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70 results for “gene classification”
Joint representation of molecular networks from multiple species improves gene classification - Data
<p>This is the data the accompanies the manuscript <em>Joint representation of molecular networks from multiple species improves gene classification</em></p> <p>Below is the license agreement for each of the publicly available datasets </p> <ul> <li><a href="https://wiki.thebiogrid.org/doku.php/terms_and_conditions">BioGRID</a></li> <li><a href="http://geneontology.org/docs/go-citation-policy/">GO</a></li> <li><a href="https://www.disgenet.org/legal">DisGeNet</a></li> <li><a href="https://monarchinitiative.org/about/licensing">Monarch</a></li> <li><a href="http://eggnog-mapper.embl.de">eggNOG</a></li> </ul> <p>No license agreement was available on <a href="http://imp.princeton.edu">IMP web site</a>, however we have obtained permission from the owner of the material to redistribute the network.</p>
Classification of Gene Mutations and Cancer-Types with BioBombe Compressed Gene Expression Features
<p>BioBombe analysis applied to gene expression data from The Cancer Genome Atlas (TCGA) PanCanAtlas.</p> <p>Then, the compressed features learned through the serial compression are fed into several machine learning algorithms.</p> <p>The algorithms are used to predict mutation status (for the top 50 most mutated genes in TCGA) and cancer-type using the compressed gene expression features.</p> <p>Method and results described in https://github.com/greenelab/BioBombe. We use the classification approach outlined in https://github.com/greenelab/pancancer</p>
Supervised learning is an accurate method for network-based gene classification - Data
<p>This file contains the data that was used in the paper titled "Supervised learning is an accurate method for network-based gene classification" (https://doi.org/10.1093/bioinformatics/btaa150). Some data was excluded if the license was not permissive enough.</p>
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings - Gene Classification Dataset
<p>The gene classification dataset used in the <a href="https://arxiv.org/abs/2106.13302">byteSteady paper</a>.</p>
Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes
<p>Supplementary Table 1: Identification of DEGs in Cervical Cancer in TCGA Dataset. </p><p>Supplementary Table 2: Acquisition of IPS-Related Genes Associated with Survival.</p>
Supplementary material 1 from: Ge Z-W, Jacobs A, Vellinga EC, Sysouphanthong P, van der Walt R, Lavorato C, An Y-F, Yang ZL (2018) A multi-gene phylogeny of Chlorophyllum (Agaricaceae, Basidiomycota): new species, new combination and infrageneric classification. MycoKeys 32: 65-90. https://doi.org/10.3897/mycokeys.32.23831
Figure S1. Maximum Likelihood tree showing the monophyly of Chlorophyllum inferred from the rpb2 data set : Explanation note: Bootstrap values (>50) are indicated along nodes. The clade where Chlorophyllum species are nested is highlighted in grey.
Data from: A reconsideration of the classification of the spider infraorder Mygalomorphae based on three nuclear genes and morphology (Arachnida: Araneae)
Open the record for dataset details and reuse information.
Figure 6 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105
Figure 6 Phylogenetic relationships of the subgenera of Bombus from the Bayesian Inference tree (Fig. 2); nearly all of them are well supported by posterior probabilities.
Figure 4 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105
Figure 4 Estimated phylogeny of the same samples using both new sequences and sequences from Cameron et al. (2007), based on five combined gene sequences (mitochondrial gene 16S rRNA, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Maximum Likelihood. Values on the branches are the bootstrap values, (C) represents Cameron et al.'s species, and "BG" represents our species. The bold font indicates species from both datasets that did not cluster together into monophyletic clades.
Supplementary material 1 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105
Key to the 26 species of the genus Bombus
Figure 2 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105
Figure 2 Estimated phylogeny of Bombus based on six combined gene sequences (mitochondrial genes 16S rRNA and COI, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Bayesian Inference and Maximum Likelihood. Subgeneric clades are noted at the right of the figure, values above branches are posterior probabilities (BI), values below branches are bootstrap values (ML). Species in bold font were collected by the authors in China and a black spot indicates species that were not included in the previous phylogeny of Bombus of Cameron et al. (2007). The outgroups are at the top of the tree. Abbreviations: SF, short-faced clade; LF, long-faced clade. Subgenera that were synonymized are in parentheses.
