Publications

[Preprint] OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Published in bioRxiv, 2022

Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2, and OpenProteinSet, the largest public database of protein multiple sequence alignments. We use OpenProteinSet to train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFold's capacity to generalize across fold space by retraining it using carefully designed datasets.

Recommended citation: OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney, Stella Biderman, Andrew M Watkins, Stephen Ra, Pablo Ribalta Lorenzo, Lucas Nivon, Brian Weitzner, Yih-En Andrew Ban, Peter K Sorger, Emad Mostaque, Zhao Zhang, Richard Bonneau, Mohammed AlQuraishi; bioRxiv 2022.11.20.517210; doi: https://doi.org/10.1101/2022.11.20.517210 https://www.biorxiv.org/content/10.1101/2022.11.20.517210v2

A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework

Published in International Conference on Algorithms for Computational Biology, 2021

The main contributions of this paper include a practical one-stage group testing protocol guided by maximizing pool entropy and a maximum-likelihood recovery algorithm under the probabilistic framework.

Recommended citation: Liu, Y., Kadyan, S., Pe’er, I. (2021). A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework. In: Martín-Vide, C., Vega-Rodríguez, M.A., Wheeler, T. (eds) Algorithms for Computational Biology. AlCoB 2021. Lecture Notes in Computer Science(), vol 12715. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-74432-8_4

[Preprint] A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework

Published in medRxiv, 2021

Recommended citation: Liu, Y., Kadyan, S., Pe’er, I. (2021). A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework. medRxiv 2021.03.09.21253193; doi: https://doi.org/10.1101/2021.03.09.21253193 https://www.medrxiv.org/content/10.1101/2021.03.09.21253193v1