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692 results on '"David, Eyal"'

1. CoverBench: A Challenging Benchmark for Complex Claim Verification

Author

Jacovi, Alon, Ambar, Moran, Ben-David, Eyal, Shaham, Uri, Feder, Amir, Geva, Mor, Marcus, Dror, and Caciularu, Avi

Subjects
Computer Science - Computation and Language
Abstract

There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning. We introduce CoverBench, a challenging benchmark focused on verifying LM outputs in complex reasoning settings. Datasets that can be used for this purpose are often designed for other complex reasoning tasks (e.g., QA) targeting specific use-cases (e.g., financial tables), requiring transformations, negative sampling and selection of hard examples to collect such a benchmark. CoverBench provides a diversified evaluation for complex claim verification in a variety of domains, types of reasoning, relatively long inputs, and a variety of standardizations, such as multiple representations for tables where available, and a consistent schema. We manually vet the data for quality to ensure low levels of label noise. Finally, we report a variety of competitive baseline results to show CoverBench is challenging and has very significant headroom. The data is available at https://huggingface.co/datasets/google/coverbench .

Published
2024

2. TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools

Author

Caciularu, Avi, Jacovi, Alon, Ben-David, Eyal, Goldshtein, Sasha, Schuster, Tal, Herzig, Jonathan, Elidan, Gal, and Globerson, Amir

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling efforts, we introduce TACT - Text And Calculations through Tables, a dataset crafted to evaluate LLMs' reasoning and computational abilities using complex instructions. TACT contains challenging instructions that demand stitching information scattered across one or more texts, and performing complex integration on this information to generate the answer. We construct this dataset by leveraging an existing dataset of texts and their associated tables. For each such tables, we formulate new queries, and gather their respective answers. We demonstrate that all contemporary LLMs perform poorly on this dataset, achieving an accuracy below 38%. To pinpoint the difficulties and thoroughly dissect the problem, we analyze model performance across three components: table-generation, Pandas command-generation, and execution. Unexpectedly, we discover that each component presents substantial challenges for current LLMs. These insights lead us to propose a focused modeling framework, which we refer to as IE as a tool. Specifically, we propose to add "tools" for each of the above steps, and implement each such tool with few-shot prompting. This approach shows an improvement over existing prompting techniques, offering a promising direction for enhancing model capabilities in these tasks., Comment: Accepted to NeurIPS 2024. Website (https://tact-benchmark.github.io), Huggingface (https://huggingface.co/datasets/google/TACT)

Published
2024

3. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

4. Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models

Author

Ventura, Mor, Ben-David, Eyal, Korhonen, Anna, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Text-To-Image (TTI) models, such as DALL-E and StableDiffusion, have demonstrated remarkable prompt-based image generation capabilities. Multilingual encoders may have a substantial impact on the cultural agency of these models, as language is a conduit of culture. In this study, we explore the cultural perception embedded in TTI models by characterizing culture across three hierarchical tiers: cultural dimensions, cultural domains, and cultural concepts. Based on this ontology, we derive prompt templates to unlock the cultural knowledge in TTI models, and propose a comprehensive suite of evaluation techniques, including intrinsic evaluations using the CLIP space, extrinsic evaluations with a Visual-Question-Answer (VQA) model and human assessments, to evaluate the cultural content of TTI-generated images. To bolster our research, we introduce the CulText2I dataset, derived from six diverse TTI models and spanning ten languages. Our experiments provide insights regarding Do, What, Which and How research questions about the nature of cultural encoding in TTI models, paving the way for cross-cultural applications of these models., Comment: Project page: https://venturamor.github.io/CulText2IWeb/

Published
2023

5. Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols

Author

Barboy, Oren, Bercovich, Akhiad, Li, Hanjie, Eyal-Lubling, Yaniv, Yalin, Adam, Shapir Itai, Yuval, Abadie, Kathleen, Zada, Mor, David, Eyal, Shlomi-Loubaton, Shir, Katzenelenbogen, Yonatan, Jaitin, Diego Adhemar, Gur, Chamutal, Yofe, Ido, Feferman, Tali, Cohen, Merav, Dahan, Rony, Newell, Evan W., Lifshitz, Aviezer, Tanay, Amos, and Amit, Ido

Published
2024
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7. Measuring the Robustness of NLP Models to Domain Shifts

Author

Calderon, Nitay, Porat, Naveh, Ben-David, Eyal, Chapanin, Alexander, Gekhman, Zorik, Oved, Nadav, Shalumov, Vitaly, and Reichart, Roi

Subjects
Computer Science - Computation and Language
Abstract

Existing research on Domain Robustness (DR) suffers from disparate setups, limited task variety, and scarce research on recent capabilities such as in-context learning. Furthermore, the common practice of measuring DR might not be fully accurate. Current research focuses on challenge sets and relies solely on the Source Drop (SD): Using the source in-domain performance as a reference point for degradation. However, we argue that the Target Drop (TD), which measures degradation from the target in-domain performance, should be used as a complementary point of view. To address these issues, we first curated a DR benchmark comprised of 7 diverse NLP tasks, which enabled us to measure both the SD and the TD. We then conducted a comprehensive large-scale DR study involving over 14,000 domain shifts across 21 fine-tuned models and few-shot LLMs. We found that both model types suffer from drops upon domain shifts. While fine-tuned models excel in-domain, few-shot LLMs often surpass them cross-domain, showing better robustness. In addition, we found that a large SD can often be explained by shifting to a harder domain rather than by a genuine DR challenge, and this highlights the importance of TD as a complementary metric. We hope our study will shed light on the current DR state of NLP models and promote improved evaluation practices toward more robust models.

