Articles in 2023

Particle tracking velocimetry to estimate particle displacements in fluid flows in complex experimental scenarios is a challenging task and often comes with high computational cost. Liang and colleagues propose a graph neural network and optimal transport-based algorithm that can greatly improve the accuracy of existing tracking algorithms in real-world applications.

Online commerce is increasingly relying on pricing algorithms. Using a network-based approach inspired by adversarial machine learning, a firm can learn the strategy of its competitors and use it to unilaterally increase all firms’ profits. This approach, termed as ‘adversarial collusion’, calls for new regulatory measures.

Deep learning can be used to predict molecular properties, but such methods usually need a large amount of data and are hard to generalize to different chemical spaces. To provide a useful primer for deep learning models models, Fang and colleagues use contrastive learning and a knowledge graph based on the Periodic Table and Wikipedia pages on chemical functional groups.

Fairness approaches in machine learning should involve more than an assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

We are trying to keep up with the torrent of developments and discussions in AI and language models since ChatGPT was unleashed on the world.

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.

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.

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.

Language models trained on proteins can help to predict functions from sequences but provide little insight into the underlying mechanisms. Vu and colleagues explain how extracting the underlying rules from a protein language model can make them interpretable and help explain biological mechanisms.

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.

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.

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.

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.

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.

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.

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.

In the next phase of space exploration, human crews will be sent on missions beyond the low Earth orbit. Artificial intelligence (AI) is expected to play a main role in autonomous biomonitoring, research and Earth-independent healthcare.