Artículos de revistas sobre el tema "Omic network inference"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Omic network inference".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Nagpal, Sunil, Rashmi Singh, Deepak Yadav y Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks". Nucleic Acids Research 48, W1 (27 de abril de 2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.
Texto completoDohlman, Anders B. y Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference". Experimental Biology and Medicine 244, n.º 6 (16 de marzo de 2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.
Texto completoRamos, Susana Isabel, Zarmeen Mussa, Bruno Giotti, Alexander Tsankov y Nadejda Tsankova. "EPCO-25. MULTI-OMIC ANALYSIS OF THE GLIOBLASTOMA EPIGENOME AND TRANSCRIPTOME INFORMS OF MIGRATORY INTERNEURON-LIKE DEVELOPMENTAL REGULATORS". Neuro-Oncology 24, Supplement_7 (1 de noviembre de 2022): vii121. http://dx.doi.org/10.1093/neuonc/noac209.460.
Texto completoGrund, Eric M., A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah et al. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis". Cancer Research 82, n.º 12_Supplement (15 de junio de 2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.
Texto completoNathasingh, Brandon, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W. Church y Yaoyu E. Wang. "Abstract LB181: Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model". Cancer Research 83, n.º 8_Supplement (14 de abril de 2023): LB181. http://dx.doi.org/10.1158/1538-7445.am2023-lb181.
Texto completoYe, Qing y Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets". Cells 12, n.º 1 (26 de diciembre de 2022): 101. http://dx.doi.org/10.3390/cells12010101.
Texto completoAlanis-Lobato, Gregorio, Thomas E. Bartlett, Qiulin Huang, Claire S. Simon, Afshan McCarthy, Kay Elder, Phil Snell, Leila Christie y Kathy K. Niakan. "MICA: a multi-omics method to predict gene regulatory networks in early human embryos". Life Science Alliance 7, n.º 1 (25 de octubre de 2023): e202302415. http://dx.doi.org/10.26508/lsa.202302415.
Texto completoWang, Pei. "Network biology: Recent advances and challenges". Gene & Protein in Disease 1, n.º 2 (6 de octubre de 2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.
Texto completoYan, Yan, Feng Jiang, Xinan Zhang y Tianhai Tian. "Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm". Entropy 24, n.º 5 (13 de mayo de 2022): 693. http://dx.doi.org/10.3390/e24050693.
Texto completoBonnet, Eric, Laurence Calzone y Tom Michoel. "Integrative Multi-omics Module Network Inference with Lemon-Tree". PLOS Computational Biology 11, n.º 2 (13 de febrero de 2015): e1003983. http://dx.doi.org/10.1371/journal.pcbi.1003983.
Texto completoWang, Huange, Joao Paulo, Willem Kruijer, Martin Boer, Hans Jansen, Yury Tikunov, Björn Usadel, Sjaak van Heusden, Arnaud Bovy y Fred van Eeuwijk. "Genotype–phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes". Molecular BioSystems 11, n.º 11 (2015): 3101–10. http://dx.doi.org/10.1039/c5mb00477b.
Texto completoZarayeneh, Neda, Euiseong Ko, Jung Hun Oh, Sang Suh, Chunyu Liu, Jean Gao, Donghyun Kim y Mingon Kang. "Integration of multi-omics data for integrative gene regulatory network inference". International Journal of Data Mining and Bioinformatics 18, n.º 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.087178.
Texto completoKang, Mingon, Donghyun Kim, Jean Gao, Chunyu Liu, Sang Suh, Jung Hun Oh, Neda Zarayeneh y Euiseong Ko. "Integration of multi-omics data for integrative gene regulatory network inference". International Journal of Data Mining and Bioinformatics 18, n.º 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.10008266.
Texto completoHu, Xinlin, Yaohua Hu, Fanjie Wu, Ricky Wai Tak Leung y Jing Qin. "Integration of single-cell multi-omics for gene regulatory network inference". Computational and Structural Biotechnology Journal 18 (2020): 1925–38. http://dx.doi.org/10.1016/j.csbj.2020.06.033.
Texto completoPeñagaricano, F. "S0101 Causal inference of molecular networks integrating multi-omics data". Journal of Animal Science 94, suppl_4 (1 de septiembre de 2016): 2. http://dx.doi.org/10.2527/jas2016.94supplement42a.
Texto completoPeñagaricano, F. "0412 Causal inference of molecular networks integrating multi-omics data". Journal of Animal Science 94, suppl_5 (1 de octubre de 2016): 199–200. http://dx.doi.org/10.2527/jam2016-0412.
Texto completoSun, Xiaoqiang, Ji Zhang y Qing Nie. "Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples". PLOS Computational Biology 17, n.º 3 (5 de marzo de 2021): e1008379. http://dx.doi.org/10.1371/journal.pcbi.1008379.
Texto completoGao, Wenliang, Wei Kong, Shuaiqun Wang, Gen Wen y Yaling Yu. "Biomarker Genes Discovery of Alzheimer’s Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia". Brain Sciences 12, n.º 9 (5 de septiembre de 2022): 1196. http://dx.doi.org/10.3390/brainsci12091196.
