Results 71 to 80 of about 7,645,086 (295)

Knowledge Graph Embedding

, 2017
A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis ...
Hailun Lin, Yantao Jia, Xueqi Cheng, Yuanzhuo Wang, Xiaolong Jin   +4 more
core   +1 more source

Intrapatient tumour heterogeneity and clonal evolution in an autopsy study of metastatic salivary gland cancer

Molecular Oncology, EarlyView.
Tumour heterogeneity and clonal evolution of metastatic salivary gland cancer were evaluated in two patients with adenoid carcinoma and one patient with myoepithelial carcinoma. Radiology‐guided autopsy enabled multi‐region sampling (total samples n = 149), followed by whole‐genome sequencing and phylogenetic reconstruction (17 tumour samples, 4–7 per ...
Gerben Lassche, Niels J. van Ruitenbeek, Charlotte Adang, Luke O'Gorman, Pui Yuen Lee, Willemijn M. Klein, Adriana C. H. van Engen‐van Grunsven, Jack A. Schalken, Sander Bervoets, Gerald W. Verhaegh, Carla M. L. van Herpen   +10 more
wiley   +1 more source

CausE: Towards Causal Knowledge Graph Embedding [PDF]

, 2023
Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC). However, KGE
Zhang, Yichi, Zhang, Wen
core   +1 more source

Evaluating the involvement of autolysosomes in the nuclear translocation of fluorescent proteins

FEBS Open Bio, EarlyView.
Endogenously expressed fluorescent proteins can be degraded by autophagy and transported to cell nuclei via the nuclear pore complex. But in some cell lines, for example, HeLa cells which are positive for immunoreactivity of a receptor ligand, such as UCN I, in cell nuclei, fusion of autolysosome with the nuclear envelope is involved in the nuclear ...
Keiichi Ikeda
wiley   +1 more source

Universal Knowledge Graph Embeddings

Companion Proceedings of the ACM Web Conference 2024
5 pages, 3 ...
N'Dah Jean Kouagou, Caglar Demir, Hamada M. Zahera, Adrian Wilke, Stefan Heindorf, Jiayi Li, Axel-Cyrille Ngonga Ngomo   +6 more
openaire   +3 more sources

Leveraging literals for knowledge graph embeddings

, 2021
Wissensgraphen (Knowledge Graphs, KGs) repräsentieren strukturierte Fakten, die sich aus Entitäten und den zwischen diesen bestehenden Relationen zusammensetzen. Um die Effizienz von KG-Anwendungen zu maximieren, ist es von Vorteil, KGs in einen niedrigdimensionalen Vektorraum zu transformieren.
openaire   +5 more sources

Evaluating the effect of γ‐oryzanol on MASLD pathology using a medaka fish model

FEBS Open Bio, EarlyView.
This study explores a liver disease called MASLD, which is increasing worldwide and can lead to serious damage. Researchers used medaka fish instead of rodents to test a food compound, γ‐oryzanol. Fish fed this compound had less liver fat and healthier gut bacteria.
Yukako Ito, Haduki Asano, Ayano Ueki, Joe Sakamoto, Yasuhiro Kamei, Hayato Tokumoto, Ayaka Yazawa, Shigeki Kamitani   +7 more
wiley   +1 more source

Rule-based data augmentation for knowledge graph embedding

AI Open, 2021
Knowledge graph (KG) embedding models suffer from the incompleteness issue of observed facts. Different from existing solutions that incorporate additional information or employ expressive and complex embedding techniques, we propose to augment KGs by ...
Guangyao Li, Zequn Sun, Lei Qian, Qiang Guo, Wei Hu   +4 more
doaj   +1 more source

Cumulative Social Disadvantage and Disease Activity in Juvenile Idiopathic Arthritis: A Childhood Arthritis and Rheumatology Research Alliance Registry Study