Published
2023

8. Pumping iron: A multi-omics analysis of two extremophilic algae reveals iron economy management

Author

Davidi, Lital, Gallaher, Sean D, Ben-David, Eyal, Purvine, Samuel O, Fillmore, Thomas L, Nicora, Carrie D, Craig, Rory J, Schmollinger, Stefan, Roje, Sanja, Blaby-Haas, Crysten E, Auber, Robert P, Wisecaver, Jennifer H, and Merchant, Sabeeha S

Subjects
Plant Biology, Biological Sciences, Life Below Water, Iron, Extremophiles, Multiomics, Proteomics, Photosynthesis, Proteins, Chlorophyceae, iron homeostasis, phytoplankton, Chlamydomonas reinhardtii, iron starvation-induced protein, flavin biosynthesis, iron starvation–induced protein
Abstract

Marine algae are responsible for half of the world's primary productivity, but this critical carbon sink is often constrained by insufficient iron. One species of marine algae, Dunaliella tertiolecta, is remarkable for its ability to maintain photosynthesis and thrive in low-iron environments. A related species, Dunaliella salina Bardawil, shares this attribute but is an extremophile found in hypersaline environments. To elucidate how algae manage their iron requirements, we produced high-quality genome assemblies and transcriptomes for both species to serve as a foundation for a comparative multiomics analysis. We identified a host of iron-uptake proteins in both species, including a massive expansion of transferrins and a unique family of siderophore-iron-uptake proteins. Complementing these multiple iron-uptake routes, ferredoxin functions as a large iron reservoir that can be released by induction of flavodoxin. Proteomic analysis revealed reduced investment in the photosynthetic apparatus coupled with remodeling of antenna proteins by dramatic iron-deficiency induction of TIDI1, which is closely related but identifiably distinct from the chlorophyll binding protein, LHCA3. These combinatorial iron scavenging and sparing strategies make Dunaliella unique among photosynthetic organisms.

Published
2023

9. Text2Model: Text-based Model Induction for Zero-shot Image Classification

Author

Amosy, Ohad, Volk, Tomer, Shapira, Eilam, Ben-David, Eyal, Reichart, Roi, and Chechik, Gal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We address the challenge of building task-agnostic classifiers using only text descriptions, demonstrating a unified approach to image classification, 3D point cloud classification, and action recognition from scenes. Unlike approaches that learn a fixed representation of the output classes, we generate at inference time a model tailored to a query classification task. To generate task-based zero-shot classifiers, we train a hypernetwork that receives class descriptions and outputs a multi-class model. The hypernetwork is designed to be equivariant with respect to the set of descriptions and the classification layer, thus obeying the symmetries of the problem and improving generalization. Our approach generates non-linear classifiers, handles rich textual descriptions, and may be adapted to produce lightweight models efficient enough for on-device applications. We evaluate this approach in a series of zero-shot classification tasks, for image, point-cloud, and action recognition, using a range of text descriptions: From single words to rich descriptions. Our results demonstrate strong improvements over previous approaches, showing that zero-shot learning can be applied with little training data. Furthermore, we conduct an analysis with foundational vision and language models, demonstrating that they struggle to generalize when describing what attributes the class lacks.

Published
2022

10. Domain Adaptation from Scratch

Author

Ben-David, Eyal, Ziser, Yftah, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Natural language processing (NLP) algorithms are rapidly improving but often struggle when applied to out-of-distribution examples. A prominent approach to mitigate the domain gap is domain adaptation, where a model trained on a source domain is adapted to a new target domain. We present a new learning setup, ``domain adaptation from scratch'', which we believe to be crucial for extending the reach of NLP to sensitive domains in a privacy-preserving manner. In this setup, we aim to efficiently annotate data from a set of source domains such that the trained model performs well on a sensitive target domain from which data is unavailable for annotation. Our study compares several approaches for this challenging setup, ranging from data selection and domain adaptation algorithms to active learning paradigms, on two NLP tasks: sentiment analysis and Named Entity Recognition. Our results suggest that using the abovementioned approaches eases the domain gap, and combining them further improves the results.

Published
2022

11. IDANI: Inference-time Domain Adaptation via Neuron-level Interventions

Author

Antverg, Omer, Ben-David, Eyal, and Belinkov, Yonatan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Large pre-trained models are usually fine-tuned on downstream task data, and tested on unseen data. When the train and test data come from different domains, the model is likely to struggle, as it is not adapted to the test domain. We propose a new approach for domain adaptation (DA), using neuron-level interventions: We modify the representation of each test example in specific neurons, resulting in a counterfactual example from the source domain, which the model is more familiar with. The modified example is then fed back into the model. While most other DA methods are applied during training time, ours is applied during inference only, making it more efficient and applicable. Our experiments show that our method improves performance on unseen domains., Comment: Our code is available at https://github.com/technion-cs-nlp/idani

Published
2022

12. Example-based Hypernetworks for Out-of-Distribution Generalization

Author

Volk, Tomer, Ben-David, Eyal, Amosy, Ohad, Chechik, Gal, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

