Volume 5 Issue 4, April 2023

There is a continuing demand for high-quality, large-scale annotated datasets in medical imaging supported by machine learning. A new study investigates the importance of what type of instructions crowdsourced annotators receive.

Predicting whether T cell receptors bind to specific peptides is a challenging problem because most binding examples in the training data involve only a few peptides. A new approach uses meta-learning to improve predictions for binding to peptides for which no or little binding data exists.

One of the main advances in deep learning in the past five years has been graph representation learning, which enabled applications to problems with underlying geometric relationships. Increasingly, such problems involve multiple data modalities and, examining over 160 studies in this area, Ektefaie et al. propose a general framework for multimodal graph learning for image-intensive, knowledge-grounded and language-intensive problems.

Cancer diagnosis and treatment decisions often focus on one data source. Steyaert and colleagues discuss the current status and challenges of data fusion, including electronic health records, molecular data, digital pathology and radiographic images, in cancer research and translational development.

Neuro-symbolic artificial intelligence approaches display both perception and reasoning capabilities, but inherit the limitations of their individual deep learning and symbolic artificial intelligence components. By combining neural networks and vector-symbolic architectures, Hersche and colleagues propose a neuro-vector-symbolic framework that can solve Raven’s progressive matrices tests faster and more accurately than other state-of-the-art methods.

Stochastic reaction networks involve solving a system of ordinary differential equations, which becomes challenging as the number of reactive species grows, but a new approach based on evolving a variational autoregressive neural network provides an efficient way to track time evolution of the joint probability distribution for general reaction networks.

Generative models in cheminformatics depend on molecules being representable as structured data, such as the simplified molecular-input line-entry system (SMILES). Mokaya and colleagues investigated how the choice of representation influences the quality of generated compounds, and found that string-based representations can hinder performance in a curriculum learning setting.

Computational modelling of the interactions between T-cell receptors (TCRs) and epitopes is a crucial yet challenging scientific problem. Peng and colleagues develop a deep learning model to capture TCR–epitope binding patterns, providing useful insights for understanding TCR recognition.

The potential of deep learning in pathological prognosis has been hampered by limited interpretability in clinical applications. Liang and colleagues present a human-centric deep learning framework that supports the discovery of prognostic biomarkers in an interpretable way.

Sepsis treatment needs to be well timed to be effective and to avoid antibiotic resistance. Machine learning can help to predict optimal treatment timing, but confounders in the data hamper reliability. Liu and colleagues present a method to predict patient-specific treatment effects with increased accuracy, accompanied by an uncertainty estimate.

Transformer models are gaining increasing popularity in modelling natural language as they can produce human-sounding text by iteratively predicting the next word in a sentence. Born and Manica apply the idea of Transformer-based text completion to property prediction of chemical compounds by providing the context of a problem and having the model complete the missing information.

Biomedical heterogeneous networks offer potentially rich information for computational drug design approaches, but fully labelled multimodal data are rare. To learn useful representations from diverse and unlabelled data, Wang et al. combine multiple self-supervised tasks to train a graph-attention-based model.

Evolutionary computation methods can find useful solutions for many complex real-world science and engineering problems, but in general there is no guarantee for finding the best solution. This challenge can be tackled with a new framework incorporating machine learning that helps evolutionary methods to avoid local optima.