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  • Foundation models have transformed artificial intelligence by training on vast amounts of broad unlabelled data. Pai et al. present a foundation model leading to more accurate, efficient and robust cancer imaging biomarkers, especially in use cases with small training datasets.

    • Suraj Pai
    • Dennis Bontempi
    • Hugo J. W. L. Aerts
    ArticleOpen Access
  • Deep learning generative approaches have been used in recent years to discover new molecules with drug-like properties. To improve the performance of such approaches, Yang et al. add chemical binding knowledge to a deep generative framework and demonstrate, including by wet-lab verification, that the method can find valid molecules that successfully bind to target proteins.

    • Yuanyuan Jiang
    • Guo Zhang
    • Shengyong Yang
    Article
  • This Reusability Report examines a recently published deep learning method PENCIL by Ren et al. for identifying phenotype populations in single-cell data. Cao et al. reproduce here the main results, analyse the sensitivity of the method to model parameters and describe how the method can be used to create a signature for immunotherapy response markers.

    • Yingying Cao
    • Tian-Gen Chang
    • Eytan Ruppin
    Article
  • Machine learning methods have made great advances in modelling protein sequences for a variety of downstream tasks. The representation used as input for these models has been primarily the sequence of amino acids. Outeiral and Deane show that using codon sequences instead can improve protein representations and lead to model performance.

    • Carlos Outeiral
    • Charlotte M. Deane
    ArticleOpen Access
  • Algorithmic decisions have a history of harming already marginalized populations. In an effort to combat these discriminative patterns, data-driven methods are used to comprehend these patterns, and recently also to identify disadvantaged communities to allocate resources. Huynh et al. analyse one of these tools and show a concerning sensitivity to input parameters that can lead to unintentional biases with substantial financial consequences.

    • Benjamin Q. Huynh
    • Elizabeth T. Chin
    • David H. Rehkopf
    ArticleOpen Access
  • A parameterized physical model that uses unpaired datasets for adaptive holographic imaging was published in Nature Machine Intelligence in 2023. Zhang and colleagues evaluate its performance and extend it to non-perfect optical systems by integrating specific optical response functions.

    • Yuhe Zhang
    • Tobias Ritschel
    • Pablo Villanueva-Perez
    ArticleOpen Access
  • Deep learning language models have proved useful for both natural language and protein modelling. Similar to semantics in natural language, protein functions are complex and depend on the context of their environment, rather than on the similarity of sequences. Kulmanov and colleagues present an approach to frame function prediction as semantic entailment using a neuro-symbolic model to augment a large protein language model.

    • Maxat Kulmanov
    • Francisco J. Guzmán-Vega
    • Robert Hoehndorf
    ArticleOpen Access
  • Denoising low-counting statistics data in the presence of multiple, unknown noise profiles is a challenging task in scientific applications where high accuracy is required. Oppliger and colleagues train a deep convolutional neural network on pairs of experimental low- and high-noise X-ray diffraction data and demonstrate better performance on experimental noise filtering compared with the case of training on artificial data pairs.

    • Jens Oppliger
    • M. Michael Denner
    • Johan Chang
    ArticleOpen Access
  • Great advances in protein structure prediction have been made with recent deep learning-based methods, but proteins interact with their environment and can change shape drastically when binding to ligand molecules. To predict the 3D structure of these combined protein–ligand complexes, Qiao et al. developed a generative diffusion model with biophysical constraints and geometric deep learning.

    • Zhuoran Qiao
    • Weili Nie
    • Animashree Anandkumar
    Article
  • AI-enabled diagnostic applications in healthcare can be powerful, but study design is very important to avoid subtle issues of bias in the dataset and evaluation. Coppock et al. demonstrate how an AI-based classifier for diagnosing SARS-Cov-2 infection from audio recordings can seem to make predictions with high accuracy but shows much lower performance after taking into account confounders, providing insights in study design and replicability in AI-based audio analysis.

    • Harry Coppock
    • George Nicholson
    • Chris Holmes
    ArticleOpen Access
  • Machine learning techniques are widely employed in chemical science, but are application specific and their development requires dedicated expertise. Jablonka and colleagues fine-tune the GPT-3 model and show that it can provide surprisingly accurate answers to a wide range of chemical questions.

    • Kevin Maik Jablonka
    • Philippe Schwaller
    • Berend Smit
    ArticleOpen Access
  • Recent years have seen many advances in deep learning models for protein design, usually involving a large amount of training data. Focusing on potential clinical impact, Garton et al. develop a variational autoencoder approach trained on sparse data of natural sequences of adenoviruses to generate large proteins that can be used as viral vectors in gene therapy.

    • Suyue Lyu
    • Shahin Sowlati-Hashjin
    • Michael Garton
    Article
  • Designing antibodies and assessing their biophysical properties for potential therapeutic development is challenging with current computational methods. Ramon et al. have developed a deep learning approach called AbNatiV, based on a vector-quantized variational encoder that accurately assesses the nativeness of antibodies and nanobodies, which are small single-domain antibodies that have recently attracted considerable interest.

    • Aubin Ramon
    • Montader Ali
    • Pietro Sormanni
    ArticleOpen Access
  • Drug design has recently seen immense improvements in computational methods, but models can still struggle generalizing across binding pockets. Feng and colleagues combine a language model with geometric deep learning to provide efficient generation of potential new drugs.

    • Wei Feng
    • Lvwei Wang
    • Wenbiao Zhou
    ArticleOpen Access
  • Accurate real-time tracking of dexterous hand movements and interactions has applications in human–computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging due to the large number of articulations and degrees of freedom. Tashakori and colleagues report accurate and dynamic tracking of articulated hand and finger movements using machine-learning powered stretchable, washable smart gloves.

    • Arvin Tashakori
    • Zenan Jiang
    • Peyman Servati
    Article