As Natural Language Processing (NLP) algorithms continually achieve new milestones, out-of-distribution generalization remains a significant challenge. This paper addresses the issue of multi-source adaptation for unfamiliar domains: We leverage labeled data from multiple source domains to generalize to unknown target domains at training. Our innovative framework employs example-based Hypernetwork adaptation: a T5 encoder-decoder initially generates a unique signature from an input example, embedding it within the source domains' semantic space. This signature is subsequently utilized by a Hypernetwork to generate the task classifier's weights. We evaluated our method across two tasks - sentiment classification and natural language inference - in 29 adaptation scenarios, where it outpaced established algorithms. In an advanced version, the signature also enriches the input example's representation. We also compare our finetuned architecture to few-shot GPT-3, demonstrating its effectiveness in essential use cases. To our knowledge, this marks the first application of Hypernetworks to the adaptation for unknown domains., Comment: First two authors contributed equally to this work. Our code and data are available at: https://github.com/TomerVolk/Hyper-PADA

Published
2022

13. DestVI identifies continuums of cell types in spatial transcriptomics data

Author

Lopez, Romain, Li, Baoguo, Keren-Shaul, Hadas, Boyeau, Pierre, Kedmi, Merav, Pilzer, David, Jelinski, Adam, Yofe, Ido, David, Eyal, Wagner, Allon, Ergen, Can, Addadi, Yoseph, Golani, Ofra, Ronchese, Franca, Jordan, Michael I, Amit, Ido, and Yosef, Nir

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Bioengineering, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Animals, Gene Expression Profiling, Mice, Neoplasms, Single-Cell Analysis, Software, Transcriptome, Exome Sequencing
Abstract

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).

Published
2022

14. DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation

Author

Calderon, Nitay, Ben-David, Eyal, Feder, Amir, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7
Abstract

Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge. Given an input text example, our DoCoGen algorithm generates a domain-counterfactual textual example (D-con) - that is similar to the original in all aspects, including the task label, but its domain is changed to a desired one. Importantly, DoCoGen is trained using only unlabeled examples from multiple domains - no NLP task labels or parallel pairs of textual examples and their domain-counterfactuals are required. We show that DoCoGen can generate coherent counterfactuals consisting of multiple sentences. We use the D-cons generated by DoCoGen to augment a sentiment classifier and a multi-label intent classifier in 20 and 78 DA setups, respectively, where source-domain labeled data is scarce. Our model outperforms strong baselines and improves the accuracy of a state-of-the-art unsupervised DA algorithm., Comment: Our code and data are available at https://github.com/nitaytech/DoCoGen

Published
2022

15. Island-specific evolution of a sex-primed autosome in a sexual planarian

Author

Guo, Longhua, Bloom, Joshua S, Dols-Serrate, Daniel, Boocock, James, Ben-David, Eyal, Schubert, Olga T, Kozuma, Kaiya, Ho, Katarina, Warda, Emily, Chui, Clarice, Wei, Yubao, Leighton, Daniel, Lemus Vergara, Tzitziki, Riutort, Marta, Sánchez Alvarado, Alejandro, and Kruglyak, Leonid

Subjects
Biological Sciences, Genetics, Contraception/Reproduction, Human Genome, Biotechnology, 1.1 Normal biological development and functioning, Underpinning research, Reproductive health and childbirth, Animals, Chromosome Mapping, Evolution, Molecular, Female, Genome, Inbreeding, Islands, Male, Planarians, Reproduction, Sex Chromosomes, General Science & Technology
Abstract

The sexual strain of the planarian Schmidtea mediterranea, indigenous to Tunisia and several Mediterranean islands, is a hermaphrodite1,2. Here we isolate individual chromosomes and use sequencing, Hi-C3,4 and linkage mapping to assemble a chromosome-scale genome reference. The linkage map reveals an extremely low rate of recombination on chromosome 1. We confirm suppression of recombination on chromosome 1 by genotyping individual sperm cells and oocytes. We show that previously identified genomic regions that maintain heterozygosity even after prolonged inbreeding make up essentially all of chromosome 1. Genome sequencing of individuals isolated in the wild indicates that this phenomenon has evolved specifically in populations from Sardinia and Corsica. We find that most known master regulators5-13 of the reproductive system are located on chromosome 1. We used RNA interference14,15 to knock down a gene with haplotype-biased expression, which led to the formation of a more pronounced female mating organ. On the basis of these observations, we propose that chromosome 1 is a sex-primed autosome primed for evolution into a sex chromosome.

Published
2022

16. Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead

Author

Ben-David, Eyal, Carmeli, Boaz, and Anaby-Tavor, Ateret

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Goal oriented dialogue systems have become a prominent customer-care interaction channel for most businesses. However, not all interactions are smooth, and customer intent misunderstanding is a major cause of dialogue failure. We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data. For that, we define a multi-task training regime that utilizes successive user-utterance generation to improve the intent prediction. Our approach achieves the reported improvement due to two complementary factors: First, it uses a large amount of unlabeled dialogue data for an auxiliary generation task. Second, it uses the generated user utterance as an additional signal for the intent prediction model. Lastly, we present a novel look-ahead approach that uses user utterance generation to improve intent prediction in inference time. Specifically, we generate counterfactual successive user utterances for conversations with ambiguous predicted intents, and disambiguate the prediction by reassessing the concatenated sequence of available and generated utterances.