Texto completoFederico, Anthony, Joseph Kern, Xaralabos Varelas y Stefano Monti. "Structure Learning for Gene Regulatory Networks". PLOS Computational Biology 19, n.º 5 (18 de mayo de 2023): e1011118. http://dx.doi.org/10.1371/journal.pcbi.1011118.
Texto completoCha, Junha y Insuk Lee. "Single-cell network biology for resolving cellular heterogeneity in human diseases". Experimental & Molecular Medicine 52, n.º 11 (noviembre de 2020): 1798–808. http://dx.doi.org/10.1038/s12276-020-00528-0.
Texto completoCapobianco, Enrico. "Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology". Journal of Clinical Medicine 8, n.º 5 (11 de mayo de 2019): 664. http://dx.doi.org/10.3390/jcm8050664.
Texto completoHan, Xudong, Bing Wang, Chenghao Situ, Yaling Qi, Hui Zhu, Yan Li y Xuejiang Guo. "scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data". PLOS Biology 21, n.º 11 (13 de noviembre de 2023): e3002369. http://dx.doi.org/10.1371/journal.pbio.3002369.
Texto completoKim, So Yeon, Eun Kyung Choe, Manu Shivakumar, Dokyoon Kim y Kyung-Ah Sohn. "Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer". Bioinformatics 37, n.º 16 (5 de febrero de 2021): 2405–13. http://dx.doi.org/10.1093/bioinformatics/btab086.
Texto completoVincent, Jonathan, Pierre Martre, Benjamin Gouriou, Catherine Ravel, Zhanwu Dai, Jean-Marc Petit y Marie Pailloux. "RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat –Omics Data". PLOS ONE 10, n.º 5 (19 de mayo de 2015): e0127127. http://dx.doi.org/10.1371/journal.pone.0127127.
Texto completoSchneider, Nimisha, Sergey Korkhov, Alexis Foroozan, Scott Marshall y Renee Deehan. "Causal inferencing of -omics data from The Cancer Genome Atlas: Lung adenocarcinoma tumors for mechanistic disease characterization and feature engineering." Journal of Clinical Oncology 38, n.º 15_suppl (20 de mayo de 2020): e21016-e21016. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21016.
Texto completoYuan, Lin, Le-Hang Guo, Chang-An Yuan, Youhua Zhang, Kyungsook Han, Asoke K. Nandi, Barry Honig y De-Shuang Huang. "Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer". IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, n.º 3 (1 de mayo de 2019): 782–91. http://dx.doi.org/10.1109/tcbb.2018.2866836.
Texto completoPanchal, Viral y Daniel F. Linder. "Reverse engineering gene networks using global–local shrinkage rules". Interface Focus 10, n.º 1 (13 de diciembre de 2019): 20190049. http://dx.doi.org/10.1098/rsfs.2019.0049.
Texto completoChen, Chen, Enakshi Saha, Dawn L. DeMeo, John Quackenbush y Camila M. Lopes-Ramos. "Abstract 3490: Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks". Cancer Research 84, n.º 6_Supplement (22 de marzo de 2024): 3490. http://dx.doi.org/10.1158/1538-7445.am2024-3490.
Texto completoWani, Nisar y Khalid Raza. "MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks". PeerJ Computer Science 7 (28 de enero de 2021): e363. http://dx.doi.org/10.7717/peerj-cs.363.
Texto completoQian, Yichun y Shao-shan Carol Huang. "Improving plant gene regulatory network inference by integrative analysis of multi-omics and high resolution data sets". Current Opinion in Systems Biology 22 (agosto de 2020): 8–15. http://dx.doi.org/10.1016/j.coisb.2020.07.010.
Texto completoBenedetti, Elisa, Nathalie Gerstner, Maja Pučić-Baković, Toma Keser, Karli R. Reiding, L. Renee Ruhaak, Tamara Štambuk et al. "Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference". Metabolites 10, n.º 7 (2 de julio de 2020): 271. http://dx.doi.org/10.3390/metabo10070271.
Texto completoConard, Ashley Mae, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence y Erica Larschan. "TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data". Nucleic Acids Research 49, W1 (14 de junio de 2021): W641—W653. http://dx.doi.org/10.1093/nar/gkab384.
Texto completoZeng, Irene Sui Lan y Thomas Lumley. "Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)". Bioinformatics and Biology Insights 12 (1 de enero de 2018): 117793221875929. http://dx.doi.org/10.1177/1177932218759292.
Texto completoNeutsch, Steffen, Caroline Heneka y Marcus Brüggen. "Inferring astrophysics and dark matter properties from 21 cm tomography using deep learning". Monthly Notices of the Royal Astronomical Society 511, n.º 3 (29 de enero de 2022): 3446–62. http://dx.doi.org/10.1093/mnras/stac218.
Texto completoUltsch, Alfred y Jörn Lötsch. "Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data". International Journal of Molecular Sciences 23, n.º 22 (15 de noviembre de 2022): 14081. http://dx.doi.org/10.3390/ijms232214081.
