20 results on '"Ethan, Xu"'
Search Results
2. CRAG - Comprehensive RAG Benchmark.
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Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang, Lingkun Kong, Brian Moran, Jiaqi Wang, Yifan Ethan Xu, An Yan, Chenyu Yang, Eting Yuan, Hanwen Zha, Nan Tang 0001, Lei Chen 0002, Nicolas Scheffer, Yue Liu, Nirav Shah, Rakesh Wanga, Anuj Kumar, Wen-tau Yih, and Xin Luna Dong
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- 2024
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3. SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM.
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Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul A. Crook, Yifan Ethan Xu, Xin Luna Dong, Christos Faloutsos, Lei Li 0005, Babak Damavandi, and Seungwhan Moon
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- 2024
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4. Are Large Language Models a Good Replacement of Taxonomies?
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Yushi Sun, Hao Xin, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang 0001, and Lei Chen 0002
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- 2024
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5. Tutorials at The Web Conference 2023.
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Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen 0016, Peng Cui 0001, Jeffrey Dalton 0001, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo 0002, Torsten Hahmann, Jiawei Han 0001, Soyeon Caren Han, Estevam Hruschka, Liang Hu 0004, Jiaxin Huang 0001, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi 0001, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee 0001, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li 0001, Ying Li 0012, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London 0001, Siqu Long, Hande Küçük-McGinty, Yu Meng 0001, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy 0009, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang 0040, Minjie Wang, Shoujin Wang, Xin Wang 0019, Ingmar Weber, Henry Weld, Lingfei Wu 0001, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang 0011, Ke Yang 0003, Elad Yom-Tov, Jaemin Yoo, Zhou Yu 0005, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang 0020, Xikun Zhang 0002, Yongfeng Zhang, Yu Zhang 0044, Zheng Zhang 0047, Liang Zhao 0002, Xiangyu Zhao 0001, and Wenwu Zhu 0001
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- 2023
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6. Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog.
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Kewei Cheng, Xian Li, Zhengyang Wang, Chenwei Zhang, Binxuan Huang, Yifan Ethan Xu, Xin Luna Dong, and Yizhou Sun
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- 2023
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7. Towards Next-Generation Intelligent Assistants Leveraging LLM Techniques.
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Xin Luna Dong, Seungwhan Moon, Yifan Ethan Xu, Kshitiz Malik, and Zhou Yu 0005
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- 2023
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8. PGE: Robust Product Graph Embedding Learning for Error Detection.
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Kewei Cheng, Xian Li, Yifan Ethan Xu, Xin Luna Dong, and Yizhou Sun
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- 2022
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9. Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?
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Kai Sun, Yifan Ethan Xu, Hanwen Zha, Yue Liu, and Xin Luna Dong
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- 2023
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10. Picsou: Enabling Efficient Cross-Consensus Communication.
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Reginald Frank, Micah Murray, Suyash Gupta, Ethan Xu, Natacha Crooks, and Manos Kapritsos
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- 2023
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11. Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data.
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Yaqing Wang, Yifan Ethan Xu, Xian Li, Xin Luna Dong, and Jing Gao 0004
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- 2020
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12. AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types.
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Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang 0004, Jun Ma 0029, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao 0002, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, and Jiawei Han 0001
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- 2020
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13. Sleep apnoea is a risk factor for severe COVID-19