Published
2021

17. The CD8+ T cell tolerance checkpoint triggers a distinct differentiation state defined by protein translation defects

Author

Van Der Byl, Willem, Nüssing, Simone, Peters, Timothy J., Ahn, Antonio, Li, Hanjie, Ledergor, Guy, David, Eyal, Koh, Andrew S., Wagle, Mayura V., Deguit, Christian Deo T., de Menezes, Maria N., Travers, Avraham, Sampurno, Shienny, Ramsbottom, Kelly M., Li, Rui, Kallies, Axel, Beavis, Paul A., Jungmann, Ralf, Bastings, Maartje M.C., Belz, Gabrielle T., Goel, Shom, Trapani, Joseph A., Crabtree, Gerald R., Chang, Howard Y., Amit, Ido, Goodnow, Chris C., Luciani, Fabio, and Parish, Ian A.

Published
2024
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18. PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Author

Ben-David, Eyal, Oved, Nadav, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Natural Language Processing algorithms have made incredible progress, but they still struggle when applied to out-of-distribution examples. We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time. Particularly, no examples, labeled or unlabeled, or any other knowledge about the target domain are available to the algorithm at training time. We present PADA: An example-based autoregressive Prompt learning algorithm for on-the-fly Any-Domain Adaptation, based on the T5 language model. Given a test example, PADA first generates a unique prompt for it and then, conditioned on this prompt, labels the example with respect to the NLP prediction task. PADA is trained to generate a prompt which is a token sequence of unrestricted length, consisting of Domain Related Features (DRFs) that characterize each of the source domains. Intuitively, the generated prompt is a unique signature that maps the test example to a semantic space spanned by the source domains. In experiments with 3 tasks (text classification and sequence tagging), for a total of 14 multi-source adaptation scenarios, PADA substantially outperforms strong baselines., Comment: Accepted for publication at TACL in January 2022. First two authors contributed equally to this work. Our code and data are available at: https://github.com/eyalbd2/PADA

Published
2021

19. Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma

Author

Kirschenbaum, Daniel, Xie, Ken, Ingelfinger, Florian, Katzenelenbogen, Yonatan, Abadie, Kathleen, Look, Thomas, Sheban, Fadi, Phan, Truong San, Li, Baoguo, Zwicky, Pascale, Yofe, Ido, David, Eyal, Mazuz, Kfir, Hou, Jinchao, Chen, Yun, Shaim, Hila, Shanley, Mayra, Becker, Soeren, Qian, Jiawen, Colonna, Marco, Ginhoux, Florent, Rezvani, Katayoun, Theis, Fabian J., Yosef, Nir, Weiss, Tobias, Weiner, Assaf, and Amit, Ido

Published
2024
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20. The transcriptional and regulatory identity of erythropoietin producing cells

Author

Kragesteen, Bjørt K., Giladi, Amir, David, Eyal, Halevi, Shahar, Geirsdóttir, Laufey, Lempke, Olga M., Li, Baoguo, Bapst, Andreas M., Xie, Ken, Katzenelenbogen, Yonatan, Dahl, Sophie L., Sheban, Fadi, Gurevich-Shapiro, Anna, Zada, Mor, Phan, Truong San, Avellino, Roberto, Wang, Shuang-Yin, Barboy, Oren, Shlomi-Loubaton, Shir, Winning, Sandra, Markwerth, Philipp P., Dekalo, Snir, Keren-Shaul, Hadas, Kedmi, Merav, Sikora, Martin, Fandrey, Joachim, Korneliussen, Thorfinn S., Prchal, Josef T., Rosenzweig, Barak, Yutkin, Vladimir, Racimo, Fernando, Willerslev, Eske, Gur, Chamutal, Wenger, Roland H., and Amit, Ido

Published
2023
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21. Semantically Driven Sentence Fusion: Modeling and Evaluation

Author

Ben-David, Eyal, Keller, Orgad, Malmi, Eric, Szpektor, Idan, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models., Comment: This paper was accepted to Findings of EMNLP 2020

Published
2020

22. PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models

Author

Ben-David, Eyal, Rabinovitz, Carmel, and Reichart, Roi

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Pivot-based neural representation models have lead to significant progress in domain adaptation for NLP. However, previous works that follow this approach utilize only labeled data from the source domain and unlabeled data from the source and target domains, but neglect to incorporate massive unlabeled corpora that are not necessarily drawn from these domains. To alleviate this, we propose PERL: A representation learning model that extends contextualized word embedding models such as BERT with pivot-based fine-tuning. PERL outperforms strong baselines across 22 sentiment classification domain adaptation setups, improves in-domain model performance, yields effective reduced-size models and increases model stability., Comment: Accepted to TACL in June 2020

Published
2020

24. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

Author

Wu, Yichen, Rivenson, Yair, Wang, Hongda, Luo, Yilin, Ben-David, Eyal, Bentolila, Laurent A., Pritz, Christian, and Ozcan, Aydogan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Applied Physics, Physics - Optics
Abstract

Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes. Here we demonstrate that a deep convolutional neural network can be trained to virtually refocus a 2D fluorescence image onto user-defined 3D surfaces within the sample volume. With this data-driven computational microscopy framework, we imaged the neuron activity of a Caenorhabditis elegans worm in 3D using a time-sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field of the microscope by 20-fold without any axial scanning, additional hardware, or a trade-off of imaging resolution or speed. Furthermore, we demonstrate that this learning-based approach can correct for sample drift, tilt, and other image aberrations, all digitally performed after the acquisition of a single fluorescence image. This unique framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. This deep learning-based 3D image refocusing method might be transformative for imaging and tracking of 3D biological samples, especially over extended periods of time, mitigating photo-toxicity, sample drift, aberration and defocusing related challenges associated with standard 3D fluorescence microscopy techniques., Comment: 47 pages, 5 figures (main text)

Published
2019
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27. Deletion of a Csf1r enhancer selectively impacts CSF1R expression and development of tissue macrophage populations.