Texto completoYang, Jiyuan, Sheetal Bhatara, Masayuki Umeda, Shanshan Bradford, SongEun Lim, Tamara Westover, Jing Ma, Lauren Ezzell, Jeffery Klco y Jiyang Yu. "Dissecting Subtype-Specific Tumor-Time Interactions and Underlying Hidden Drivers in Pediatric Acute Myeloid Leukemia Via Single-Cell Multi-Omics". Blood 142, Supplement 1 (28 de noviembre de 2023): 5977. http://dx.doi.org/10.1182/blood-2023-189178.
Texto completoFang, Yan, Jiayin Yu, Yumei Ding y Xiaohua Lin. "Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning". Mathematics 11, n.º 22 (20 de noviembre de 2023): 4709. http://dx.doi.org/10.3390/math11224709.
Texto completoKlepikova, Anna V. y Aleksey A. Penin. "Gene Expression Maps in Plants: Current State and Prospects". Plants 8, n.º 9 (28 de agosto de 2019): 309. http://dx.doi.org/10.3390/plants8090309.
Texto completoChen, Xi, Yuan Wang, Antonio Cappuccio, Wan-Sze Cheng, Frederique Ruf Zamojski, Venugopalan D. Nair, Clare M. Miller et al. "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data". Nature Computational Science 3, n.º 7 (25 de julio de 2023): 644–57. http://dx.doi.org/10.1038/s43588-023-00476-5.
Texto completoGuo, Tingbo, Haiqi Zhu, Xiao Wang, Jia Wang, Xinyu Zhou, Yuhui Wei, Pengtao Dang, Chi Zhang y Sha Cao. "Abstract 2072: Computational modeling of metabolic variations in tumor microenvironment". Cancer Research 83, n.º 7_Supplement (4 de abril de 2023): 2072. http://dx.doi.org/10.1158/1538-7445.am2023-2072.
Texto completoJin, Qiao y Ronald Ching Wan Ma. "Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies". Cells 10, n.º 11 (21 de octubre de 2021): 2832. http://dx.doi.org/10.3390/cells10112832.
Texto completoSchwaber, Jessica L., Darren Korbie, Stacey Andersen, Erica Lin, Panagiotis K. Chrysanthopoulos, Matt Trau y Lars K. Nielsen. "Network mapping of primary CD34+ cells by Ampliseq based whole transcriptome targeted resequencing identifies unexplored differentiation regulatory relationships". PLOS ONE 16, n.º 2 (5 de febrero de 2021): e0246107. http://dx.doi.org/10.1371/journal.pone.0246107.
Texto completoMajumdar, Abhishek, Yueze Liu, Yaoqin Lu, Shaofeng Wu y Lijun Cheng. "kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression". Genes 12, n.º 6 (30 de mayo de 2021): 844. http://dx.doi.org/10.3390/genes12060844.
Texto completoClark, Natalie M., Trevor M. Nolan, Ping Wang, Gaoyuan Song, Christian Montes, Conner T. Valentine, Hongqing Guo, Rosangela Sozzani, Yanhai Yin y Justin W. Walley. "Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis". Nature Communications 12, n.º 1 (6 de octubre de 2021). http://dx.doi.org/10.1038/s41467-021-26165-3.
Texto completoBen Guebila, Marouen, Tian Wang, Camila M. Lopes-Ramos, Viola Fanfani, Des Weighill, Rebekka Burkholz, Daniel Schlauch et al. "The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks". Genome Biology 24, n.º 1 (9 de marzo de 2023). http://dx.doi.org/10.1186/s13059-023-02877-1.
Texto completoKim, Daniel, Andy Tran, Hani Jieun Kim, Yingxin Lin, Jean Yee Hwa Yang y Pengyi Yang. "Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data". npj Systems Biology and Applications 9, n.º 1 (19 de octubre de 2023). http://dx.doi.org/10.1038/s41540-023-00312-6.
Texto completoFotuhi Siahpirani, Alireza, Sara Knaack, Deborah Chasman, Morten Seirup, Rupa Sridharan, Ron Stewart, James Thomson y Sushmita Roy. "Dynamic regulatory module networks for inference of cell type-specific transcriptional networks". Genome Research, 15 de junio de 2022, gr.276542.121. http://dx.doi.org/10.1101/gr.276542.121.
Texto completoOgris, Christoph, Yue Hu, Janine Arloth y Nikola S. Müller. "Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data". Scientific Reports 11, n.º 1 (24 de marzo de 2021). http://dx.doi.org/10.1038/s41598-021-85544-4.
Texto completoCapobianco, Enrico, Elisabetta Marras y Antonella Travaglione. "Multiscale Characterization of Signaling Network Dynamics through Features". Statistical Applications in Genetics and Molecular Biology 10, n.º 1 (20 de enero de 2011). http://dx.doi.org/10.2202/1544-6115.1657.
Texto completoZhang, Shilu, Saptarshi Pyne, Stefan Pietrzak, Spencer Halberg, Sunnie Grace McCalla, Alireza Fotuhi Siahpirani, Rupa Sridharan y Sushmita Roy. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets". Nature Communications 14, n.º 1 (27 de mayo de 2023). http://dx.doi.org/10.1038/s41467-023-38637-9.
Texto completo