Various unsupervised, supervised, and semi-supervised methods employ machine learning (ML) for anomaly detection. Common shallow methods include z-score and Mahalanobis distance threshold-based classification, local outlier factor (LOF), K-nearest neighbors, and support vector machines. Deep neural networks such as variational auto-encoders (VAEs) can detect anomalies by learning to effectively reconstruct compressed representations of normal data while achieving only lossy reconstructions of anomalous data. Multiple ML methods can detect anomalies in biological data. For example, Michael-