Author

Rojo, Rocío, Raper, Anna, Ozdemir, Derya D, Lefevre, Lucas, Grabert, Kathleen, Wollscheid-Lengeling, Evi, Bradford, Barry, Caruso, Melanie, Gazova, Iveta, Sánchez, Alejandra, Lisowski, Zofia M, Alves, Joana, Molina-Gonzalez, Irene, Davtyan, Hayk, Lodge, Rebecca J, Glover, James D, Wallace, Robert, Munro, David AD, David, Eyal, Amit, Ido, Miron, Véronique E, Priller, Josef, Jenkins, Stephen J, Hardingham, Giles E, Blurton-Jones, Mathew, Mabbott, Neil A, Summers, Kim M, Hohenstein, Peter, Hume, David A, and Pridans, Clare

Subjects
Microglia, Monocytes, Macrophages, Animals, Mice, Inbred C57BL, Mice, Knockout, Mice, Disease Models, Animal, Epidermal Growth Factor, Macrophage Colony-Stimulating Factor, Receptors, Granulocyte-Macrophage Colony-Stimulating Factor, Cell Differentiation, Cell Proliferation, Phagocytosis, Gene Expression Regulation, Sequence Deletion, Base Sequence, Regulatory Sequences, Nucleic Acid, Genes, fms, Female, Male, Embryonic Stem Cells, RAW 264.7 Cells, Inbred C57BL, Knockout, Disease Models, Animal, Receptors, Granulocyte-Macrophage Colony-Stimulating Factor, Regulatory Sequences, Nucleic Acid, Genes, fms, Stem Cell Research, Stem Cell Research - Nonembryonic - Non-Human, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, MD Multidisciplinary
Abstract

The proliferation, differentiation and survival of mononuclear phagocytes depend on signals from the receptor for macrophage colony-stimulating factor, CSF1R. The mammalian Csf1r locus contains a highly conserved super-enhancer, the fms-intronic regulatory element (FIRE). Here we show that genomic deletion of FIRE in mice selectively impacts CSF1R expression and tissue macrophage development in specific tissues. Deletion of FIRE ablates macrophage development from murine embryonic stem cells. Csf1rΔFIRE/ΔFIRE mice lack macrophages in the embryo, brain microglia and resident macrophages in the skin, kidney, heart and peritoneum. The homeostasis of other macrophage populations and monocytes is unaffected, but monocytes and their progenitors in bone marrow lack surface CSF1R. Finally, Csf1rΔFIRE/ΔFIRE mice are healthy and fertile without the growth, neurological or developmental abnormalities reported in Csf1r-/- rodents. Csf1rΔFIRE/ΔFIRE mice thus provide a model to explore the homeostatic, physiological and immunological functions of tissue-specific macrophage populations in adult animals.

Published
2019

28. Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk.

Author

Burga, Alejandro, Ben-David, Eyal, Lemus Vergara, Tzitziki, Boocock, James, and Kruglyak, Leonid

Subjects
Animals, Caenorhabditis elegans, Chromosome Mapping, Gene Expression Regulation, Drug Resistance, Quantitative Trait, Heritable, Quantitative Trait Loci, Time Factors, Female, Male, Genetic Fitness, Quantitative Trait, Heritable
Abstract

Genetic studies of complex traits in animals have been hindered by the need to generate, maintain, and phenotype large panels of recombinant lines. We developed a new method, C. elegans eXtreme Quantitative Trait Locus (ceX-QTL) mapping, that overcomes this obstacle via bulk selection on millions of unique recombinant individuals. We use ceX-QTL to map a drug resistance locus with high resolution. We also map differences in gene expression in live worms and discovered that mutations in the co-chaperone sti-1 upregulate the transcription of HSP-90. Lastly, we use ceX-QTL to map loci that influence fitness genome-wide confirming previously reported causal variants and uncovering new fitness loci. ceX-QTL is fast, powerful and cost-effective, and will accelerate the study of complex traits in animals.

