Senior, Andrew

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 43,764

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 10,260

WaveNet: a generative model for raw audio

Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 4,911

Protein complex prediction with AlphaFold-Multimer

Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,808

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,378

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,025

Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 2,973

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 2,163

Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,678

Deep Audio-Visual Speech Recognition

Afouras, T., Chung, J. S., Senior, A., Vinyals, O., & Zisserman, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Speech and Audio ProcessingSpeech Recognition and Synthesis

Cited by 801

Quantum error correction below the surface code threshold

Collaborators, G. Q. A. A., Acharya, R., Abanin, D. A., Aghababaie-Beni, L., Aleiner, I., Andersen, T. I., Ansmann, M., Arute, F., Arya, K., Asfaw, A., et al. (2025) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 778

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 395

Advancing regulatory variant effect prediction with AlphaGenome

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 373

Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2019) · Proteins: Structure, Function, and Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 347

Latent Predictor Networks for Code Generation

Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., & Senior, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Software Engineering ResearchTopic Modeling

Cited by 307

Learning high-accuracy error decoding for quantum processors

Bausch, J., Senior, A. W., Heras, F. J. H., Edlich, T., Davies, A., Newman, M., Jones, C., Satzinger, K., Niu, M. Y., Blackwell, S., et al. (2024) · Nature

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 106

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Rae, J. W., Hunt, J. J., Harley, T., Danihelka, I., Senior, A., Wayne, G., Graves, A., & Lillicrap, T. P. (2016) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 89

Scaling and logic in the colour code on a superconducting quantum processor

Lacroix, N., Bourassa, A., Heras, F. J. H., Zhang, L. M., Bausch, J., Senior, A. W., Edlich, T., Shutty, N., Sivak, V., Bengtsson, A., et al. (2025) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 51