Arthritis Care &Research, EarlyView.
Objective Social determinants of health (SDOH) contribute to juvenile idiopathic arthritis (JIA) disparities, but most studies have assessed SDOH independently rather than cumulatively across individual, family, and neighborhood levels. Using a socioecological framework, we investigated the relationship among cumulative social disadvantage ...
William Daniel Soulsby, John Boscardin, Andrea Knight, Daniel B. Horton, Karine Toupin‐April, Emily von Scheven, on behalf of the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry Investigators and for the CARRA Health Equity Work Group, R. Aamir, K. Abulaban, A. Adams, C. Aguiar Lapsia, H. Ahmed, S. Akoghlanian, A. AlBijadi, E. Allenspach, M. Alpizar, G. Amarilyo, M. Amoruso, S. Angeles‐Han, S. Ardoin, S. Armendariz, N. Aviran Dagan, I. Balboni, S. Balevic, S. Ballinger, S. Baluta, L. Barillas‐Arias, L. Barillas‐Arias, M. Basiaga, K. Baszis, M. Becker, A. Begezda, E. Beil, H. Bell‐Brunson, H. Benham, S. Benseler, L. Bermudez‐Santiago, W. Bernal, T. Bigley, C. Bingham, B. Binstadt, C. Black, B. Blackmon, M. Blakley, J. Bohnsack, A. Boneparth, H. Bradfield, J. Bridges, E. Brooks, M. Brothers, D. Brown, H. Brunner, L. Buckley, Mary Buckley, Meredith Buckley, H. Bukulmez, D. Bullock, A. Cancino, S. Canna, L. Cannon, S. Canny, V. Cartwright, E. Chalom, Johanna Chang, Joyce Chang, M. Chang, A. Chang‐Hoftman, A. Chen, P. Chiraseveenuprapund, K. Ciaglia, M. Cidon, D. Co, E. Cohen, R. Connor, K. Cook, A. Cooper, J. Cooper, K. Corbin, C. Correll, R. Cron, M. Curry, A. Dagci, A. Dalrymple, E. Datyner, T. Davis, D. De Ranieri, J. Dean, C. DeCoste, F. Dedeoglu, M. DeGuzman, N. Delnay, E. L. DeSantis, R. Devine, M. Dhalla, A. Dhanrajani, D. Dissanayake, B. Dizon, J. Drew, K. Driest, Q. Du, E. Duncan, K. Dunnock, D. Durkee, J. Dvergsten, A. Eberhard, K. Ede, B. Edelheit, C. Edens, M. Elder, Y. Elzaki, C. Failing, D. Fair, L. Favier, B. Feldman, J. Fennell, I. Ferguson, P. Ferguson, C. Figueroa, E. Flanagan, L. Fogel, E. Fox, M. Fox, L. Franklin, R. Fuhlbrigge, J. Fuller, T. Futch‐West, S. Gagne, M. Geiszler, D. Gerstbacher, M. Gilbert, A.C. Gironella, D. Glaser, I. Goh, S. Gorry, N. Goswami, B. Gottlieb, T. Graham, S. Grevich, T. Griffin, A. Grim, A. Grom, M. Guevara, L. Guzman, T. Hahn, O. Halyabar, E. Hammelev, T. Hammond, S. Haro, J. Harris, O. Harry, J. Hausmann, A. Hay, K. Hays, K. Hayward, L. Henderson, M. Henrickson, A. Hersh, L. Hiraki, M. Hiskey, P. Hobday, C. Hoffart, M. J. Holland, M. Hollander, S. Hong, D. Horton, J. Hsu, A. Huber, J. Huggins, J. Hui‐Yuen, M. Ibarra, A. Imlay, L. Imundo, C. Inman, A. Jackson, K. James, G. Janow, Y. Jiang, L. Johnson, N. Johnson, J. Jones, D. Kafisheh, K. Kaidar, S. Kasinathan, R. Kaur, E. Kessler, B. Kienzle, S. Kim, Y. Kimura, D. Kingsbury, M. Kitcharoensakkul, J. Klauss, K. Klein, M. Klein‐Gitelman, A. Knight, L. Kovalick, D. Krajewski, C. Kremer, T. LaFlam, B. Lang, S. Lapidus, B. Lapin, A. Lasky, E. Lawson, R. Laxer, A. Lee, Patricia Lee, Pui