Published
2019

33. XCR1.sup.+ type 1 conventional dendritic cells drive liver pathology in non-alcoholic steatohepatitis

Author

Deczkowska, Aleksandra, David, Eyal, Ramadori, Pierluigi, Pfister, Dominik, Safran, Michael, At the, Baoguo, and Giladi, Amir

Subjects
Fatty liver -- Causes of -- Care and treatment -- Development and progression, Dendritic cells -- Health aspects, Biological sciences, Health
Abstract

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are prevalent liver conditions that underlie the development of life-threatening cirrhosis, liver failure and liver cancer. Chronic necro-inflammation is a critical factor in development of NASH, yet the cellular and molecular mechanisms of immune dysregulation in this disease are poorly understood. Here, using single-cell transcriptomic analysis, we comprehensively profiled the immune composition of the mouse liver during NASH. We identified a significant pathology-associated increase in hepatic conventional dendritic cells (cDCs) and further defined their source as NASH-induced boost in cycling of cDC progenitors in the bone marrow. Analysis of blood and liver from patients on the NAFLD/NASH spectrum showed that type 1 cDCs (cDC1) were more abundant and activated in disease. Sequencing of physically interacting cDC-T cell pairs from liver-draining lymph nodes revealed that cDCs in NASH promote inflammatory T cell reprogramming, previously associated with NASH worsening. Finally, depletion of cDC1 in XCR1.sup.DTA mice or using anti-XCL1-blocking antibody attenuated liver pathology in NASH mouse models. Overall, our study provides a comprehensive characterization of cDC biology in NASH and identifies XCR1.sup.+ cDC1 as an important driver of liver pathology. Single-cell analyses reveal cDC1 as conserved immunological drivers of non-alcoholic steatohepatitis in mice and humans, Author(s): Aleksandra Deczkowska [sup.1] [sup.14] , Eyal David [sup.1] , Pierluigi Ramadori [sup.2] , Dominik Pfister [sup.2] , Michael Safran [sup.3] , Baoguo At the [sup.1] , Amir Giladi [sup.1] [...]

Published
2021
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35. A maternal-effect selfish genetic element in Caenorhabditis elegans

Author

Ben-David, Eyal, Burga, Alejandro, and Kruglyak, Leonid

Subjects
Biological Sciences, Genetics, Generic health relevance, Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Embryo, Nonmammalian, Female, Genes, Lethal, Male, Maternal Inheritance, Repetitive Sequences, Nucleic Acid, General Science & Technology
Abstract

Selfish genetic elements spread in natural populations and have an important role in genome evolution. We discovered a selfish element causing embryonic lethality in crosses between wild strains of the nematode Caenorhabditis elegans The element is made up of sup-35, a maternal-effect toxin that kills developing embryos, and pha-1, its zygotically expressed antidote. pha-1 has long been considered essential for pharynx development on the basis of its mutant phenotype, but this phenotype arises from a loss of suppression of sup-35 toxicity. Inactive copies of the sup-35/pha-1 element show high sequence divergence from active copies, and phylogenetic reconstruction suggests that they represent ancestral stages in the evolution of the element. Our results suggest that other essential genes identified by genetic screens may turn out to be components of selfish elements.

Published
2017

36. A genetic signature of the evolution of loss of flight in the Galapagos cormorant

Author

Burga, Alejandro, Wang, Weiguang, Ben-David, Eyal, Wolf, Paul C, Ramey, Andrew M, Verdugo, Claudio, Lyons, Karen, Parker, Patricia G, and Kruglyak, Leonid

Subjects
Biological Sciences, Evolutionary Biology, Genetics, Human Genome, Animals, Biological Evolution, Birds, Bone and Bones, Caenorhabditis elegans, Cell Differentiation, Cell Line, Chondrogenesis, Cilia, Ecuador, Gene Expression Regulation, Genetic Variation, Genome, Homeodomain Proteins, Mutation, Phylogeny, Wings, Animal, General Science & Technology
Abstract

We have a limited understanding of the genetic and molecular basis of evolutionary changes in the size and proportion of limbs. We studied wing and pectoral skeleton reduction leading to flightlessness in the Galapagos cormorant (Phalacrocorax harrisi). We sequenced and de novo assembled the genomes of four cormorant species and applied a predictive and comparative genomics approach to find candidate variants that may have contributed to the evolution of flightlessness. These analyses and cross-species experiments in Caenorhabditis elegans and in chondrogenic cell lines implicated variants in genes necessary for transcriptional regulation and function of the primary cilium. Cilia are essential for Hedgehog signaling, and humans affected by skeletal ciliopathies suffer from premature bone growth arrest, mirroring skeletal features associated with loss of flight.

Published
2017

37. Deciphering the state of immune silence in fatal COVID-19 patients

Author

Pierre Bost, Francesco De Sanctis, Stefania Canè, Stefano Ugel, Katia Donadello, Monica Castellucci, David Eyal, Alessandra Fiore, Cristina Anselmi, Roza Maria Barouni, Rosalinda Trovato, Simone Caligola, Alessia Lamolinara, Manuela Iezzi, Federica Facciotti, Annarita Mazzariol, Davide Gibellini, Pasquale De Nardo, Evelina Tacconelli, Leonardo Gottin, Enrico Polati, Benno Schwikowski, Ido Amit, and Vincenzo Bronte

Subjects
Science
Abstract

Integrated studies of matched tissue sites and cell types in COVID-19 patients are important to define the immune mechanisms of pathology. Here, the authors describe an immune signature in fatal COVID-19 patients harmonizing single-cell RNA sequencing of blood and matched BAL cells with deep clinical, immunological and functional data.

