AIO2: Online Correction of Object Labels for Deep Learning With Incomplete Annotation in Remote Sensing Image Segmentation
IEEE Transactions on Geoscience and Remote Sensing Institute of Electrical and Electronics Engineers (IEEE) 62 (2024) 1-17
Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometre-Scale Models
(2024)
Multilabel-Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining
IEEE Transactions on Geoscience and Remote Sensing Institute of Electrical and Electronics Engineers (IEEE) 62 (2024) 1-16
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
ArXiv 2312.11176 (2023)
A Machine Learning Approach for Predicting Essentiality of Metabolic Genes
In: Braman, J.C. (eds) Synthetic Biology. Methods in Molecular Biology, vol 2760 (2024)
Abstract:
The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.