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Mining of inland water traffic accident data using a biclustering algorithm: A case study of the Yangtze River

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2019年第1期233卷 48-57页

作者： Chen, Chen Wu, Qing Gao, Song Wuhan Univ Technol Wuhan Hubei Peoples R China Wuhan Business Univ Wuhan Hubei Peoples R China Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan Hubei Peoples R China

Analysis of maritime accident data is important for improving safety management. Clustering is the favoured method of mining marine accident data. However, traditional one-way clustering methods are limited by their focus on global patterns, which does not account for the contingent characteristics of accidents. In this study, biclustering algorithms (BAs) typically used for gene expressions are introduced for analysis of inland water traffic accident data. BAs are good for discovering local patterns (LPs), which represent the similarities between partial accidents and partial attributes. LPs are the more likely modes in accident data, which are difficult to discern using who is traditional one-way clustering. During biclustering of original accident data, six LPs involving replicative accidents are uncovered, thereby suggesting a high risk in similar scenarios. With biclustering of accident attribute factors, the interrelationships among factors are discovered. According to the LPs explored using BAs, high-risk scenarios should gain the attention of shipping companies and safety management departments. Two recommendations are presented: raising awareness of the need for immediate accident reporting and disseminating rescue knowledge. After comparing their applications, the order-preserving submatrix (OPSM) and conserved gene expression motif (xMotifs) algorithms are regarded as the most suitable BAs for analysing maritime accident data.

关键词： Inland water traffic accident analysis Yangtze River biclustering algorithm OPSM xMotifs

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Rank-preserving biclustering algorithm: a case study on miRNA breast cancer

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2021年第4期59卷 989-1004页

作者： Mandal, Koyel Sarnnah, Rosy Bhattacharyya, Dhruba Kumar Kalita, Jugal Kumar Borah, Bhogeswar Tezpur Univ Dept Comp Sci & Engn Tezpur Assam India Univ Colorado Dept Comp Sci Colorado Springs CO 80907 USA

Effective biomarkers aid in the early diagnosis and monitoring of breast cancer and thus play an important role in the treatment of patients suffering from the disease. Growing evidence indicates that alteration of expression levels of miRNA is one of the principal causes of cancer. We analyze breast cancer miRNA data to discover a list of biclusters as well as breast cancer miRNA biomarkers which can help to understand better this critical disease and take important clinical decisions for treatment and diagnosis. In this paper, we propose a pattern-based parallel biclustering algorithm termed Rank-Preserving biclustering (RPBic). The key strategy is to identify rank-preserved rows under a subset of columns based on a modified version of all substrings common subsequence (ALCS) framework. To illustrate the effectiveness of the RPBic algorithm, we consider synthetic datasets and show that RPBic outperforms relevant biclustering algorithms in terms of relevance and recovery. For breast cancer data, we identify 68 biclusters and establish that they have strong clinical characteristics among the samples. The differentially co-expressed miRNAs are found to be involved in KEGG cancer related pathways. Moreover, we identify frequency-based biomarkers (hsa-miR-410, hsa-miR-483-5p) and network-based biomarkers (hsa-miR-454, hsa-miR-137) which we validate to have strong connectivity with breast cancer. The source code and the datasets used can be found at .

关键词： Cancer gene expression data miRNA expression data biclustering algorithm Biomarker

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BiSim: A Simple and Efficient biclustering algorithm

BiSim: A Simple and Efficient Biclustering Algorithm

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International Conference of Soft Computing and Pattern Recognition

作者： Noureen, Nighat Qadir, Muhammad Abdul Mohammad Ali Jinnah Univ Dept Comp Sci & Bioinformat Blue Area Islamabad Islamabad Pakistan

ISBN: (纸本)9781424453306

Analysis of gene expression data includes classification of the data into groups and subgroups based on similar expression patterns. Standard clustering methods for the analysis of gene expression data only identifies the global models while missing the local expression patterns. In order to identify the missed patterns biclustering approach has been introduced. Various biclustering algorithms have been proposed by scientists. Among them Binary Inclusion maximal algorithm (BiMax) forms biclusters when applied on a gene expression data through divide and conquer approach. The worst-case running-time complexity of BiMax for matrices containing disjoint biclusters is O(nmb) and for arbitrary matrices is of order O(nmb min{n, m}) where b is the number of all inclusion-maximal biclusters in matrix. In this paper we present an improved algorithm, BiSim, for biclustering which is easy and avoids complex computations as in BiMax. The complexity of our approach is O(n*m) for n genes and m conditions, i.e, a matrix of size n*m. Also it avoids extra computations within the same complexity class and avoids missing of any biclusters.