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Riitta Kaarteenaho, Nina Hautala, Athena Matakidou, Jaakko Kaprio, Ying Wu, Kai Kaarniranta, Peeter Karihtala, Kari Pulkki, Wei Zhou, Caroline Fox, Jussi Pihlajamäki, Apinya Lertratanakul, Juha Paloneva, Johannes Kettunen, Marita Kalaoja, Markus Perola, Veikko Salomaa, Hilkka Soininen, Mika Kähönen, Hao Chen, Andrey Loboda, Soumitra Ghosh, Anders Mälarstig, Markku Laakso, Marja Luodonpää, Markus Juonala, Xing Chen, Marika Crohns, Juhani Junttila, Sirkku Peltonen, Keith Usiskin, Juha Sinisalo, Aarno Palotie, Samuli Ripatti, Oili Kaipiainen-Seppänen, Aki Havulinna, Satu Strausz, Tuomo Kiiskinen, Martin Broberg, Sanni Ruotsalainen, Jukka Koskela, Adel Bachour, Tuula Palotie, Hanna M. Ollila, Mark Daly, Howard Jacob, Heiko Runz, Sally John, Robert Plenge, Mark McCarthy, Julie Hunkapiller, Meg Ehm, Dawn Waterworth, Kathy Klinger, Kathy Call, Tomi Mäkelä, Petri Virolainen, Terhi Kilpi, Jukka Partanen, Anne Pitkäranta, Seppo Vainio, Kimmo Savinainen, Veli-Matti Kosma, Urho Kujala, Outi Tuovila, Minna Hendolin, Raimo Pakkanen, Jeff Waring, States Bridget Riley-Gillis, Jimmy Liu, Shameek Biswas, Dorothee Diogo, Anders Pfizer, Catherine Marshall, Xinli Hu, Matthias Gossel, Johanna Schleutker, Mikko Arvas, Olli Carpen, Reetta Hinttala, Reijo Laaksonen, Arto Mannermaa, Valtteri Julkunen, Anne Remes, Reetta Kälviäinen, Mikko Hiltunen, Jukka Peltola, Pentti Tienari, Juha Rinne, Adam Ziemann, Jeffrey Waring, Sahar Esmaeeli, Nizar Smaoui, Anne Lehtonen, Susan Eaton, Sanni Lahdenperä, John Michon, Geoff Kerchner, Natalie Bowers, Edmond Teng, John Merck, Vinay Mehta, Padhraig Gormley, Kari Linden, Christopher Whelan, Fanli Xu, David Pulford, Martti Färkkilä, Sampsa Pikkarainen, Airi Jussila, Timo Blomster, Mikko Kiviniemi, Markku Voutilainen, Bob Georgantas, Graham Heap, Fedik Rahimov, Joseph Maranville, Tim Lu, Danny Oh, Kirsi Kalpala, Melissa Miller, Linda McCarthy, Kari Eklund, Antti Palomäki, Pia Isomäki, Laura Pirilä, Johanna Huhtakangas, David Close, Marla Hochfeld, Nan Bing, Jorge Esparza Gordillo, Nina Mars, Tarja Laitinen, Margit Pelkonen, Paula Kauppi, Hannu Kankaanranta, Terttu Harju, Steven GreenbergCelgene, Hubert Chen, Jo Betts, Teemu Niiranen, Kaj Metsärinne, Marja-Riitta Taskinen, Tiinamaija Tuomi, Jari Laukkanen, Andrew Peterson Ben Challis, Audrey Chu, Jaakko Parkkinen, Anthony Muslin, Heikki Joensuu, Tuomo Meretoja, Lauri Aaltonen, Annika Auranen, Saila Kauppila, Päivi Auvinen, Klaus Elenius, Relja Popovic, Bridget Riley-Gillis, Jennifer Schutzman, Aparna Chhibber, Heli Lehtonen, Stefan McDonough, Diptee Kulkarni, Joni Turunen, Terhi Ollila, Sanna Seitsonen, Hannu Uusitalo, Vesa Aaltonen, Hannele Uusitalo-Järvinen, Erich Strauss, Anna Podgornaia, Joshua Hoffman, Kaisa Tasanen, Laura Huilaja, Katariina Hannula-Jouppi, Teea Salmi, Leena Koulu, Ilkka Harvima, David Choy, Anu Jalanko, Risto Kajanne, Ulrike Lyhs, Mari Kaunisto, Justin Davis, Danjuma Quarless, Slavé Petrovski, Chia-Yen Chen, Paola Bronson, Robert Yang, Diana Chang, Tushar Bhangale, Emily Holzinger, Xulong Wang, Åsa Hedman, Kirsi Auro, Clarence Wang, Ethan Xu, Franck Auge, Clement Chatelain, Mitja Kurki, Juha Karjalainen, Kimmo Palin, Priit Palta, Pietro Della, Susanna Lemmelä, Manuel Rivas, Jarmo Harju, Arto Lehisto, Andrea Ganna, Vincent Llorens, Antti Karlsson, Kati Kristiansson, Kati Hyvärinen, Jarmo Ritari, Tiina Wahlfors, Miika Koskinen, Katri Pylkäs, Minna Karjalainen, Tuomo Mantere, Eeva Kangasniemi, Sami Heikkinen, Eija Laakkonen, Juha Kononen, Anu Loukola, Päivi Laiho, Tuuli Sistonen, Essi Kaiharju, Markku Laukkanen, Elina Järvensivu, Sini Lähteenmäki, Lotta Männikkö, Regis Wong, Hannele Mattsson, Tero Hiekkalinna, Manuel Jiménez, Kati Donner, Kalle Pärn, Javier Nunez-Fontarnau, Elina Kilpeläinen, Timo Sipilä, Georg Brein, Alexander Dada, Ghazal Awaisa, Anastasia Shcherban, Tuomas Sipilä, Hannele Laivuori, Harri Siirtola, Javier Tabuenca, Lila Kallio, Sirpa Soini, Kimmo Pitkänen, and Teijo Kuopio
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Medicine ,Diseases of the respiratory system ,RC705-779 - Abstract
Background Obstructive sleep apnoea (OSA) is associated with higher body mass index (BMI), diabetes, older age and male gender, which are all risk factors for severe COVID-19.We aimed to study if OSA is an independent risk factor for COVID-19 infection or for severe COVID-19.Methods OSA diagnosis and COVID-19 infection were extracted from the hospital discharge, causes of death and infectious diseases registries in individuals who participated in the FinnGen study (n=260 405). Severe COVID-19 was defined as COVID-19 requiring hospitalisation. Multivariate logistic regression model was used to examine association. Comorbidities for either COVID-19 or OSA were selected as covariates. We performed a meta-analysis with previous studies.Results We identified 445 individuals with COVID-19, and 38 (8.5%) of them with OSA of whom 19 out of 91 (20.9%) were hospitalised. OSA associated with COVID-19 hospitalisation independent from age, sex, BMI and comorbidities (p-unadjusted=5.13×10−5, OR-adjusted=2.93 (95% CI 1.02 to 8.39), p-adjusted=0.045). OSA was not associated with the risk of contracting COVID-19 (p=0.25). A meta-analysis of OSA and severe COVID-19 showed association across 15 835 COVID-19 positive controls, and n=1294 patients with OSA with severe COVID-19 (OR=2.37 (95% 1.14 to 4.95), p=0.021).Conclusion Risk for contracting COVID-19 was the same for patients with OSA and those without OSA. In contrast, among COVID-19 positive patients, OSA was associated with higher risk for hospitalisation. Our findings are in line with earlier works and suggest OSA as an independent risk factor for severe COVID-19.
