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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1881 1
1921 1
1924 1
1925 1
1926 1
1927 1
1928 2
1929 1
1931 2
1932 4
1933 1
1945 4
1946 25
1947 25
1948 9
1949 4
1950 6
1951 3
1952 4
1953 4
1954 5
1955 5
1956 7
1957 4
1958 5
1959 4
1960 5
1961 4
1962 8
1963 7
1964 39
1965 50
1966 19
1967 16
1968 25
1969 30
1970 29
1971 16
1972 20
1973 26
1974 24
1975 76
1976 97
1977 80
1978 98
1979 126
1980 122
1981 96
1982 120
1983 144
1984 158
1985 117
1986 133
1987 124
1988 204
1989 290
1990 308
1991 298
1992 288
1993 316
1994 316
1995 349
1996 345
1997 358
1998 343
1999 372
2000 523
2001 584
2002 587
2003 622
2004 924
2005 1101
2006 1339
2007 1407
2008 1505
2009 1622
2010 1797
2011 1972
2012 2256
2013 2449
2014 2698
2015 2877
2016 3052
2017 3486
2018 3918
2019 3907
2020 4489
2021 5756
2022 6953
2023 7479
2024 8419
2025 7469
2026 4

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75,990 results

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Page 1
Neuronal Graphs: A Graph Theory Primer for Microscopic, Functional Networks of Neurons Recorded by Calcium Imaging.
Nelson CJ, Bonner S. Nelson CJ, et al. Front Neural Circuits. 2021 Jun 10;15:662882. doi: 10.3389/fncir.2021.662882. eCollection 2021. Front Neural Circuits. 2021. PMID: 34177469 Free PMC article. Review.
The formal study of graphs, graph theory, can provide neuroscientists with a large bank of algorithms for exploring networks. ...In this primer we explain the basics of graph theory and relate them to features of microscopic functional networks of neurons fro …
The formal study of graphs, graph theory, can provide neuroscientists with a large bank of algorithms for exploring networks. …
Incomplete graph learning: A comprehensive survey.
Xia R, Liu H, Li A, Liu X, Zhang Y, Zhang C, Yang B. Xia R, et al. Neural Netw. 2025 Oct;190:107682. doi: 10.1016/j.neunet.2025.107682. Epub 2025 Jun 11. Neural Netw. 2025. PMID: 40517747 Review.
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, result …
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extrac …
A Novel Composite Graph Neural Network.
Liu Z, Yang J, Zhong X, Wang W, Chen H, Chang Y. Liu Z, et al. IEEE Trans Neural Netw Learn Syst. 2024 Oct;35(10):13411-13425. doi: 10.1109/TNNLS.2023.3268766. Epub 2024 Oct 7. IEEE Trans Neural Netw Learn Syst. 2024. PMID: 37200114
However, most GNNs can only be applied to scenarios where graphs are known, but real-world data are often noisy or even do not have available graph structures. ...Different from existing methods, our method uses composite graphs (C-graphs) to character …
However, most GNNs can only be applied to scenarios where graphs are known, but real-world data are often noisy or even do not have a …
Pangenome graph construction from genome alignments with Minigraph-Cactus.
Hickey G, Monlong J, Ebler J, Novak AM, Eizenga JM, Gao Y; Human Pangenome Reference Consortium; Marschall T, Li H, Paten B. Hickey G, et al. Nat Biotechnol. 2024 Apr;42(4):663-673. doi: 10.1038/s41587-023-01793-w. Epub 2023 May 10. Nat Biotechnol. 2024. PMID: 37165083 Free PMC article.
Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-re …
Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually …
Graph convolutional networks: a comprehensive review.
Zhang S, Tong H, Xu J, Maciejewski R. Zhang S, et al. Comput Soc Netw. 2019;6(1):11. doi: 10.1186/s40649-019-0069-y. Epub 2019 Nov 10. Comput Soc Netw. 2019. PMID: 37915858 Free PMC article.
However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are o …
However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally str …
Relational graph convolutional networks: a closer look.
Thanapalasingam T, van Berkel L, Bloem P, Groth P. Thanapalasingam T, et al. PeerJ Comput Sci. 2022 Nov 2;8:e1073. doi: 10.7717/peerj-cs.1073. eCollection 2022. PeerJ Comput Sci. 2022. PMID: 36426239 Free PMC article.
In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowled …
In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain …
SkyMap: a generative graph model for GNN benchmarking.
Wassington A, Higueras R, Abadal S. Wassington A, et al. Front Artif Intell. 2024 Nov 14;7:1427534. doi: 10.3389/frai.2024.1427534. eCollection 2024. Front Artif Intell. 2024. PMID: 39610852 Free PMC article.
However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generative model for labeled attributed graphs with a fine-grained control over graph topology and feature distribution parameters. W …
However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generat …
MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality.
Lu X, Chen G, Li J, Hu X, Sun F. Lu X, et al. IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):2681-2689. doi: 10.1109/TCBB.2022.3221736. Epub 2023 Oct 9. IEEE/ACM Trans Comput Biol Bioinform. 2023. PMID: 36374879
However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). ...Meanwhile, we p …
However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs …
A spectral graph convolution for signed directed graphs via magnetic Laplacian.
Ko T, Choi Y, Kim CK. Ko T, et al. Neural Netw. 2023 Jul;164:562-574. doi: 10.1016/j.neunet.2023.05.009. Epub 2023 May 12. Neural Netw. 2023. PMID: 37216757 Free article.
Signed directed graphs contain both sign and direction information on their edges, providing richer information about real-world phenomena compared to unsigned or undirected graphs. ...Consequently, despite their potential uses, signed directed graphs have re …
Signed directed graphs contain both sign and direction information on their edges, providing richer information about real-world phen …
StemP: A Fast and Deterministic Stem-Graph Approach for RNA Secondary Structure Prediction.
Tang M, Hwang K, Kang SH. Tang M, et al. IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):3278-3291. doi: 10.1109/TCBB.2023.3253049. Epub 2023 Oct 9. IEEE/ACM Trans Comput Biol Bioinform. 2023. PMID: 37028040
The main idea is to consider all possible stem with certain stem loop energy and strength to predict RNA secondary structure. We use graph notation, where stems are represented as vertexes, and co-existence between stems as edges. This full Stem-graph presents all p …
The main idea is to consider all possible stem with certain stem loop energy and strength to predict RNA secondary structure. We use grap …
75,990 results
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