Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging | Nature Machine Intelligence
The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization and translatable clinical decision support systems. In this Perspective, the authors explore challenges unique to high-dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations involved in developing machine learning systems that better represent the high-dimensional nature of many imaging modalities. Furthermore, they argue that methods that attempt to address explainability, uncertainty and bias should be treated as core components of any clinical machine learning system.
MGEnrichment: A web application for microglia gene list enrichment analysis | PLOS Computational Biology
Gene expression analysis is becoming increasingly utilized in neuro-immunology research, and there is a growing need for non-programming scientists to be able to analyze their own genomic data. MGEnrichment is a web application developed both to disseminate to the community a curated database of microglia-relevant gene lists, and to allow non-programming scientists to easily conduct statistical enrichment analysis on their gene expression data.
Promotech: a general tool for bacterial promoter recognition | Genome Biology
Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. Here, researchers present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species.