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- 2021
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14. Efficient Knowledge Graph Accuracy Evaluation.
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Junyang Gao, Xian Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong, and Jun Yang 0001
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- 2019
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15. New insights into the genetic etiology of Alzheimer's disease and related dementias
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Bellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., Holmans, P. A., Boland, A., Damotte, V., van der Lee, S. J., Costa, M. R., Kuulasmaa, T., Yang, Q., de Rojas, I., Bis, J. C., Yaqub, A., Prokic, I., Chapuis, J., Ahmad, S., Giedraitis, V., Aarsland, D., Garcia-Gonzalez, P., Abdelnour, C., Alarcón-Martín, E., Alcolea, D., Alegret, M., Alvarez, I., Álvarez, V., Armstrong, N. J., Tsolaki, A., Antúnez, C., Appollonio, I., Arcaro, M., Archetti, S., Pastor, A. A., Arosio, B., Athanasiu, L., Bailly, H., Banaj, N., Baquero, M., Barral, S., Beiser, A., Pastor, A. B., Below, J. E., Benchek, P., Benussi, L., Berr, C., Besse, C., Bessi, V., Binetti, G., Bizarro, A., Blesa, R., Boada, M., Boerwinkle, E., Borroni, B., Boschi, S., Bossù, P., Bråthen, G., Bressler, J., Bresner, C., Brodaty, H., Brookes, K. J., Brusco, L. I., Buiza-Rueda, D., Bûrger, K., Burholt, V., Bush, W. S., Calero, M., Cantwell, L. B., Chene, G., Chung, J., Cuccaro, M. L., Carracedo, Á., Cecchetti, R., Cervera-Carles, L., Charbonnier, C., Chen, H. -H., Chillotti, C., Ciccone, S., Claassen, J. A. H. R., Clark, C., Conti, E., Corma-Gómez, A., Costantini, E., Custodero, C., Daian, D., Dalmasso, M. C., Daniele, A., Dardiotis, E., Dartigues, J. -F., de Deyn, P. P., de Paiva Lopes, K., de Witte, L. D., Debette, S., Deckert, J., del Ser, T., Denning, N., Destefano, A., Dichgans, M., Diehl-Schmid, J., Diez-Fairen, M., Rossi, P. D., Djurovic, S., Duron, E., Düzel, E., Dufouil, C., Eiriksdottir, G., Engelborghs, S., Escott-Price, V., Espinosa, A., Ewers, M., Faber, K. M., Fabrizio, T., Nielsen, S. F., Fardo, D. W., Farotti, L., Fenoglio, C., Fernández-Fuertes, M., Ferrari, R., Ferreira, C. B., Ferri, E., Fin, B., Fischer, P., Fladby, T., Fließbach, K., Fongang, B., Fornage, M., Fortea, J., Foroud, T. M., Fostinelli, S., Fox, N. C., Franco-Macías, E., Bullido, M. J., Frank-García, A., Froelich, L., Fulton-Howard, B., Galimberti, D., García-Alberca, J. M., García-González, P., Garcia-Madrona, S., Garcia-Ribas, G., Ghidoni, R., Giegling, I., Giorgio, G., Goate, A. M., Goldhardt, O., Gomez-Fonseca, D., González-Pérez, A., Graff, C., Grande, G., Green, E., Grimmer, T., Grünblatt, E., Grunin, M., Gudnason, V., Guetta-Baranes, T., Haapasalo, A., Hadjigeorgiou, G., Haines, J. L., Hamilton-Nelson, K. L., Hampel, H., Hanon, O., Hardy, J., Hartmann, A. M., Hausner, L., Harwood, J., Heilmann-Heimbach, S., Helisalmi, S., Heneka, M. T., Hernández, I., Herrmann, M. J., Hoffmann, P., Holmes, C., Holstege, H., Vilas, R. H., Hulsman, M., Humphrey, J., Biessels, G. J., Jian, X., Johansson, C., Jun, G. R., Kastumata, Y., Kauwe, J., Kehoe, P. G., Kilander, L., Ståhlbom, A. K., Kivipelto, M., Koivisto, A., Kornhuber, J., Kosmidis, M. H., Kukull, W. A., Kuksa, P. P., Kunkle, B. W., Kuzma, A. B., Lage, C., Laukka, E. J., Launer, L., Lauria, A., Lee, C. -Y., Lehtisalo, J., Lerch, O., Lleó, A., Longstreth, W., Lopez, O., de