Reviews & Analysis

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  • Deep learning models for sequential data can be trained to make accurate predictions from large biological datasets. New tools from computer vision and natural language processing can help us make these models interpretable to biologists.

    • Ahmed M. Alaa
    News & Views
  • Microscopy-based drug screens with fluorescent markers can shed light on how drugs affect biological processes. Without adding markers and imaging channels, which is cumbersome and costly, a new generative deep-learning method extracts new fluorescence channels from images, potentially improving the drug-discovery pipeline.

    • Florian Heigwer
    News & Views
  • Neural networks can be implemented by using purified DNA molecules that interact in a test tube. Convolutional neural networks to classify high-dimensional data have now been realized in vitro, in one of the most complex demonstrations of molecular programming so far.

    • William Poole
    News & Views
  • Unsupervised machine learning algorithms reduce the dependence on curated, labeled datasets that are characteristic of supervised machine learning. The authors argue that the developmental science of infant cognition could inform the design of unsupervised machine learning approaches.

    • Lorijn Zaadnoordijk
    • Tarek R. Besold
    • Rhodri Cusack
  • Both proteins and natural language are essentially based on a sequential code, but feature complex interactions at multiple scales, which can be useful when transferring machine learning models from one domain to another. In this Review, Ferruz and Höcker summarize recent advances in language models, such as transformers, and their application to protein design.

    • Noelia Ferruz
    • Birte Höcker
    Review Article
  • Despite the growing number of initiatives that employ AI to counter corruption, few studies empirically tackle the political and social consequences of embedding AI in anti-corruption efforts. The authors outline the societal and technical challenges that need to be overcome for AI to fight corruption.

    • Nils Köbis
    • Christopher Starke
    • Iyad Rahwan
  • Machine reading and knowledge extraction methods can be used to mine the scientific literature and reveal the direction and robustness of discoveries. Such efforts now point to the importance of independent tests of reported claims.

    • Luís A. Nunes Amaral
    News & Views
  • It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

    • Dhireesha Kudithipudi
    • Mario Aguilar-Simon
    • Hava Siegelmann
  • GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

    • Mohit Pandey
    • Michael Fernandez
    • Artem Cherkasov
    Review Article
  • Graph convolutional networks have become a popular tool for learning with graphs and networks. We reflect on the reasons behind the success story.

    • Mostafa Haghir Chehreghani
    News & Views
  • Predicting the performance of a tactile sensor from its composition and morphology is nearly impossible with traditional computational approaches. Machine learning can not only predict device-level performance, but also recommend new material compositions for soft machine applications.

    • James T. Glazar
    • Vivek B. Shenoy
    News & Views
  • Drug resistance in tropical diseases such as malaria requires constant improvement and development of new drugs. To find potential candidates, generative machine learning methods that can search for novel bioactive molecules can be employed.

    • David A. Winkler
    News & Views
  • Machine learning applications in agriculture can bring many benefits in crop management and productivity. However, to avoid harmful effects of a new round of technological modernization, fuelled by AI, a thorough risk assessment is required, to review and mitigate risks such as unintended socio-ecological consequences and security concerns associated with applying machine learning models at scale.

    • Asaf Tzachor
    • Medha Devare
    • Seán Ó hÉigeartaigh
  • Although artificial reinforcement learning agents do well when rules are rigid, such as games, they fare poorly in real-world scenarios where small changes in the environment or the required actions can impair performance. The authors provide an overview of the cognitive foundations of hierarchical problem-solving, and propose steps to integrate biologically inspired hierarchical mechanisms to enable problem-solving skills in artificial agents.

    • Manfred Eppe
    • Christian Gumbsch
    • Stefan Wermter
  • Digitally recreating the likeness of a person used to be a costly and complex process. Through the use of generative models, AI-generated characters can now be made with relative ease. Pataranutaporn et al. discuss in this Perspective how this technology can be used for positive applications in education and well-being.

    • Pat Pataranutaporn
    • Valdemar Danry
    • Misha Sra
  • Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

    • Kenneth Atz
    • Francesca Grisoni
    • Gisbert Schneider
    Review Article
  • Newly sequenced organisms present a challenge for protein function prediction, as they lack experimental characterisation. A network-propagation approach that integrates functional network relationships with protein annotations, transferred from well-studied organisms, produces a more complete picture of the possible protein functions.

    • Yingying Zhang
    • Shayne D. Wierbowski
    • Haiyuan Yu
    News & Views
  • Substantial advances have been made in the past decade in developing high-performance machine learning models for medical applications, but translating them into practical clinical decision-making processes remains challenging. This Perspective provides insights into a range of challenges specific to high-dimensional, multimodal medical imaging.

    • Rohan Shad
    • John P. Cunningham
    • William Hiesinger