Lee, T. Lee, E. Leisinger, L. Lentini, M. Lerman, Y. Levinsky, D. Levy, S. Li, S. Lieberman, L. Lim, E. Limenis, C. Lin, N. Ling, G. Lionetti, R. Livny, M. Lo, A. Long, M. Lopez‐Peña, D. Lovell, S. Lvovich, A. Lytch, M. Ma, A. Machado, J. MacMahon, J. Madison, M. Mannion, C. Manos, L. Mansfield, B. Marston, K. Marzan, T. Mason, S. Matossian, L. McAllister, K. McBrearty, D. McCurdy, K. McDaniels, J. McDonald, L. McIntosh, E. Meidan, E. Mellins, Z. Mian, P. Miettunen, M. Miller, D. Milojevic, R. Mitacek, R. Modica, S. Mohan, K. Moore, T. Moore, L. Moorthy, J. Moreno, E. Morgan, A. Moyer, B. Murante, A. Murphy, E. Muscal, O. Mwizerwa, A. Najafi, K. Nanda, L. Nassi, S. Nativ, M. Natter, J. Neely, L. Newhall, A. Nuyen, P. Nigrovic, J. Nocton, B. Nolan, A. Nowakowski, K. Nowicki, R. Oakes, E. Oberle, S. Ogbonnaya‐Whittesley, E. Ogbu, M. Oliver, R. Olveda, K. Onel, A. Orandi, J. Padam, N. Pan, J. Pandya, S. Panupattanapong, A. Pappo Toledano, J. Patel, P. Patel, A. Patrick, S. Patrizi, S. Paul, J. Perfetto, M. Perron, M. Peskin, C. Pinotti, L. Ponder, R. Pooni, S. Prahalad, M. Quinlan‐Waters, J. Rafko, H. Rahimi, S. Ramsey, R. Randell, L. Ray, Ann Reed, Annelle Reed, H. Reid, D. Reiff, I. Reyhan, B. Richard, M. Riebschleger, E. Rife, M. Riskalla, A. Robinson, L. Robinson, L. Rodgers, M. Rodriquez, D. Rogers, T. Ronis, A. Rosado, M. Rosenkranz, N. Rosenwasser, H. Rothermel, D. Rothman, E. Rothschild, K. Rouster ‐ Stevens, T. Rubinstein, N. Ruth, S. Sabbagh, R. Sadun, L. Santiago, V. Saper, A. Sarkissian, L. Scalzi, J. Schahn, K. Schikler, A. Schlefman, B. Schlichting, H. Schmeling, E. Schmitt, G. Schulert, C. Schutt, C. Seper, B. Shaham, R. Sheets, A. Shehab, S. Shenoi, M. Sherman, J. Shirley, M. Shishov, N. Singer, V. Sivaraman, E. Sloan, C. Smith, J. Smith, E. Smitherman, J. Soep, M. B. Son, C. Spencer, L. Spiegel, J. Spitznagle, H. Srinivasalu, H. Stapp, A. Stephens, Y. Sterba Rakovchik, S. Stern, B. Stevens, R. Stevenson, C. Stingl, M. Stoll, E. Stringer, S. Sule, J. Sullivan, R. Sundel, M. Sutter, C. Swaffar, N. Swayne, T. Symington, G. Syverson, A.M. Szymanski, S. Taber, R. Tal, A. Tambralli, A. Taneja, T. Tanner, S. Tarvin, A. Taxter, M. Tesher, T. Thakurdeen, A. Theisen, G. Thieroff, B. Thomas, L. Thomas, N. Thomas, L. Timmerman, T. Ting, C. Todd, D. Toib, K. Torok, H. Tory, M. Toth, E. Treemarcki, S. Tse, T. Tse, C. Tsin, J. Twachtman‐Bassett, M. Twilt, T. Valcarcel, R. Valdovinos, A. Vallee, H. Van Mater, S. Vandenbergen, C. Varghese, N. Vasquez, P. Vega‐Fernandez, J. Verbsky, R. Verstegen, E. von Scheven, S. Vora, L. Wagner‐Weiner, D. Wahezi, S. Wakefield, B. Walker, S. Wallgren, H. Walters, M. Waterfield, J. Weiss, P. Weiss, E. Wershba, V. Westheuser, K. Widrick, C. Williams, S. Wong, S. Wooldridge, L. Woolnough, T. Wright, E. Wu, A. Yalcindag, R. Yeung, K. Yomogida, A. Zeft, Y. J. Zhang, Y. D. Zhao, Z. Zheng, A. Zhu, C. Zic   +448 more
wiley   +1 more source

Knowledge Graph Embedding for Hyper-Relational Data

Tsinghua Science and Technology, 2017
Knowledge graph representation has been a long standing goal of artificial intelligence. In this paper, we consider a method for knowledge graph embedding of hyper-relational data, which are commonly found in knowledge graphs.
Chunhong Zhang, Miao Zhou, Xiao Han, Zheng Hu, Yang Ji   +4 more
doaj   +1 more source