Munain, A. L., Love, S., Löwemark, M., Luckcuck, L., Lunetta, K. L., Ma, Y., Macías, J., Macleod, C. A., Maier, W., Mangialasche, F., Spallazzi, M., Marquié, M., Marshall, R., Martin, E. R., Montes, A. M., Rodríguez, C. M., Masullo, C., Mayeux, R., Mead, S., Mecocci, P., Medina, M., Meggy, A., Mehrabian, S., Mendoza, S., Menéndez-González, M., Mir, P., Moebus, S., Mol, M., Molina-Porcel, L., Montrreal, L., Morelli, L., Moreno, F., Morgan, K., Mosley, T., Nöthen, M. M., Muchnik, C., Mukherjee, S., Nacmias, B., Ngandu, T., Nicolas, G., Nordestgaard, B. G., Olaso, R., Orellana, A., Orsini, M., Ortega, G., Padovani, A., Paolo, C., Papenberg, G., Parnetti, L., Pasquier, F., Pastor, P., Peloso, G., Pérez-Cordón, A., Pérez-Tur, J., Pericard, P., Peters, O., Pijnenburg, Y. A. L., Pineda, J. A., Piñol-Ripoll, G., Pisanu, C., Polak, T., Popp, J., Posthuma, D., Priller, J., Puerta, R., Quenez, O., Quintela, I., Thomassen, J. Q., Rábano, A., Rainero, I., Rajabli, F., Ramakers, I., Real, L. M., Reinders, M. J. T., Reitz, C., Reyes-Dumeyer, D., Ridge, P., Riedel-Heller, S., Riederer, P., Roberto, N., Rodriguez-Rodriguez, E., Rongve, A., Allende, I. R., Rosende-Roca, M., Royo, J. L., Rubino, E., Rujescu, D., Sáez, M. E., Sakka, P., Saltvedt, I., Sanabria, Á., Sánchez-Arjona, M. B., Sanchez-Garcia, F., Juan, P. S., Sánchez-Valle, R., Sando, S. B., Sarnowski, C., Satizabal, C. L., Scamosci, M., Scarmeas, N., Scarpini, E., Scheltens, P., Scherbaum, N., Scherer, M., Schmid, M., Schneider, A., Schott, J. M., Selbæk, G., Seripa, D., Serrano, M., Sha, J., Shadrin, A. A., Skrobot, O., Slifer, S., Snijders, G. J. L., Soininen, H., Solfrizzi, V., Solomon, A., Song, Y. E., Sorbi, S., Sotolongo-Grau, O., Spalletta, G., Spottke, A., Squassina, A., Stordal, E., Tartan, J. P., Tárraga, L., Tesí, N., Thalamuthu, A., Thomas, T., Tosto, G., Traykov, L., Tremolizzo, L., Tybjærg-Hansen, A., Uitterlinden, A., Ullgren, A., Ulstein, I., Valero, S., Valladares, O., Broeckhoven, C. V., Vance, J., Vardarajan, B. N., van der Lugt, A., Dongen, J. V., van Rooij, J., van Swieten, J., Vandenberghe, R., Verhey, F., Vidal, J. -S., Vogelgsang, J., Vyhnalek, M., Wagner, M., Wallon, D., Wang, L. -S., Wang, R., Weinhold, L., Wiltfang, J., Windle, G., Woods, B., Yannakoulia, M., Zare, H., Zhao, Y., Zhang, X., Zhu, C., Zulaica, M., Laczo, J., Matoska, V., Serpente, M., Assogna, F., Piras, F., Ciullo, V., Shofany, J., Ferrarese, C., Andreoni, S., Sala, G., Zoia, C. P., Zompo, M. D., Benussi, A., Bastiani, P., Takalo, M., Natunen, T., Laatikainen, T., Tuomilehto, J., Antikainen, R., Strandberg, T., Lindström, J., Peltonen, M., Abraham, R., Al-Chalabi, A., Bass, N. J., Brayne, C., Brown, K. S., Collinge, J., Craig, D., Deloukas, P., Fox, N., Gerrish, A., Gill, M., Gwilliam, R., Harold, D., Hollingworth, P., Johnston, J. A., Jones, L., Lawlor, B., Livingston, G., Lovestone, S., Lupton, M., Lynch, A., Mann, D., Mcguinness, B., Mcquillin, A., O’Donovan, M. C., Owen, M. J., Passmore, P., Powell, J. F., Proitsi, P., Rossor, M., Shaw, C. E., Smith, A. D., Gurling, H., Todd, S., Mummery, C., Ryan, N., Lacidogna, G., Adarmes-Gómez, A., Mauleón, A., Pancho, A., Gailhajenet, A., Lafuente, A., Macias-García, D., Martín, E., Pelejà, E., Carrillo, F., Merlín, I. S., Garrote-Espina, L., Vargas, L., Carrion-Claro, M., Marín, M., Labrador, M., Buendia, M., Alonso, M. D., Guitart, M., Moreno, M., Ibarria, M., Periñán, M., Aguilera, N., Gómez-Garre, P., Cañabate, P., Escuela, R., Pineda-Sánchez, R., Vigo-Ortega, R., Jesús, S., Preckler, S., Rodrigo-Herrero, S., Diego, S., Vacca, A., Roveta, F., Salvadori, N., Chipi, E., Boecker, H., Laske, C., Perneczky, R., Anastasiou, C., Janowitz, D., Malik, R., Anastasiou, A., Parveen, K., López-García, S., Antonell, A., Mihova, K. Y., Belezhanska, D., Weber, H., Kochen, S., Solis, P., Medel, N., Lisso, J., Sevillano, Z., Politis, D. G., Cores, V., Cuesta, C., Ortiz, C., Bacha, J. I., Rios, M., Saenz, A., Abalos, M. S., Kohler, E., Palacio, D. L., Etchepareborda, I., Kohler, M., Novack, G., Prestia, F. A., Galeano, P., Castaño, E. M., Germani, S., Toso, C. R., Rojo, M., Ingino, C., Mangone, C., Rubinsztein, D. C., Teipel, S., Fievet, N., Deramerourt, V., Forsell, C., Thonberg, H., Bjerke, M., Roeck, E. D., Martínez-Larrad, M. T., Olivar, N., Cano, A., Macias, J., Maroñas, O., Nuñez-Llaves, R., Olivé, C., Adarmes-Gómez, A. D., Amer-Ferrer, G., Antequera, M., Burguera, J. A., Casajeros, M. J., Martinez