Overview Aotearoa Research Science
In this branch: Overview Neuroscience Bioinformatics Imaging Clinical AI Drug Discovery References
Research · Medical Science

Bibliography & References

Full bibliography for the Medical Science branch. Entries are grouped by topic area and cross-referenced from inline citations throughout the articles.

Bibliography

Neuroscience & AI

  1. Poirazi, P., & Papoutsi, A. (2020). Illuminating dendritic function with computational models. Nature Reviews Neuroscience, 21, 541–553.
  2. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607–609.
  3. Bi, G. Q., & Poo, M. M. (1998). Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience, 18(24), 10464–10472.
  4. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex. Nature Neuroscience, 2, 79–87.
  5. Neftci, E. O., Mostafa, H., & Zenke, F. (2019). Surrogate gradient learning in spiking neural networks. IEEE Signal Processing Magazine, 36(6), 51–63.
  6. Davies, M., et al. (2021). Advancing neuromorphic computing with Loihi: A survey of results and outlook. Proceedings of the IEEE, 109(5), 911–934.
  7. Hochberg, L. R., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485, 372–375.
  8. Pandarinath, C., et al. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods, 15, 805–815.
  9. Willett, F. R., et al. (2021). High-performance brain-to-text communication via handwriting. Nature, 593, 249–254.
  10. Willett, F. R., et al. (2023). A high-performance speech neuroprosthesis. Nature, 620, 1031–1036.
  11. Dorkenwald, S., et al. (2024). Neuronal wiring diagram of an adult brain. Nature, 634, 124–138.
  12. Pachitariu, M., et al. (2017). Suite2p: Beyond 10,000 neurons with standard two-photon microscopy. bioRxiv.

Bioinformatics

  1. Eddy, S. R. (2011). Accelerated profile HMM searches. PLOS Computational Biology, 7(10), e1002195.
  2. Poplin, R., et al. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology, 36, 983–987.
  3. Avsec, Ž., et al. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods, 18, 1196–1203.
  4. Lopez, R., et al. (2018). Deep generative modelling for single-cell transcriptomics. Nature Methods, 15, 1053–1058.
  5. Jumper, J., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589.
  6. Lin, Z., et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379, 1123–1130.
  7. Hayes, T., et al. (2025). Simulating 500 million years of evolution with a language model. Science, 387, 850–858.

Medical Imaging AI

  1. Rajpurkar, P., et al. (2017). CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225.
  2. Gulshan, V., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410.
  3. McKinney, S. M., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577, 89–94.
  4. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI).
  5. Isensee, F., et al. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18, 203–211.
  6. Ma, J., et al. (2024). Segment anything in medical images. Nature Communications, 15, 654.
  7. Lu, M. Y., et al. (2021). Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering, 5, 555–570.
  8. Zech, J. R., et al. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs. PLOS Medicine, 15(11), e1002686.
  9. Zhou, Y., et al. (2023). A foundation model for generalizable disease detection from retinal images. Nature, 622, 156–163.

Clinical AI

  1. Choi, E., et al. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. Advances in Neural Information Processing Systems, 29.
  2. Li, Y., et al. (2020). BEHRT: Transformer for electronic health records. Scientific Reports, 10, 7155.
  3. Wong, A., et al. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070.
  4. Lee, J., et al. (2020). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
  5. Singhal, K., et al. (2023). Large language models encode clinical knowledge. Nature, 620, 172–180.
  6. Obermeyer, Z., et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

Drug Discovery

  1. Yang, K., et al. (2019). Analyzing learned molecular representations for property prediction. Journal of Chemical Information and Modeling, 59(8), 3370–3388.
  2. Jin, W., Barzilay, R., & Jaakkola, T. (2018). Junction tree variational autoencoder for molecular graph generation. International Conference on Machine Learning (ICML).
  3. Schneuing, A., et al. (2022). Structure-based drug design with equivariant diffusion models. arXiv:2210.13695.
  4. Corso, G., et al. (2023). DiffDock: Diffusion steps, twists, and turns for molecular docking. International Conference on Learning Representations (ICLR 2023).
  5. Huang, K., et al. (2021). Therapeutics Data Commons: Machine learning datasets and tasks for drug discovery and development. Advances in Neural Information Processing Systems.
  6. Abramson, J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630, 493–500.