1 code implementation • 21 Dec 2023 • Alex Morehead, Jeffrey Ruffolo, Aadyot Bhatnagar, Ali Madani
In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise.
4 code implementations • 27 Jun 2022 • Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
no code implementations • bioRxiv 2022 • Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications.
no code implementations • 29 Sep 2021 • Ben Krause, Nikhil Naik, Wenhao Liu, Ali Madani
Predicting the fitness, i. e. functional value, of a protein sequence is an important and challenging task in biology, particularly due to the scarcity of assay-labeled data.
1 code implementation • NeurIPS 2021 • Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik
Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution.
no code implementations • 1 Dec 2020 • Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani
Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks.
2 code implementations • ICLR 2021 • Jesse Vig, Ali Madani, Lav R. Varshney, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Transformer architectures have proven to learn useful representations for protein classification and generation tasks.
2 code implementations • 8 Mar 2020 • Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, Richard Socher
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
no code implementations • 22 Oct 2019 • Ali Madani, Cyna Shirazinejad, Jia Rui Ong, Hengameh Shams, Mohammad Mofrad
Graph neural networks are a quickly emerging field for non-Euclidean data that leverage the inherent graphical structure to predict node, edge, and global-level properties of a system.
no code implementations • 5 Sep 2018 • Mehdi Moradi, Ali Madani, Yaniv Gur, Yufan Guo, Tanveer Syeda-Mahmood
The source of big data is typically large image collections and clinical reports recorded for these images.
no code implementations • 27 Jun 2017 • Ali Madani, Ramy Arnaout, Mohammad Mofrad, Rima Arnaout
The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views.