de Pancorbo, M., Hevilla, S., Espinosa, M. A. L., Legaz, A., Manzanares, S., Marín-Muñoz, J., Marín, T., Martínez, B., Martínez, V., Martínez-Lage Álvarez, P., Iriarte, M. M., Periñán-Tocino, M. T., Real de Asúa, D., Rodrigo, S., Sastre, I., Vicente, M. P., Vivancos, L., Epelbaum, J., Hannequin, D., Campion, D., Deramecourt, V., Tzourio, C., Brice, A., Dubois, B., Williams, A., Thomas, C., Davies, C., Nash, W., Dowzell, K., Morales, A. C., Bernardo-Harrington, M., Turton, J., Lord, J., Brown, K., Vardy, E., Fisher, E., Warren, J. D., Ryan, N. S., Guerreiro, R., Uphill, J., Bass, N., Heun, R., Kölsch, H., Schürmann, B., Lacour, A., Herold, C., Powell, J., Patel, Y., Hodges, A., Becker, T., Warden, D., Wilcock, G., Clarke, R., Ben-Shlomo, Y., Hooper, N. M., Pickering-Brown, S., Sussams, R., Warner, N., Bayer, A., Heuser, I., Drichel, D., Klopp, N., Mayhaus, M., Riemenschneider, M., Pinchler, S., Feulner, T., Gu, W., van den Bussche, H., Hüll, M., Frölich, L., Wichmann, H. -E., Jöckel, K. -H., O’Donovan, M., Owen, M., Bahrami, S., Bosnes, I., Selnes, P., Bergh, S., Palotie, A., Daly, M., Jacob, H., Matakidou, A., Runz, H., John, S., Plenge, R., Mccarthy, M., Hunkapiller, J., Ehm, M., Waterworth, D., Fox, C., Malarstig, A., Klinger, K., Call, K., Behrens, T., Loerch, P., Mäkelä, T., Kaprio, J., Virolainen, P., Pulkki, K., Kilpi, T., Perola, M., Partanen, J., Pitkäranta, A., Kaarteenaho, R., Vainio, S., Turpeinen, M., Serpi, R., Laitinen, T., Mäkelä, J., Kosma, V. -M., Kujala, U., Tuovila, O., Hendolin, M., Pakkanen, R., Waring, J., Riley-Gillis, B., Liu, J., Biswas, S., Diogo, D., Marshall, C., Hu, X., Gossel, M., Graham, R., Cummings, B., Ripatti, S., Schleutker, J., Arvas, M., Carpén, O., Hinttala, R., Kettunen, J., Mannermaa, A., Laukkanen, J., Julkunen, V., Remes, A., Kälviäinen, R., Peltola, J., Tienari, P., Rinne, J., Ziemann, A., Esmaeeli, S., Smaoui, N., Lehtonen, A., Eaton, S., Lahdenperä, S., van Adelsberg, J., Michon, J., Kerchner, G., Bowers, N., Teng, E., Eicher, J., Mehta, V., Gormley, P., Linden, K., Whelan, C., Xu, F., Pulford, D., Färkkilä, M., Pikkarainen, S., Jussila, A., Blomster, T., Kiviniemi, M., Voutilainen, M., Georgantas, B., Heap, G., Rahimov, F., Usiskin, K., Lu, T., Oh, D., Kalpala, K., Miller, M., Mccarthy, L., Eklund, K., Palomäki, A., Isomäki, P., Pirilä, L., Kaipiainen-Seppänen, O., Huhtakangas, J., Lertratanakul, A., Hochfeld, M., Bing, N., Gordillo, J. 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M, Humphrey, J, Biessels, G, Jian, X, Johansson, C, Jun, G, Kastumata, Y, Kauwe, J, Kehoe, P, Kilander, L, Ståhlbom, A, Kivipelto, M, Koivisto, A, Kornhuber, J, Kosmidis, M, Kukull, W, Kuksa, P, Kunkle, B, Kuzma, A, Lage, C, Laukka, E, Launer, L, Lauria, A, Lee, C, Lehtisalo, J, Lerch, O, Lleó, A, Longstreth, W, Lopez, O, de Munain, A, Love, S, Löwemark, M, Luckcuck, L, Lunetta, K, Ma, Y, Macías, J, Macleod, C, Maier, W, Mangialasche, F, Spallazzi, M, Marquié, M, Marshall, R, Martin, E, Montes, A, Rodríguez, C, Masullo, C, Mayeux, R, Mead, S, Mecocci, P, Medina, M, Meggy, A, Mehrabian, S, Mendoza, S, Menéndez-González, M, Mir, P, Moebus, S, Mol, M, Molina-Porcel, L, Montrreal, L, Morelli, L, Moreno, F, Morgan, K, Mosley, T, Nöthen, M, Muchnik, C, Mukherjee, S, Nacmias, B, Ngandu, T, Nicolas, G, Nordestgaard, B, Olaso, R, Orellana, A, Orsini, M, Ortega, G, Padovani, A, Paolo, C, Papenberg, G, Parnetti, L, Pasquier, F, Pastor, P, Peloso, G, Pérez-Cordón, A, Pérez-Tur, J, Pericard, P, Peters, O, Pijnenburg, Y, Pineda, J, Piñol-Ripoll, G, Pisanu, C, Polak, T, Popp, J, Posthuma, D, Priller, J, Puerta, R, Quenez, O, Quintela, I, Thomassen, J, Rábano, A, Rainero, I, Rajabli, F, Ramakers, I, Real, L, Reinders, M, Reitz, C, Reyes-Dumeyer, D, Ridge, P, Riedel-Heller, S, Riederer, P, Roberto, N, Rodriguez-Rodriguez, E, Rongve, A, Allende, I, Rosende-Roca, M, Royo, J, Rubino, E, Rujescu, D, Sáez, M, Sakka, P, Saltvedt, I, Sanabria, Á, Sánchez-Arjona, M, Sanchez-Garcia, F, Juan, P, Sánchez-Valle, R, Sando, S, Sarnowski, C, Satizabal, C, Scamosci, M, Scarmeas, N, Scarpini, E, Scheltens, P, Scherbaum, N, Scherer, M, Schmid, M, Schneider, A, Schott, J, Selbæk, G, Seripa, D, Serrano, M, Sha, J, Shadrin, A, Skrobot, O, Slifer, S, Snijders, G, Soininen, H, Solfrizzi, V, Solomon, A, Song, Y, Sorbi, S, Sotolongo-Grau, O, Spalletta, G, Spottke, A, Squassina, A, Stordal, E, Tartan, J, Tárraga, L, Tesí, N, Thalamuthu, A, Thomas, T, Tosto, G, Traykov, L, Tremolizzo, L, Tybjærg-Hansen, A, 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Sachdev, Perminder [0000-0002-9595-3220], Mather, Karen [0000-0003-4143-8941], Ikram, M Arfan [0000-0003-0372-8585], Tsolaki, Magda [0000-0002-2072-8010], Pericak-Vance, Margaret A [0000-0001-7283-8804], Amouyel, Philippe [0000-0001-9088-234X], Williams, Julie [0000-0002-4069-0259], Frikke-Schmidt, Ruth [0000-0003-4084-5027], Seshadri, Sudha [0000-0001-6135-2622], Andreassen, Ole A [0000-0002-4461-3568], Sleegers, Kristel [0000-0002-0283-2332], van Duijn, Cornelia M [0000-0002-2374-9204], Sims, Rebecca [0000-0002-3885-1199], van der Flier, Wiesje M [0000-0001-8766-6224], Ramirez, Alfredo [0000-0003-4991-763X], Lambert, Jean-Charles [0000-0003-0829-7817], Apollo - University of Cambridge Repository, Complex Trait Genetics, Clinical sciences, Neuroprotection & Neuromodulation, Pathologic Biochemistry and Physiology, Clinical Biology, Epidemiology, Internal Medicine, Psychiatrie & Neuropsychologie, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, MUMC+: MA Med Staf Spec Psychiatrie (9), UAM. Departamento de Biología Molecular, University of Helsinki, Department of Neurosciences, HUS Internal Medicine and Rehabilitation, Timo Strandberg / Principal Investigator, Department of Medicine, Clinicum, HUS Neurocenter, Neurologian yksikkö, Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institut Pasteur, Institut National de la Santé et de la Recherche Médicale (France), European Commission, LabEx DISTALZ, Pérez-Tur, Jordi, University Children’s Hospital Basel (Suiza), INSERM (Francia), Lille Métropole Communauté Urbaine, Government of France (Francia), EADB, GR@ACE, DEGESCO, EADI, GERAD, Demgene, FinnGen, ADGC, CHARGE, Holmans, Peter A. [0000-0003-0870-9412], van der Lee, Sven J. [0000-0003-1606-8643], Costa, Marcos R. [0000-0002-4928-2163], Bis, Joshua C. [0000-0002-3409-1110], Brookes, Keeley J. [0000-0003-2427-2513], Bush, William S. [0000-0002-9729-6519], de Witte, Lot D. [0000-0002-7235-9958], del Ser, Teodoro [0000-0001-9806-7083], Fox, Nick C. [0000-0002-6660-657X], Bullido, María J. [0000-0002-6477-1117], Goate, Alison M. [0000-0002-0576-2472], Herrmann, Martin J. [0000-0001-9970-2122], Jun, Gyungah R. [0000-0002-3230-8697], Kehoe, Patrick G. [0000-0002-7542-1139], Kosmidis, Mary H. [0000-0001-8790-1220], Lunetta, Kathryn L. [0000-0002-9268-810X], MacLeod, Catherine A. [0000-0002-9314-7380], Nöthen, Markus M. [0000-0002-8770-2464], Nordestgaard, Børge G. [0000-0002-1954-7220], Pineda, Juan A. [0000-0002-3751-0296], Real, Luis M. [0000-0003-4932-7429], Reinders, Marcel J. T. [0000-0002-1148-1562], Satizabal, Claudia L. [0000-0002-1115-4430], Schott, Jonathan M. [0000-0003-2059-024X], Shadrin, Alexey A. [0000-0002-7467-250X], Farrer, Lindsay A. [0000-0001-5533-4225], Psaty, Bruce M. [0000-0002-7278-2190], Ikram, M. Arfan [0000-0003-0372-8585], Pericak-Vance, Margaret A. [0000-0001-7283-8804], Andreassen, Ole A. [0000-0002-4461-3568], van Duijn, Cornelia M. [0000-0002-2374-9204], van der Flier, Wiesje M. [0000-0001-8766-6224], and Molecular Neuroscience and Ageing Research (MOLAR)