Figure 3 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105
Figure 3 Estimated phylogeny of Bombus based on six combined gene sequences (mitochondrial genes 16S rRNA and COI, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Maximum Parsimony. Subgeneric clades are noted at the right of the figure and values on branches are the bootstrap values. Species in bold font were collected in China and a black spot indicates species that were not included in the phylogeny of Bombus of Cameron et al. (2007). The outgroups are at the top of the tree. Abbreviations: SF, short-faced clade; LF, long-faced clade. Subgenera that were synonymized are in parentheses.
Figure 5 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105
Figure 5 Estimated phylogeny of Bombus based on six combined gene sequences (mitochondrial genes 16S rRNA and COI, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Neighbor Joining. Subgeneric clades are noted at the right of the Figure, and values on branches are the bootstrap values of NJ. Species in bold font were collected in China and a black spot indicates species that were not included in the phylogeny of Bombus of Cameron et al. (2007). The outgroups are at the bottom of the tree. Subgenera that were synonymized are in parentheses.
Data from: Molecular phylogenetics of Braconidae (Hymenoptera: Ichneumonoidea) based on multiple nuclear genes and implications for classification
This study examined subfamilial relationships within Braconidae, using 4kb of sequence data for 139 taxa. Genetic sampling included previously used markers for phylogenetic studies of Braconidae (28S and 18S rDNA) as well as new nuclear protein-coding genes (CAD and ACC). Maximum likelihood and Bayesian inference of the concatenated dataset recovered a robust phylogeny, particularly for early divergences within the family. This study focused primarily on non-cyclostome subfamilies, but the monophyly of the cyclostome complex was strongly supported. There was evidence supporting an independent clade, termed the aphidioid complex as sister to the cyclostome complex of subfamilies. Maxfischeria was removed from Helconinae and placed within its own subfamily within the aphidioid complex. Most relationships within the cyclostome complex were poorly supported, likely due to lower taxonomic sampling within this group. Similar to other studies, there was strong support for the alysioid subcomplex containing Gnamptodontinae, Alysiinae, Opiinae, and Exothecinae. Cenocoeliinae was recovered as sister to all other subfamilies within the euphoroid complex. Planitorus and Mannokeraia, previously placed in Betylobraconinae and Masoninae, respectively, were moved to the Euphorinae and may share a close affiliation with Neoneurinae. Neoneurinae and Ecnomiinae were placed as tribes within Euphorinae. A sister relationship between the microgastroid and sigalphoid complexes was also recovered. Within the helconoid complex was a well-supported lineage parasitic on lepidopteran larvae (macrocentroid subcomplex). Helconini was raised to subfamily status and was recovered as sister to the macrocentroid subcomplex. Blacinae was demoted to tribal status and placed within the newly circumscribed subfamily Brachistinae, which also contains the tribes Diospilini, Brulleiini, and Brachistini, all formerly in Helconinae.
Homology-based classification of accessory proteins in coronavirus genomes uncovers extremely dynamic evolution of gene content
<p>Fasta files of ORF sequences for all orthogroups generated in the study.</p>
Supplementary material 2 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Table S2
Supplementary material 4 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Supplementary tree file
Supplementary material 3 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Figure S1
Supplementary material 1 from: Ringelberg JJ, Koenen EJM, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A, Luckow M, Lewis GP, Hughes CE (2022) Phylogenomic analysis of 997 nuclear genes reveals the need for extensive generic re-delimitation in Caesalpinioideae (Leguminosae). In: Hughes CE, de Queiroz LP, Lewis GP (Eds) Advances in Legume Systematics 14. Classification of Caesalpinioideae Part 1: New generic delimitations. PhytoKeys 205: 3-58. https://doi.org/10.3897/phytokeys.205.85866
Table S1
Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.