Filter By:

Article Type
  • Training a deep neural network can be costly but training time is reduced when a pre-trained network can be adapted to different use cases. Ideally, only a small number of parameters needs to be changed in this process of fine-tuning, which can then be more easily distributed. In this Analysis, different methods of fine-tuning with only a small number of parameters are compared on a large set of natural language processing tasks.

    • Ning Ding
    • Yujia Qin
    • Maosong Sun
    AnalysisOpen Access
  • Recent developments in deep learning have allowed for a leap in computational analysis of epigenomic data, but a fair comparison of different architectures is challenging. Toneyan et al. use GOPHER, their new framework for model evaluation and comparison, to perform a comprehensive analysis, exploring modelling choices of deep learning for epigenomic profiles.

    • Shushan Toneyan
    • Ziqi Tang
    • Peter K. Koo
  • Deep learning methods have in recent years shown promising results in characterizing proteins and extracting complex sequence–structure–function relationships. This Analysis describes a benchmarking study to compare the performances and advantages of recent deep learning approaches in a range of protein prediction tasks.

    • Serbulent Unsal
    • Heval Atas
    • Tunca Doğan
  • Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

    • Michael Roberts
    • Derek Driggs
    • Carola-Bibiane Schönlieb
    AnalysisOpen Access
  • Several technology companies offer platforms for users without coding experience to develop deep learning algorithms. This Analysis compares the performance of six ‘code-free deep learning’ platforms (from Amazon, Apple, Clarifai, Google, MedicMind and Microsoft) in creating medical image classification models.

    • Edward Korot
    • Zeyu Guan
    • Pearse A. Keane
    AnalysisOpen Access
  • 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