- Subjects
tau Proteins/genetics ,Alzheimer`s disease Donders Center for Medical Neuroscience [Radboudumc 1] ,Neurologi ,MED/03 - GENETICA MEDICA ,45/43 ,Medizin ,Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13] ,genetics [Alzheimer Disease] ,Genome-Wide Association Study ,Humans ,tau Proteins ,Alzheimer Disease ,Cognitive Dysfunction ,VARIANTS ,pathology [Alzheimer Disease] ,Tau Proteins ,Settore BIO/13 - Biologia Applicata ,Cognitive Dysfunction/psychology ,692/699/375/365/1283 ,IMPUTATION ,article ,1184 Genetics, developmental biology, physiology ,Biología y Biomedicina / Biología ,AMYLOID-BETA ,Settore MED/26 - NEUROLOGIA ,Neurology ,psychology [Cognitive Dysfunction] ,Medical Genetics ,Human ,Neuroscience(all) ,631/208/205/2138 ,All institutes and research themes of the Radboud University Medical Center ,SDG 3 - Good Health and Well-being ,ddc:570 ,Genetics ,Genetic Predisposition to Disease ,GENOME-WIDE ASSOCIATION ,METAANALYSIS ,Medicinsk genetik ,MED/26 - NEUROLOGIA ,Alzheimer Disease/genetics ,neurology ,tau Protein ,NECROSIS-FACTOR-ALPHA ,RISK LOCI ,genetics [tau Proteins] ,PREDICTION MODELS ,Human medicine ,GENERATION ,RESPONSES - Abstract
25 páginas, 6 figuras, 2 tablas, Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele., This work was funded by a grant (EADB) from the EU Joint Programme – Neurodegenerative Disease Research. INSERM UMR1167 is also funded by the INSERM, Institut Pasteur de Lille, Lille Métropole Communauté Urbaine and French government’s LABEX DISTALZ program (development of innovative strategies for a transdisciplinary approach to AD). Full consortium acknowledgements and funding are in the Supplementary Not
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- 2022
16. Sample Preparation for Rapid Lipid Analysis in Drosophila Brain using Matrix-assisted Laser Desorption/Ionization Mass Spectrometry Imaging
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Yuki X, Chen, Kelly, Veerasammy, Jun, Yin, Tenzin, Choetso, Tiffany, Zhong, Muniyat A, Choudhury, Cory, Weng, Ethan, Xu, Mayan A, Hein, Rinat, Abzalimov, and Ye, He
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Lasers ,Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ,Animals ,Brain ,Drosophila ,Lipids - Abstract
Lipid profiling, or lipidomics, is a well-established technique used to study the entire lipid content of a cell or tissue. Information acquired from lipidomics is valuable in studying the pathways involved in development, disease, and cellular metabolism. Many tools and instrumentations have aided lipidomics projects, most notably various combinations of mass spectrometry and liquid chromatography techniques. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) has recently emerged as a powerful imaging technique that complements conventional approaches. This novel technique provides unique information on the spatial distribution of lipids within tissue compartments, which was previously unattainable without the use of excessive modifications. The sample preparation of the MALDI MSI approach is critical and, therefore, is the focus of this paper. This paper presents a rapid lipid analysis of a large number of Drosophila brains embedded in optimal cutting temperature compound (OCT) to provide a detailed protocol for the preparation of small tissues for lipid analysis or metabolite and small molecule analysis through MALDI MSI.