关键词： biclustering gene expression data biclustering algorithm bicluster co-regulated co-expressed local expression patterns

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Classification of muscle activity patterns in healthy children using biclustering algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期84卷

作者： Pauk, Jolanta Daunoraviciene, Kristina Ziziene, Jurgita Minta-Bielecka, Katarzyna Dzieciol-Anikiej, Zofia Bialystok Tech Univ Wiejska 45C PL-15351 Bialystok Poland Vilnius Gediminas Tech Univ Dept Biomech Engn Plytines str 27A LT-10105 Vilnius Lithuania Med Univ Bialystok Jana Kilinskiego 1 PL-15089 Bialystok Poland Vilnius Gediminas Tech Univ Vilnius Lithuania

In recent years, there has been major interest in recognising electromyography (EMG) patterns. This work proposes a new method based on a biclustering algorithm which can group strides showing homogeneous EMG activation intervals. The surface EMG signals of biceps femoris, rectus femoris, semitendinosus, lateral gastrocnemius, and medial gastrocnemius muscles of 17 healthy children aged between 4 and 11 years old were obtained using a Trigno EMG wireless system. The data set was tested for different values of parameter alpha (the threshold describing when the multiple node deletion step is used) and delta (the threshold that limits the value of the mean square residue). The highest number of coincidences of muscle activation was observed in 6 to 7-year-old subjects. This was not affected by their anthropometrics or gender. The obtained biclusters reflect actual differences between the subjects' gait parameters, namely stride length, stride time, and walking speed. These results can be used to develop strategies for finding homogeneous groups of patients.

关键词： biclustering algorithm EMG pattern Children gait Homogenous group

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BBM: A novel beta-binomial-distribution-based biclustering algorithm for mining m6A co-methylation patterns

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Liu, Zhaoyang Xiao, Yuteng Wang, Honglei Li, Chunyan Yin, Hongsheng Xuzhou Univ Technol Sch Informat Engn Sch Big Data Xuzhou 221018 Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat SecurMinist Jinan 250353 Peoples R China Yunnan Normal Univ Sch Informat Kunming 650500 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

Studies have indicated that mining co-methylation patterns of N6-methyladenosine (m6A) can aid in understanding its functional mechanism. However, current algorithms require the conversion of IP and input data matrices into a single methylation level matrix for co-methylation pattern mining. This conversion introduces additional noise and leads to inaccurate results. To address this issue, we propose a novel biclustering algorithm, BBM, based on the beta-binomial distribution. BBM can perform biclustering analysis directly on IP and input matrices without the need for data matrix conversion. The Gibbs sampling method is used to infer parameters in BBM. Simulation studies show that BBM can accurately reproduce the three pre-planted patterns and their overlap. On real data, BBM identify two patterns. Gene Ontology enrichment analysis reveal that the sites remaining in the two patterns are enriched in different biological processes. One pattern is associated with RNA splicing, translation, transport, and localization, while the other is related to histone modification and histone H3 acetylation. These findings suggest that the two patterns are effective co-methylation patterns. In addition, GOE_Score scoring demonstrate that these two patterns are more biologically relevant than patterns mined by other current mainstream algorithms. BBM is freely available at https://***/cumtlzhy/BBM.

关键词： Beta-binomial-distribution biclustering algorithm Co-methylation pattern m 6 A

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AN IMPROVED biclustering algorithm AND ITS APPLICATION TO GENE EXPRESSION SPECTRUM ANALYSIS

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Genomics, Proteomics & Bioinformatics 2005年第3期3卷 189-193页

作者： Hua Qu Liu-Pu Wang Yan-Chun Liang Chun-Guo Wu College of Software Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Guangzhou Institute of China Telecom Guangzhou 510630 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Key Laboratory of Information Science and Engineering of the Ministry of Railway/Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing Jiaotong University Beijing 100044 China.

Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the quality of clustering results is enhanced obviously, the mining expression models are better, and the data possess a strong consistency with fluctuation on the condition while the computational time does not increase significantly.

关键词： biclustering algorithm gene expression pedigree analysis Cheng and Church algorithm

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Bayesian biclustering by dynamics: algorithm testing, comparison against random agglomeration, and calculation of application specific prior information

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METHODSX 2020年 7卷 100897-100897页

作者： Pinto, Helen Gates, Ian Wang, Xin Univ Calgary Geomat Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Univ Calgary Chem & Petr Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

Bayesian biclustering by Dynamics (BBCD) is a new clustering algorithm for Steam-Assisted Gravity Drainage (SAGD) oil recovery time series data [1] . In this companion paper the BBCD algorithm is tested on synthetic data, demonstrating use of the algorithm, as well as its robustness, and performance accuracy against Random Agglomeration. Supplementary information includes formulae to calculate analytical steam and oil volume data used as background knowledge for the SAGD application. Advantages of the BBCD algorithm are listed below. It includes background knowledge directly into the clustering process. It finds similarity between series and over time. It allows a user-specified definition for behaviour of interest, which relaxes dependency on series shape. This is important when similar behavioural events do not necessarily occur in the same temporal order. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Bbcd biclustering algorithm Bayesian statistics Steam-assisted gravity drainage (sagd) application