Published
2021
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38. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing

Author

Cohen, Yael C., Zada, Mor, Wang, Shuang-Yin, Bornstein, Chamutal, David, Eyal, Moshe, Adi, Li, Baoguo, Shlomi-Loubaton, Shir, Gatt, Moshe E., Gur, Chamutal, Lavi, Noa, Ganzel, Chezi, Luttwak, Efrat, Chubar, Evgeni, Rouvio, Ory, Vaxman, Iuliana, and Pasvolsky, Oren

Subjects
Oncology, Experimental, Molecular dynamics -- Research, Drug resistance -- Research, Multiple myeloma -- Drug therapy -- Patient outcomes, Cancer -- Relapse -- Research, Biological sciences, Health
Abstract

Multiple myeloma (MM) is a neoplastic plasma-cell disorder characterized by clonal proliferation of malignant plasma cells. Despite extensive research, disease heterogeneity within and between treatment-resistant patients is poorly characterized. In the present study, we conduct a prospective, multicenter, single-arm clinical trial (NCT04065789), combined with longitudinal single-cell RNA-sequencing (scRNA-seq) to study the molecular dynamics of MM resistance mechanisms. Newly diagnosed MM patients (41), who either failed to respond or experienced early relapse after a bortezomib-containing induction regimen, were enrolled to evaluate the safety and efficacy of a daratumumab, carfilzomib, lenalidomide and dexamethasone combination. The primary clinical endpoint was safety and tolerability. Secondary endpoints included overall response rate, progression-free survival and overall survival. Treatment was safe and well tolerated; deep and durable responses were achieved. In prespecified exploratory analyses, comparison of 41 primary refractory and early relapsed patients, with 11 healthy subjects and 15 newly diagnosed MM patients, revealed new MM molecular pathways of resistance, including hypoxia tolerance, protein folding and mitochondria respiration, which generalized to larger clinical cohorts (CoMMpass). We found peptidylprolyl isomerase A (PPIA), a central enzyme in the protein-folding response pathway, as a potential new target for resistant MM. CRISPR-Cas9 deletion of PPIA or inhibition of PPIA with a small molecule inhibitor (ciclosporin) significantly sensitizes MM tumor cells to proteasome inhibitors. Together, our study defines a roadmap for integrating scRNA-seq in clinical trials, identifies a signature of highly resistant MM patients and discovers PPIA as a potent therapeutic target for these tumors. Integration of longitudinal single-cell analysis of relapsed and refractory multiple myeloma patients in a prospective clinical trial uncovers new pathways of drug resistance and identifies potential actionable targets., Author(s): Yael C. Cohen [sup.1] [sup.2] , Mor Zada [sup.1] [sup.3] , Shuang-Yin Wang [sup.3] , Chamutal Bornstein [sup.3] , Eyal David [sup.3] , Adi Moshe [sup.3] , Baoguo Li [...]

Published
2021
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39. Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

Author

Kang, Eun Yong, Martin, Lisa J, Mangul, Serghei, Isvilanonda, Warin, Zou, Jennifer, Ben-David, Eyal, Han, Buhm, Lusis, Aldons J, Shifman, Sagiv, and Eskin, Eleazar

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Biotechnology, Bioengineering, 2.1 Biological and endogenous factors, Aetiology, Algorithms, Alleles, Cell Line, Tumor, Europe, Genome-Wide Association Study, HapMap Project, Humans, Immune System Diseases, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Quantitative Trait Loci, Transcriptome, White People, Allele specific expression, expression quantitative trait loci, causal variants, Developmental Biology, Biochemistry and cell biology
Abstract

The study of the genetics of gene expression is of considerable importance to understanding the nature of common, complex diseases. The most widely applied approach to identifying relationships between genetic variation and gene expression is the expression quantitative trait loci (eQTL) approach. Here, we increased the computational power of eQTL with an alternative and complementary approach based on analyzing allele specific expression (ASE). We designed a novel analytical method to identify cis-acting regulatory variants based on genome sequencing and measurements of ASE from RNA-sequencing (RNA-seq) data. We evaluated the power and resolution of our method using simulated data. We then applied the method to map regulatory variants affecting gene expression in lymphoblastoid cell lines (LCLs) from 77 unrelated northern and western European individuals (CEU), which were part of the HapMap project. A total of 2309 SNPs were identified as being associated with ASE patterns. The SNPs associated with ASE were enriched within promoter regions and were significantly more likely to signal strong evidence for a regulatory role. Finally, among the candidate regulatory SNPs, we identified 108 SNPs that were previously associated with human immune diseases. With further improvements in quantifying ASE from RNA-seq, the application of our method to other datasets is expected to accelerate our understanding of the biological basis of common diseases.

Published
2016

40. Imputing Phenotypes for Genome-wide Association Studies

Author

Hormozdiari, Farhad, Kang, Eun Yong, Bilow, Michael, Ben-David, Eyal, Vulpe, Chris, McLachlan, Stela, Lusis, Aldons J, Han, Buhm, and Eskin, Eleazar