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- 2022
17. Sample Preparation for Rapid Lipid Analysis in Drosophila Brain Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging
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Ye He, Rinat Abzalimov, Mayan A. Hein, Ethan Xu, Cory Weng, Muniyat A. Choudhury, Tiffany Zhong, Tenzin Choetso, Jun Yin, Kelly Veerasammy, and Yuki X. Chen
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General Immunology and Microbiology ,General Chemical Engineering ,General Neuroscience ,General Biochemistry, Genetics and Molecular Biology - Published
- 2022
18. AutoKnow
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Yaqing Wang, Yan Liang, Surender Pal Singh, Yifan Ethan Xu, Fan Xiao, Andrew McCallum, Xian Li, Jiawei Han, Xiang He, Jun Ma, Chenwei Zhang, Alexandre Michetti Manduca, Xin Luna Dong, Yuning Mao, Saurabh Deshpande, Andrey Kan, Giannis Karamanolakis, Jay Ren, Gabriel Blanco Saldana, Tong Zhao, Christos Faloutsos, and Haw-Shiuan Chang
- Subjects
FOS: Computer and information sciences ,Computer Science - Artificial Intelligence ,Computer science ,02 engineering and technology ,Product type ,Data science ,Identification (information) ,Artificial Intelligence (cs.AI) ,Knowledge graph ,Knowledge extraction ,020204 information systems ,Taxonomy (general) ,Synonym (database) ,0202 electrical engineering, electronic engineering, information engineering ,Question answering ,020201 artificial intelligence & image processing ,Product (category theory) - Abstract
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types., KDD 2020
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- 2020
19. Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data
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Yifan Ethan Xu, Jing Gao, Yaqing Wang, Xian Li, and Xin Luna Dong
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Information retrieval ,Correctness ,Computer Science - Computation and Language ,Meta learning (computer science) ,Computer science ,business.industry ,media_common.quotation_subject ,02 engineering and technology ,Latent variable ,E-commerce ,Machine Learning (cs.LG) ,Consistency (database systems) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Quality (business) ,Latent variable model ,business ,Computation and Language (cs.CL) ,Information integration ,media_common - Abstract
Product catalogs are valuable resources for eCommerce website. In the catalog, a product is associated with multiple attributes whose values are short texts, such as product name, brand, functionality and flavor. Usually individual retailers self-report these key values, and thus the catalog information unavoidably contains noisy facts. Although existing deep neural network models have shown success in conducting cross-checking between two pieces of texts, their success has to be dependent upon a large set of quality labeled data, which are hard to obtain in this validation task: products span a variety of categories. To address the aforementioned challenges, we propose a novel meta-learning latent variable approach, called MetaBridge, which can learn transferable knowledge from a subset of categories with limited labeled data and capture the uncertainty of never-seen categories with unlabeled data. More specifically, we make the following contributions. (1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values. (2) We propose to integrate meta learning and latent variable in a unified model to effectively capture the uncertainty of various categories. (3) We propose a novel objective function based on latent variable model in the few-shot learning setting, which ensures distribution consistency between unlabeled and labeled data and prevents overfitting by sampling from the learned distribution. Extensive experiments on real eCommerce datasets from hundreds of categories demonstrate the effectiveness of MetaBridge on textual attribute validation and its outstanding performance compared with state-of-the-art approaches., Comment: KDD 2020
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- 2020
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20. Efficient Knowledge Graph Accuracy Evaluation
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Xin Luna Dong, Yifan Ethan Xu, Junyang Gao, Bunyamin Sisman, Jun Yang, and Xian Li
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FOS: Computer and information sciences ,Computer science ,business.industry ,media_common.quotation_subject ,General Engineering ,Sampling (statistics) ,Databases (cs.DB) ,02 engineering and technology ,Function (mathematics) ,Machine learning ,computer.software_genre ,Stratified sampling ,Cost reduction ,Computer Science - Databases ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Cluster sampling ,Quality (business) ,Artificial intelligence ,Reservoir sampling ,business ,computer ,media_common - Abstract
Estimation of the accuracy of a large-scale knowledge graph (KG) often requires humans to annotate samples from the graph. How to obtain statistically meaningful estimates for accuracy evaluation while keeping human annotation costs low is a problem critical to the development cycle of a KG and its practical applications. Surprisingly, this challenging problem has largely been ignored in prior research. To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts. Motivated by the properties of the annotation cost function observed in practice, we propose the use of cluster sampling to reduce the overall cost. We further apply weighted and two-stage sampling as well as stratification for better sampling designs. We also extend our framework to enable efficient incremental evaluation on evolving KG, introducing two solutions based on stratified sampling and a weighted variant of reservoir sampling. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed solution. Compared to baseline approaches, our best solutions can provide up to 60% cost reduction on static KG evaluation and up to 80% cost reduction on evolving KG evaluation, without loss of evaluation quality., Comment: in VLDB 2019
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- 2019
- Full Text
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