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Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection

DIGITAL CHEMICAL ENGINEERING

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷

作者： Al-Sakkari, Eslam G. Ragab, Ahmed Amer, Mostafa Ajao, Olumoye Benali, Marzouk Boffito, Daria C. Dagdougui, Hanane Amazouz, Mouloud Polytech Montreal Succ Ctr Ville Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada CanmetENERGY Nat Resources Canada 1615 Lionel Boulet BlvdPOB 4800 Varennes PQ J3X 1P7 Canada Polytech Montreal Succ Ctr Ville Dept Elect Engn CP 6079 Montreal PQ H3C 3A7 Canada Nat Resources Canada Fuel Diversificat Div Clean Fuels Branch 580 Booth St Ottawa ON K1A 0E4 Canada Polytech Montreal Succ Ctr Ville Dept Chem Engn Montreal PQ H3C 3A7 Canada

Several processes and strategies have been developed to promote the utilization of lignin and to facilitate its market adoption across a broad spectrum of applications within the expanding lignin bioeconomy. However, the inherent variability in lignin properties, resulting from diverse feedstock sources and varied recovery and downstream processing methods, remains a significant challenge. This highlights the critical need to investigate lignin's miscibility and reactivity with polymers and solvents, as most lignin valorization pathways involve mixing, blending, or solubilization. Accurate estimation of Hansen solubility parameters (HSP) is crucial for solvent selection in several fields such as polymer science, coatings, adhesives, lignin-based biorefineries and solvent-based carbon capture. Traditional methods for predicting HSP are time-consuming and involve complex experiments, especially in applications dealing with carbon dioxide and lignin solubility. This paper introduces a novel ensemble modeling methodology based on machine learning (ML) techniques for accurate HSP prediction using Simplified Molecular Input Line Entry System (SMILES) codes as entries. The methodology integrates different ML approaches, including deep and shallow learning, to enhance prediction accuracy. Decision fusion of individual ML models is achieved through a hybrid approach combining non-learnable and learnable methods, resulting in reduced errors and enhanced accuracy. The results highlight the effectiveness of the ensemble-based methodology, which achieved 99% accuracy in predicting dispersion solubility parameters, outperforming other individual ML techniques. The proposed generic methodology, from data preprocessing to decision fusion through diverse ML algorithms, can be applied to various chemical analytics beyond HSP prediction.

关键词： Machine learning biclustering algorithm Decision fusion Chemical analytics Lignins Smiles Hansen solubility parameters

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Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms

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algorithmS FOR MOLECULAR BIOLOGY 2010年第1期5卷 23-23页

作者： Chia, Burton Kuan Hui Karuturi, R. Krishna Murthy ASTAR Genome Inst Singapore Singapore Singapore Natl Univ Singapore Sch Comp Singapore 117548 Singapore

Background: biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking. Results: In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking. Conclusions: Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.

关键词： Strong Gene Unify Ranking biclustering algorithm Lung Dataset Iterative Signature algorithm

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Gene Expression Data Analysis Using a Novel Approach to biclustering Combining Discrete and Continuous Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2008年第4期5卷 583-593页

作者： Christinat, Yann Wachmann, Bernd Zhang, Lei Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lab Computat Biol & Bioinformat CH-1015 Lausanne Switzerland Siemens Corp Res Princeton NJ 08540 USA

Many different methods exist for pattern detection in gene expression data. In contrast to classical methods, biclustering has the ability to cluster a group of genes together with a group of conditions (replicates, set of patients, or drug compounds). However, since the problem is NP-complex, most algorithms use heuristic search functions and, therefore, might converge toward local maxima. By using the results of biclustering on discrete data as a starting point for a local search function on continuous data, our algorithm avoids the problem of heuristic initialization. Similar to Order-Preserving Submatrices (OPSM), our algorithm aims to detect biclusters whose rows and columns can be ordered such that row values are growing across the bicluster's columns and vice versa. Results have been generated on the yeast genome (Saccharomyces cerevisiae), a human cancer data set, and random data. Results on the yeast genome showed that 89 percent of the 100 biggest nonoverlapping biclusters were enriched with Gene Ontology annotations. A comparison with the methods OPSM and Iterative Signature algorithm (ISA, a generalization of singular value decomposition) demonstrated a better efficiency when using gene and condition orders. We present results on random and real data sets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters.

关键词： Data mining biclustering algorithm gene expression data discrete data simultaneous clustering microarray analysis

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