Search Results - graph Algorithms in Bioinformatics

Graph representation learning in bioinformatics: trends, methods and applications by Yi, Hai-Cheng, You, Zhu-Hong, Huang, De-Shuang, Kwoh, Chee Keong

“… Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain…”
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METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs by Zhang, Zhenmiao, Zhang, Lu

“…), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs…”
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Fitchi: haplotype genealogy graphs based on the Fitch algorithm by Matschiner, Michael

“… I here present Fitchi, a new program that produces publication-ready haplotype genealogy graphs based on the Fitch algorithm…”
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Risk stratification and pathway analysis based on graph neural network and interpretable algorithm by Liang, Bilin, Gong, Haifan, Lu, Lu, Xu, Jie

“… Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits…”
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GCphase: an SNP phasing method using a graph partition and error correction algorithm by Luo, Junwei, Wang, Jiayi, Zhai, Haixia, Wang, Junfeng

“… Results In this study, we present a graph-based algorithm, GCphase, which utilizes the minimum cut algorithm to perform phasing…”
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WGT: Tools and algorithms for recognizing, visualizing, and generating Wheeler graphs by Chao, Kuan-Hao, Chen, Pei-Wei, Seshia, Sanjit A., Langmead, Ben

“…A Wheeler graph represents a collection of strings in a way that is particularly easy to index and query…”
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TwoPaCo: an efficient algorithm to build the compacted de Bruijn graph from many complete genomes by Minkin, Ilia, Pham, Son, Medvedev, Paul

“…). In this article, we present TwoPaCo, a simple and scalable low memory algorithm for the direct construction of the compacted de Bruijn graph from a set of complete genomes…”
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Graph classification algorithm based on graph structure embedding by Ma, Tinghuai, Pan, Qian, Wang, Hongmei, Shao, Wenye, Tian, Yuan, Al-Nabhan, Najla

“…•Proposed a graph classification algorithm based on graph embedding. With the application of data mining in many fields such as information science, bioinformatic…”
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Recycler: an algorithm for detecting plasmids from de novo assembly graphs by Rozov, Roye, Brown Kav, Aya, Bogumil, David, Shterzer, Naama, Halperin, Eran, Mizrahi, Itzhak, Shamir, Ron

“… Here, we attempt to ameliorate this situation by introducing a new circular element assembly algorithm, leveraging assembly graphs provided by a conventional de novo assembler and alignments…”
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Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations by Luo, Yuan, Uzuner, Özlem, Szolovits, Peter

“… increasingly critical to enable deep understanding of scientific papers and clinical narratives. Shared task challenges have been organized by both bioinformatics and clinical informatics communities to assess and advance the state-of-the-art research…”
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BOSS: a novel scaffolding algorithm based on an optimized scaffold graph by Luo, Junwei, Wang, Jianxin, Zhang, Zhen, Li, Min, Wu, Fang-Xiang

“… Most existing scaffolding tools adopt scaffold graph approaches. However, due to repetitive regions in genome, sequencing errors and uneven sequencing depth, constructing an accurate scaffold graph is still a challenge task…”
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A graph-based algorithm for detecting rigid domains in protein structures by Dang, Truong Khanh Linh, Nguyen, Thach, Habeck, Michael, Gültas, Mehmet, Waack, Stephan

“… Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm…”
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Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory by Huang, Chien-Hung, Chang, Peter Mu-Hsin, Hsu, Chia-Wei, Huang, Chi-Ying F., Ng, Ka-Lok

“… Results This work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung…”
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Biological network analysis with deep learning by Muzio, Giulia, O’Bray, Leslie, Borgwardt, Karsten

“… One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs…”
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Tree diet: reducing the treewidth to unlock FPT algorithms in RNA bioinformatics by Marchand, Bertrand, Ponty, Yann, Bulteau, Laurent

“…Hard graph problems are ubiquitous in Bioinformatics, inspiring the design of specialized Fixed-Parameter Tractable algorithms, many of which rely on a combination of tree-decomposition and dynamic programming…”
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A Graph Coarsening Algorithm for Compressing Representations of Single-Cell Data with Clinical or Experimental Attributes by Chen, Chi-Jane, Crawford, Emma, Stanley, Natalie

“… Here, we introduce cytocoarsening, a novel graph-coarsening algorithm that significantly reduces the size of single-cell graph representations, which can then be used as input to downstream bio…”
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Matrix Reordering Methods for Table and Network Visualization by Behrisch, Michael, Bach, Benjamin, Henry Riche, Nathalie, Schreck, Tobias, Fekete, Jean-Daniel

“… The goal of this survey is to provide a comprehensive list of reordering algorithms published in different fields such as statistics, bioinformatics, or graph theory…”
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Essential proteins in cancer networks: a graph-based perspective using Dijkstra’s algorithm by Rout, Trilochan, Mohapatra, Anjali, Kar, Madhabananda, Muduly, Dillip Kumar

“… This study introduces a novel graph-based approach to identify essential cancer proteins within PPI networks, focusing on breast, lung, colorectal, and ovarian cancers…”
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma by Ye, Maodong, Ren, Shuai, Luo, Huanjuan, Wu, Xiumin, Lian, Hongwei, Cai, Xiangna, Ji, Yingchang

“… Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies…”
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CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements by Badr, Eman, Heath, Lenwood S.

“…), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy…”
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