Applying interpretable machine learning in computational biology—pitfalls, recommendations and opportunities for new developments
Recent advances in machine learning have enabled the development of next-generation
predictive models for complex computational biology problems, thereby spurring the use of …
predictive models for complex computational biology problems, thereby spurring the use of …
High-throughput methods in the discovery and study of biomaterials and materiobiology
The complex interaction of cells with biomaterials (ie, materiobiology) plays an increasingly
pivotal role in the development of novel implants, biomedical devices, and tissue …
pivotal role in the development of novel implants, biomedical devices, and tissue …
Image-based cell phenotyping with deep learning
A cell's phenotype is the culmination of several cellular processes through a complex
network of molecular interactions that ultimately result in a unique morphological signature …
network of molecular interactions that ultimately result in a unique morphological signature …
Enhancing scientific discoveries in molecular biology with deep generative models
Generative models provide a well‐established statistical framework for evaluating
uncertainty and deriving conclusions from large data sets especially in the presence of …
uncertainty and deriving conclusions from large data sets especially in the presence of …
Visualizing population structure with variational autoencoders
Dimensionality reduction is a common tool for visualization and inference of population
structure from genotypes, but popular methods either return too many dimensions for easy …
structure from genotypes, but popular methods either return too many dimensions for easy …
Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations
The identification of genetic and chemical perturbations with similar impacts on cell
morphology can elucidate compounds' mechanisms of action or novel regulators of genetic …
morphology can elucidate compounds' mechanisms of action or novel regulators of genetic …
From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability
Cell Painting assays generate morphological profiles that are versatile descriptors of
biological systems and have been used to predict in vitro and in vivo drug effects. However …
biological systems and have been used to predict in vitro and in vivo drug effects. However …
Contrastive learning of single-cell phenotypic representations for treatment classification
Learning robust representations to discriminate cell phenotypes based on microscopy
images is important for drug discovery. Drug development efforts typically analyse …
images is important for drug discovery. Drug development efforts typically analyse …
Machine learning in microscopy–insights, opportunities and challenges
Machine learning (ML) is transforming the field of image processing and analysis, from
automation of laborious tasks to open-ended exploration of visual patterns. This has striking …
automation of laborious tasks to open-ended exploration of visual patterns. This has striking …
Self-supervised learning of phenotypic representations from cell images with weak labels
JO Cross-Zamirski, G Williams, E Mouchet… - arXiv preprint arXiv …, 2022 - arxiv.org
We propose WS-DINO as a novel framework to use weak label information in learning
phenotypic representations from high-content fluorescent images of cells. Our model is …
phenotypic representations from high-content fluorescent images of cells. Our model is …