Data quality issues have been acknowledged as one of the greatest obstacles in medical artificial intelligence research. Here, we present DeepFundus, which employs deep learning techniques to perform multidimensional classification of fundus image qu...
DeepContact is a deep learning software for high-throughput quantification of membrane contact site (MCS) in 2D electron microscopy images. This protocol will guide users through incorporating available DeepContact models in Amira's artificial intell...
Cellular image analysis is an important method for microbiologists to identify and study microbes. Here, we present a knowledge-integrated deep learning framework for cellular image analysis, using three tasks as examples: classification, detection, ...
Here, we present a protocol for multivariate quantitative-image-based cytometry (QIBC) analysis by fluorescence microscopy of asynchronous adherent cells. We describe steps for the preparation, treatment, and fixation of cells, sample staining, and i...
The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system...
Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capa...
Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific bioti...
We recently developed a robotic human vaping mimetic real-time particle analyzer (HUMITIPAA) to evaluate the impact of change in chemical constituents and breathing profiles of electronic cigarettes (ECs) on potential pulmonary toxicity. Here, we des...
Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protocol, we describe steps to execute Python scripts for a chemical dataset from the original publication...
Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer ty...