Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

A tutorial on hidden Markov models and selected applications in speech recognition

Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743

AUGUSTUS: ab initio prediction of alternative transcripts

Stanke, M., Keller, O., Gunduz, I., Hayes, A., Waack, S., & Morgenstern, B. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl200 · Cited by 2,829

Genome sequence, comparative analysis and haplotype structure of the domestic dog

members, B. S. P., Lindblad-Toh, K., Wade, C. M., Mikkelsen, T. S., Karlsson, E. K., Jaffe, D. B., Kamal, M., Clamp, M., Chang, J. L., Kulbokas, E. J., et al. (2005) · Nature · doi:10.1038/nature04338 · Cited by 2,568

PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

Wang, K., Li, M., Hadley, D., Liu, R., Glessner, J., Grant, S. F. A., Hakonarson, H., & Bucan, M. (2007) · Genome Research · doi:10.1101/gr.6861907 · Cited by 1,792

Untitled work

(2003) · Cited by 1,680

Three decades of nanopore sequencing

Deamer, D., Akeson, M., & Branton, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3423 · Cited by 1,292

Factorial Hidden Markov Models

Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning · doi:10.1023/a:1007425814087 · Cited by 1,157

A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities

Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036

Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

Kudo, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1007 · Cited by 1,021

Deep Visual-Semantic Alignments for Generating Image Descriptions

Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598339 · Cited by 1,048

The hierarchical hidden Markov model: Analysis and applications

Fine, S., Singer, Y., & Tishby, N. (1998) · Machine Learning · doi:10.1023/a:1007469218079 · Cited by 906

Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs

Korn, J. M., Kuruvilla, F. G., McCarroll, S. A., Wysoker, A., Nemesh, J., Cawley, S., Hubbell, E., Veitch, J., Collins, P. J., Darvishi, K., et al. (2008) · Nature Genetics · doi:10.1038/ng.237 · Cited by 783

Neural Speech Synthesis with Transformer Network

Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016706 · Cited by 754

Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers

Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A., & Lis, J. T. (2014) · Nature Genetics · doi:10.1038/ng.3142 · Cited by 693

Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria

Moses, D. A., Metzger, S. L., Liu, J. R., Anumanchipalli, G. K., Makin, J. G., Sun, P. F., Chartier, J., Dougherty, M. E., Liu, P. M., Abrams, G. M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2027540 · Cited by 582

Evidence for archaic adaptive introgression in humans

Racimo, F., Sankararaman, S., Nielsen, R., & Huerta-Sánchez, E. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3936 · Cited by 521

Discovering Activities to Recognize and Track in a Smart Environment

Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.148 · Cited by 462

Learning Setting-Generalized Activity Models for Smart Spaces

Cook, D. (2012) · IEEE Intelligent Systems · doi:10.1109/mis.2010.112 · Cited by 445

Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns

Tu, W., Cao, J., Yue, Y., Shaw, S.-L., Zhou, M., Wang, Z., Chang, X., Xu, Y., & Li, Q. (2017) · International Journal of Geographical Information Science · doi:10.1080/13658816.2017.1356464 · Cited by 298

Untitled work

(1998) · MPG.PuRe (Max Planck Society) · Cited by 293

Fast Algorithms for Signal Processing

Blahut, R. E. (2010) · doi:10.1017/cbo9780511760921 · Cited by 274

Recognizing independent and joint activities among multiple residents in smart environments

Singla, G., Cook, D. J., & Schmitter-Edgecombe, M. (2010) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-009-0007-1 · Cited by 265

Real-time decoding of question-and-answer speech dialogue using human cortical activity

Moses, D. A., Leonard, M. K., Makin, J. G., & Chang, E. F. (2019) · Nature Communications · doi:10.1038/s41467-019-10994-4 · Cited by 224

GraphAligner: rapid and versatile sequence-to-graph alignment

Rautiainen, M., & Marschall, T. (2020) · Genome Biology · doi:10.1186/s13059-020-02157-2 · Cited by 242

Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones

Anderson, I., Maitland, J., Sherwood, S., Barkhuus, L., Chalmers, M., Hall, M., Brown, B., & Muller, H. (2007) · Mobile Networks and Applications · doi:10.1007/s11036-007-0011-7 · Cited by 223

Object Detection and Tracking for Autonomous Navigation in Dynamic Environments

Ess, A., Schindler, K., Leibe, B., & Van Gool, L. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910365417 · Cited by 191

The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection

Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175

Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model

(2025) · doi:10.1038/s41592-025-02939-1 · Cited by 158

Machine learning for inferring animal behavior from location and movement data

Wang, G. (2019) · Ecological Informatics · doi:10.1016/j.ecoinf.2018.12.002 · Cited by 154

COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems

Rashidi, P., & Cook, D. J. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508045 · Cited by 94

Hippocampal replay of experience at real-world speeds

Denovellis, E. L., Gillespie, A. K., Coulter, M. E., Sosa, M., Chung, J. E., Eden, U. T., & Frank, L. M. (2021) · eLife · doi:10.7554/elife.64505 · Cited by 89

LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks

Jiang, Y., Kim, H., Asnani, H., Kannan, S., Oh, S., & Viswanath, P. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2988577 · Cited by 87

Improving reference prioritisation with PICO recognition.

Brockmeier, A. J., Ju, M., Przybyła, P., & Ananiadou, S. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0992-8 · Cited by 62

Bayesian spiking neurons II: learning

Deneve, S. (2008) · Neural Computation · doi:10.1162/neco.2008.20.1.118 · Cited by 71

Video Rewrite: Driving Visual Speech with Audio

Bregler, C., Covell, M., & Slaney, M. (2023) · Seminal Graphics Papers: Pushing the Boundaries, Volume 2 · doi:10.1145/3596711.3596787 · Cited by 67

Hunting for Insider Threats Using LSTM-Based Anomaly Detection

Villarreal-Vasquez, M., Modelo-Howard, G., Dube, S., & Bhargava, B. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3135639 · Cited by 52

Untangling serially dependent underreported count data for gender-based violence

Fernández‐Fontelo, A., Cabaña, A., Joe, H., Puig, P., & Moriña, D. (2019) · Statistics in Medicine · doi:10.1002/sim.8306 · Cited by 47

Regulation of spatiotemporal limits of developmental gene expression via enhancer grammar

Keller, S. H., Jena, S. G., Yamazaki, Y., & Lim, B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917040117 · Cited by 36

Highly accurate ab initio gene annotation with ANNEVO

Zhang, P., Xu, T., Wang, S., Yang, X., Sun, P., Jia, P., Lin, J., Wang, B., Zhang, Y., Meng, D., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03036-7 · Cited by 34

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

Feng, S.-H., Xia, C.-Q., & Shen, H.-B. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab744 · Cited by 21

MobiGesture: Mobility-aware hand gesture recognition for healthcare

Zhao, H., Ma, Y., Wang, S., Watson, A., & Zhou, G. (2018) · Smart Health · doi:10.1016/j.smhl.2018.07.010 · Cited by 18

Quantum-Assisted Joint Multi-Objective Routing and Load Balancing for Socially-Aware Networks

Alanis, D., Hu, J., Botsinis, P., Babar, Z., Ng, S. X., & Hanzo, L. (2016) · IEEE Access · doi:10.1109/access.2016.2629671 · Cited by 15

Transfer of knowledge from model organisms to evolutionarily distant non-model organisms: The coral Pocillopora damicornis membrane signaling receptome

Kumar, L., Brenner, N., Sledzieski, S., Olaosebikan, M., Roger, L. M., Lynn-Goin, M., Klein-Seetharaman, R., Berger, B., Putnam, H., Yang, J., et al. (2023) · PLOS ONE · doi:10.1371/journal.pone.0270965 · Cited by 11