Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Bunne, Charlotte"'
Autor:
Bunne, Charlotte, Roohani, Yusuf, Rosen, Yanay, Gupta, Ankit, Zhang, Xikun, Roed, Marcel, Alexandrov, Theo, AlQuraishi, Mohammed, Brennan, Patricia, Burkhardt, Daniel B., Califano, Andrea, Cool, Jonah, Dernburg, Abby F., Ewing, Kirsty, Fox, Emily B., Haury, Matthias, Herr, Amy E., Horvitz, Eric, Hsu, Patrick D., Jain, Viren, Johnson, Gregory R., Kalil, Thomas, Kelley, David R., Kelley, Shana O., Kreshuk, Anna, Mitchison, Tim, Otte, Stephani, Shendure, Jay, Sofroniew, Nicholas J., Theis, Fabian, Theodoris, Christina V., Upadhyayula, Srigokul, Valer, Marc, Wang, Bo, Xing, Eric, Yeung-Levy, Serena, Zitnik, Marinka, Karaletsos, Theofanis, Regev, Aviv, Lundberg, Emma, Leskovec, Jure, Quake, Stephen R.
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intell
Externí odkaz:
http://arxiv.org/abs/2409.11654
It is now possible to conduct large scale perturbation screens with complex readout modalities, such as different molecular profiles or high content cell images. While these open the way for systematic dissection of causal cell circuits, integrated s
Externí odkaz:
http://arxiv.org/abs/2405.00838
Autor:
van Gerwen, Puck, Briling, Ksenia R., Bunne, Charlotte, Somnath, Vignesh Ram, Laplaza, Ruben, Krause, Andreas, Corminboeuf, Clemence
Publikováno v:
J. Chem. Inf. Model. 64, 5771-5785 (2024)
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success,
Externí odkaz:
http://arxiv.org/abs/2312.08307
Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery. Current approaches to sample transition paths use Markov chain Monte Carlo and rely on time-in
Externí odkaz:
http://arxiv.org/abs/2312.05340
Autor:
Serrano, Erik, Chandrasekaran, Srinivas Niranj, Bunten, Dave, Brewer, Kenneth I., Tomkinson, Jenna, Kern, Roshan, Bornholdt, Michael, Fleming, Stephen, Pei, Ruifan, Arevalo, John, Tsang, Hillary, Rubinetti, Vincent, Tromans-Coia, Callum, Becker, Tim, Weisbart, Erin, Bunne, Charlotte, Kalinin, Alexandr A., Senft, Rebecca, Taylor, Stephen J., Jamali, Nasim, Adeboye, Adeniyi, Abbasi, Hamdah Shafqat, Goodman, Allen, Caicedo, Juan C., Carpenter, Anne E., Cimini, Beth A., Singh, Shantanu, Way, Gregory P.
Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process the
Externí odkaz:
http://arxiv.org/abs/2311.13417
Schr\"odinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems. Such natural processes are commonly subject to changes in population size over time due to the e
Externí odkaz:
http://arxiv.org/abs/2306.09099
Autor:
Somnath, Vignesh Ram, Pariset, Matteo, Hsieh, Ya-Ping, Martinez, Maria Rodriguez, Krause, Andreas, Bunne, Charlotte
Diffusion Schr\"odinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Despite numerous successful applications, existing algorithms for solving
Externí odkaz:
http://arxiv.org/abs/2302.11419
Autor:
Lübeck, Frederike, Bunne, Charlotte, Gut, Gabriele, del Castillo, Jacobo Sarabia, Pelkmans, Lucas, Alvarez-Melis, David
Comparing unpaired samples of a distribution or population taken at different points in time is a fundamental task in many application domains where measuring populations is destructive and cannot be done repeatedly on the same sample, such as in sin
Externí odkaz:
http://arxiv.org/abs/2209.15621
Optimal transport (OT) theory describes general principles to define and select, among many possible choices, the most efficient way to map a probability measure onto another. That theory has been mostly used to estimate, given a pair of source and t
Externí odkaz:
http://arxiv.org/abs/2206.14262
Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks. One key property for robustness which the learned representation should capture and which recently rec
Externí odkaz:
http://arxiv.org/abs/2206.11646