An interesting paper by Abrol et al. emphasizes the importance of representation learning for deep learning in comparison to feature-selected standard machine learning approaches through multiple classification and regression tasks. nature.com/articles/s41467 #SMILEJournalClub

This week's journal club paper by Schulz et al. compares the sample size scaling of linear/non-linear machine learning and deep learning using the UK Biobank dataset and the MNIST and Fashion-MNIST benchmark datasets. nature.com/articles/s4146… #SMILEJournalClub

This paper, "Meta-matching as a simple framework to translate phenotypic predictive models from big to small data", by He et al. exploits phenotypic correlations to improve prediction performance of new phenotypes in small-scale studies. nature.com/articles/s4159… #SMILEJournalClub

This week's #SMILEJournalClub, "The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions" by Littlejohns et al. revisits 100,000 participants with plans to expand the dataset with additional imaging modalities.

This paper, ncbi.nlm.nih.gov/pmc/articles/P…, by Groos et al. uses a deep learning method to predict cerebral palsy in infants from 9 to 18 weeks' corrected age. Their deep learning approach outperforms the clinically recommended general movement assessment (GMA) tool. #SMILEJournalClub

This paper, arxiv.org/abs/1902.07409, by Athey and Wager explores an extension of random forests called causal forests and its' ability to understand how treatment effects vary across different samples. #SMILEJournalClub

This paper, arxiv.org/abs/2111.14791, by Tang et al. introduces a self-supervised shifted window transformer for medical image analysis on a large computed tomography data set that is capable of extracting features at various resolutions from the whole image. #SMILEJournalClub