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Cardiovascular, Bioengineering, Prevention, Human Genome, Animals, Blood Pressure, Body Mass Index, Cohort Studies, Datasets as Topic, Finland, Genome-Wide Association Study, Genotype, Humans, Mice, Models, Genetic, Multifactorial Inheritance, Phenotype, Reproducibility of Results, Research Design, Sample Size, Triglycerides, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Genome-wide association studies (GWASs) have been successful in detecting variants correlated with phenotypes of clinical interest. However, the power to detect these variants depends on the number of individuals whose phenotypes are collected, and for phenotypes that are difficult to collect, the sample size might be insufficient to achieve the desired statistical power. The phenotype of interest is often difficult to collect, whereas surrogate phenotypes or related phenotypes are easier to collect and have already been collected in very large samples. This paper demonstrates how we take advantage of these additional related phenotypes to impute the phenotype of interest or target phenotype and then perform association analysis. Our approach leverages the correlation structure between phenotypes to perform the imputation. The correlation structure can be estimated from a smaller complete dataset for which both the target and related phenotypes have been collected. Under some assumptions, the statistical power can be computed analytically given the correlation structure of the phenotypes used in imputation. In addition, our method can impute the summary statistic of the target phenotype as a weighted linear combination of the summary statistics of related phenotypes. Thus, our method is applicable to datasets for which we have access only to summary statistics and not to the raw genotypes. We illustrate our approach by analyzing associated loci to triglycerides (TGs), body mass index (BMI), and systolic blood pressure (SBP) in the Northern Finland Birth Cohort dataset.

Published
2016

41. Supplementary Figure S4 from Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary

Author

Yofe, Ido, primary, Shami, Tamar, primary, Cohen, Noam, primary, Landsberger, Tomer, primary, Sheban, Fadi, primary, Stoler-Barak, Liat, primary, Yalin, Adam, primary, Phan, Truong San, primary, Li, Baoguo, primary, Monteran, Lea, primary, Scharff, Ye'ela, primary, Giladi, Amir, primary, Elbaz, Miriam, primary, David, Eyal, primary, Gurevich-Shapiro, Anna, primary, Gur, Chamutal, primary, Shulman, Ziv, primary, Erez, Neta, primary, and Amit, Ido, primary

Published
2023
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42. Supplementary Table S2 from Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary

Author

Yofe, Ido, primary, Shami, Tamar, primary, Cohen, Noam, primary, Landsberger, Tomer, primary, Sheban, Fadi, primary, Stoler-Barak, Liat, primary, Yalin, Adam, primary, Phan, Truong San, primary, Li, Baoguo, primary, Monteran, Lea, primary, Scharff, Ye'ela, primary, Giladi, Amir, primary, Elbaz, Miriam, primary, David, Eyal, primary, Gurevich-Shapiro, Anna, primary, Gur, Chamutal, primary, Shulman, Ziv, primary, Erez, Neta, primary, and Amit, Ido, primary

Published
2023
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43. Data from Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary

Author

Yofe, Ido, primary, Shami, Tamar, primary, Cohen, Noam, primary, Landsberger, Tomer, primary, Sheban, Fadi, primary, Stoler-Barak, Liat, primary, Yalin, Adam, primary, Phan, Truong San, primary, Li, Baoguo, primary, Monteran, Lea, primary, Scharff, Ye'ela, primary, Giladi, Amir, primary, Elbaz, Miriam, primary, David, Eyal, primary, Gurevich-Shapiro, Anna, primary, Gur, Chamutal, primary, Shulman, Ziv, primary, Erez, Neta, primary, and Amit, Ido, primary

Published
2023
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44. Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma

Author

Kirschenbaum, Daniel, primary, Xie, Ken, additional, Ingelfinger, Florian, additional, Katzenelenbogen, Yonatan, additional, Abadie, Kathleen, additional, Look, Thomas, additional, Sheban, Fadi, additional, Phan, Truong San, additional, Li, Baoguo, additional, Zwicky, Pascale, additional, Yofe, Ido, additional, David, Eyal, additional, Mazuz, Kfir, additional, Hou, Jinchao, additional, Chen, Yun, additional, Shaim, Hila, additional, Shanley, Mayra, additional, Becker, Soeren, additional, Qian, Jiawen, additional, Colonna, Marco, additional, Ginhoux, Florent, additional, Rezvani, Katayoun, additional, Theis, Fabian J., additional, Yosef, Nir, additional, Weiss, Tobias, additional, Weiner, Assaf, additional, and Amit, Ido, additional

Published
2023
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46. Cancer-associated fibroblast compositions change with breast cancer progression linking the ratio of S100A4+ and PDPN+ CAFs to clinical outcome

Author

Friedman, Gil, Levi-Galibov, Oshrat, David, Eyal, Bornstein, Chamutal, Giladi, Amir, Dadiani, Maya, Mayo, Avi, Halperin, Coral, Pevsner-Fischer, Meirav, Lavon, Hagar, Mayer, Shimrit, Nevo, Reinat, Stein, Yaniv, Balint-Lahat, Nora, Barshack, Iris, Ali, H. Raza, Caldas, Carlos, Nili-Gal-Yam, Einav, Alon, Uri, Amit, Ido, and Scherz-Shouval, Ruth

Published
2020
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48. Spatial and temporal mapping of breast cancer lung metastases identify TREM2 macrophages as regulators of the metastatic boundary

Author

Yofe, Ido, primary, Shami, Tamar, additional, Cohen, Noam, additional, Landsberger, Tomer, additional, Sheban, Fadi, additional, Stoler-Barak, Liat, additional, Yalin, Adam, additional, Phan, Truong San, additional, Li, Baoguo, additional, Monteran, Lea, additional, Scharff, Ye'ela, additional, Giladi, Amir, additional, Elbaz, Miriam, additional, David, Eyal, additional, Gurevich-Shapiro, Anna, additional, Gur, Chamutal, additional, Shulman, Ziv, additional, Erez, Neta, additional, and Amit, Ido, additional

Published
2023
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