing this issue, a Proximal Symmetric Nonnegative Latent-factor-analysis (PSNL) model is proposed. It incorporates a proximal term into symmetry-aware and data density-oriented objective function for high representation accuracy. Then an adaptive Alternating Direction Method of Multipliers (ADMM)-based learning scheme is implemented through a Tree-structured of Parzen Estimators (TPE) method for high computational efficiency. Empirical studies on four UWNs demonstrate that PSNL achieves higher accuracy gain than state-of-the-art models, as well as highly competitive computational efficiency.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-06-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Exploring the Antiovarian Cancer Mechanisms of Salvia Miltiorrhiza Bunge by Network Pharmacological Analysis and Molecular Docking.

Xu, Xiao / Zhang, Zhiwei / Liu, Likun / Che, Cheng / Li, Weiling

Computational and mathematical methods in medicine

2022 Volume 2022, Page(s) 7895246

Abstract: Background: Ovarian cancer was one of the gynecological malignant tumors. : Objective: The antitumor effect of SMB on ovarian cancer was studied by network pharmacology and molecular docking techniques, and its possible molecular mechanisms were ... ...

Abstract	Background: Ovarian cancer was one of the gynecological malignant tumors. Objective: The antitumor effect of SMB on ovarian cancer was studied by network pharmacology and molecular docking techniques, and its possible molecular mechanisms were analyzed. Method: The active ingredients of SMB and the target data of ovarian cancer were obtained from the Traditional Chinese Medicines for Systems Pharmacology Database (TCMSP) and the GeneCards database. The relationship between active ingredients of SMB and ovarian cancer targets was analyzed by String database, David 6.8 online database, and Cytoscape 3.7.2 software, and then potential pathways were screened out. In addition, molecular docking technology was used to verify further the binding effect of antiovarian cancer pathway targets with active ingredients of SMB. Finally, survival analysis was performed for all potential targets. Results: We analyzed 71 SMB-ovarian cancer common targets, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the PI3K-Akt signaling pathway might be an essential pathway for SMB to inhibit ovarian cancer. Luteolin, Tanshinone IIA, and Cryptotanshinone in SMB might play an important role. HSP90AA1, CDK2, and PIK3CG might be potential targets of SMB in inhibiting ovarian cancer. Conclusion: Through network pharmacology and molecular docking analysis, we found that SMB might partially inhibit ovarian cancer by the PI3K-Akt signaling pathway. We believe that SMB might be a potential therapeutic agent for ovarian cancer patients.
MeSH term(s)	Humans ; Female ; Molecular Docking Simulation ; Salvia miltiorrhiza ; Phosphatidylinositol 3-Kinases ; Ovarian Neoplasms/drug therapy ; Ovarian Neoplasms/genetics ; Databases, Factual
Chemical Substances	Phosphatidylinositol 3-Kinases (EC 2.7.1.-)
Language	English
Publishing date	2022-11-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2252430-7
ISSN	1748-6718 ; 1748-670X ; 1027-3662
ISSN (online)	1748-6718
ISSN	1748-670X ; 1027-3662
DOI	10.1155/2022/7895246
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Article ; Online: RAPID Software to the Clinical Application Value of Acute Basilar Artery Occlusion with Endovascular Treatment.

Li, Weiling / Hong, Weijun / Wang, En / Jiang, Yiqing

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

2021 Volume 30, Issue 12, Page(s) 106147

Abstract: Objective: To investigate the clinical application value of RAPID software based on computed tomography perfusion imaging (CTP) in the endovascular treatment of acute basilar artery occlusion (BAO).: Materials and methods: The data of patients with ... ...

Abstract	Objective: To investigate the clinical application value of RAPID software based on computed tomography perfusion imaging (CTP) in the endovascular treatment of acute basilar artery occlusion (BAO). Materials and methods: The data of patients with acute basilar artery occlusion who received endovascular treatment in Taizhou Hospital, Zhejiang Province, between January 2020 and April 2021 were retrospectively analysed. The patients were divided into a perfusion imaging and a no-perfusion imaging group based on whether the image analysis results were obtained by RAPID software. Age, preoperative National Institute of Health stroke scale (NIHSS) score, onset to puncture time (OPT), operation methods, good prognosis at 3 months after surgery (modified Rankin scale (mRS) score ≤3), symptomatic intracranial haemorrhage (sICH) and other clinical data were compared between the two groups. Multivariate logistic regression analysis was used to identify the independent factors affecting the prognosis of BAO patients. Results: In total, 61 patients with acute BAO were included: 31 patients in the perfusion imaging group and 30 patients in the no-perfusion imaging group. There were no statistically significant differences between the two groups in age, NIHSS score or operation methods (all P >0.05). However, OPT and the good prognosis rate were significantly higher in the perfusion imaging group than in the no-perfusion imaging group (χ Conclusions: RAPID software based on CTP can be used for preoperative screening of patients with acute basilar artery occlusion to identify those suitable for endovascular treatment, which is worthy of clinical promotion.
MeSH term(s)	Arterial Occlusive Diseases/diagnostic imaging ; Arterial Occlusive Diseases/surgery ; Basilar Artery/diagnostic imaging ; Basilar Artery/pathology ; Basilar Artery/surgery ; Endovascular Procedures ; Humans ; Mass Screening/methods ; Preoperative Care ; Retrospective Studies ; Software
Language	English
Publishing date	2021-10-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	1131675-5
ISSN	1532-8511 ; 1052-3057
ISSN (online)	1532-8511
ISSN	1052-3057
DOI	10.1016/j.jstrokecerebrovasdis.2021.106147
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Book ; Online: An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Xie, Zhe / Li, Weiling / Zhong, Yurong

2022

Abstract: Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative ...

Abstract	Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it relies on a constraint-combination training scheme, which makes it lack flexibility. To address this issue, this paper proposes an unconstrained symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is two-fold: 1) The output LFs are separated from the decision parameters via integrating a nonnegative mapping function into an SNL model; and 2) Stochastic gradient descent (SGD) is adopted for implementing unconstrained model training along with ensuring the output LFs nonnegativity. Empirical studies on four SHDI matrices generated from real big data applications demonstrate that an USNL model achieves higher prediction accuracy of missing data than an SNL model, as well as highly competitive computational efficiency.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-08-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization

Wang, Jialiang / Zhong, Yurong / Li, Weiling

2022

Abstract: Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective ... ...

Abstract	Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a practical SLF (PSLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that PSLF model has a competitive advantage over state-of-the-art models in data representation ability. Comment: 7 pages
Keywords	Computer Science - Machine Learning
Publishing date	2022-08-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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