Research articles

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  • A combination of engineering advances shows promise for myoelectric prosthetic hands that are controlled by a user’s remaining muscle activity. Fine finger movements are decoded from surface electromyograms with machine learning algorithms and this is combined with a robotic controller that is active only during object grasping to assist in maximizing contact. This shared control scheme allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is required.

    • Katie Z. Zhuang
    • Nicolas Sommer
    • Silvestro Micera
  • Memristive devices can provide energy-efficient neural network implementations, but they must be tailored to suit different network architectures. Wang et al. develop a trainable weight-sharing mechanism for memristor-based CNNs and ConvLSTMs, achieving a 75% reduction in weights without compromising accuracy.

    • Zhongrui Wang
    • Can Li
    • J. Joshua Yang
  • Controlling the flow and representation of information in deep neural networks is fundamental to making networks intelligible. Bergomi et al introduce a mathematical framework in which the space of possible operators representing the data is constrained by using symmetries. This constrained space is still suitable for machine learning: operators can be efficiently computed, approximated and parameterized for optimization.

    • Mattia G. Bergomi
    • Patrizio Frosini
    • Nicola Quercioli
  • An approach to protein structure prediction is to assemble candidate structures from template fragments, which are extracted from known protein structures. Wang et al. demonstrate that combining deep neural network architectures with a relatively small but high-resolution fragment dataset can improve the quality of the sample fragment libraries used for protein structure prediction.

    • Tong Wang
    • Yanhua Qiao
    • Haipeng Gong
  • When neural networks are retrained to solve more than one problem, they tend to forget what they have learned earlier. Here, the authors propose orthogonal weights modification, a method to avoid this so-called catastrophic forgetting problem. Capitalizing on such an ability, a new module is introduced to enable the network to continually learn context-dependent processing.

    • Guanxiong Zeng
    • Yang Chen
    • Shan Yu
  • Deep neural networks can contain arbitrary mathematical operators, as long as they are derivable. The authors investigate how knowledge about a problem can be incorporated into machine learning through the use of operators that are related to the problem.

    • Andreas K. Maier
    • Christopher Syben
    • Silke Christiansen
  • For some combinatorial puzzles, solutions can be verified to be optimal, for others, the state space is too large to be certain that a solution is optimal. A new deep learning based search heuristic performs well on the iconic Rubik’s cube and can also generalize to puzzles in which optimal solvers are intractable.

    • Forest Agostinelli
    • Stephen McAleer
    • Pierre Baldi
  • Artificial intelligence approaches can aid medicinal chemists to creatively look for new chemical entities with drug-like properties. A rule-based approach combined with a machine learning model was trained on successful synthetic routes described in chemical patent literature. This process produced computer-generated compounds that mimic known medicines.

    • Alexander Button
    • Daniel Merk
    • Gisbert Schneider
  • Reducing the radiation dose for medical CT scans can provide a less invasive imaging method, but requires a method for reconstructing an image up to the image quality from a full-dose scan. In this article, Wang and colleagues show that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

    • Hongming Shan
    • Atul Padole
    • Ge Wang
  • High-throughput brain image registration methods that are independent of any pre-processing steps, and are robust under mild image transformations, could accelerate the study of region-specific changes in brain development. A deep learning-based method is therefore developed for automated registration through segmenting brain regions of interest with minimal human supervision.

    • Asim Iqbal
    • Romesa Khan
    • Theofanis Karayannis
  • Deep neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

    • Julius Upmeier zu Belzen
    • Thore Bürgel
    • Roland Eils
  • Many functions of RNA strands that do not code for proteins are still to be deciphered. Methods to classify different groups of non-coding RNA increasingly use deep learning, but the landscape is diverse and methods need to be categorized and benchmarked to move forward. The authors take a close look at six state-of-the-art deep learning non-coding RNA classifiers and compare their performance and architecture.

    • Noorul Amin
    • Annette McGrath
    • Yi-Ping Phoebe Chen
  • Accurate manoeuvring of autonomous aerial and aquatic robots requires detailed knowledge of the fluid forces, which can be challenging especially in turbulent water or air. A control method for autonomous underwater vehicles (AUVs) uses intelligent distributed sensing inspired by fish ‘lateral line’ sensing. This is used by many species of fish to feel the flow around them and respond instantly, before they are displaced by disturbances. An AUV designed with such a sensory shell similarly compensates for disturbances and has improved position tracking.

    • Michael Krieg
    • Kevin Nelson
    • Kamran Mohseni
  • Clustering groups of cells in single-cell RNA sequencing datasets can produce high-resolution information for complex biological questions. However, it is statistically and computationally challenging due to the low RNA capture rate, which results in a high number of false zero count observations. A deep learning approach called scDeepCluster, which efficiently combines a model for explicitly characterizing missing values with clustering, shows high performance and improved scalability with a computing time increasing linearly with sample size.

    • Tian Tian
    • Ji Wan
    • Zhi Wei
  • Biomedical publications provide a rich and largely untapped source of knowledge. INtERAcT exploits word embeddings trained on a corpus of cancer-specific articles to estimate molecular interactions. The algorithm is able to reconstruct molecular pathways associated with ten cancer types, even in corpora of limited size.

    • Matteo Manica
    • Roland Mathis
    • María Rodríguez Martínez
  • Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.

    • Juan Carrasquilla
    • Giacomo Torlai
    • Leandro Aolita
  • To perform complex tasks, robots need to learn the relationship between their bodies and dynamic environments. A biologically plausible approach to hardware and software design shows that a robotic tendon-driven limb can make effective movements based on a short period of learning.

    • Ali Marjaninejad
    • Darío Urbina-Meléndez
    • Francisco J. Valero-Cuevas