Genome mining for anti-CRISPR operons using machine learning.

MOTIVATION: Encoded by (pro-)viruses, anti-CRISPR (Acr) proteins inhibit the CRISPR-Cas immune system of their prokaryotic hosts. As a result, Acr proteins can be employed to develop more controllable CRISPR-Cas genome editing tools. Recent studies revealed that known acr genes often coexist with other acr genes and with phage structural genes within the same operon. For example, we found that 47 of 98 known acr genes (or their homologs) co-exist in the same operons. None of the current Acr prediction tools have considered this important genomic context feature. We have developed a new software tool AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known acr genes and their homologs. RESULTS: AOminer is the first machine learning based tool focused on the discovery of Acr operons (AOs). A two-state HMM (hidden Markov model) was trained to learn the conserved genomic context of operons that contain known acr genes or their homologs, and the learnt features could distinguish AOs and non-AOs. AOminer allows automated mining for potential AOs from query genomes or operons. AOminer outperformed all existing Acr prediction tools with an accuracy = 0.85. AOminer will facilitate the discovery of novel anti-CRISPR operons. AVAILABILITY AND IMPLEMENTATION: The webserver is available at: http://aca.unl.edu/AOminer/AOminer_APP/. The python program is at: https://github.com/boweny920/AOminer.

Enhancing Interpretability in Medical Image Classification by Integrating Formal Concept Analysis with Convolutional Neural Networks.

In this study, we present a novel approach to enhancing the interpretability of medical image classification by integrating formal concept analysis (FCA) with convolutional neural networks (CNNs). While CNNs are increasingly applied in medical diagnoses, understanding their decision-making remains a challenge. Although visualization techniques like saliency maps offer insights into CNNs' decision-making for individual images, they do not explicitly establish a relationship between the high-level features learned by CNNs and the class labels across entire dataset. To bridge this gap, we leverage the FCA framework as an image classification model, presenting a novel method for understanding the relationship between abstract features and class labels in medical imaging. Building on our previous work, which applied this method to the MNIST handwritten image dataset and demonstrated that the performance is comparable to CNNs, we extend our approach and evaluation to histopathological image datasets, including Warwick-QU and BreakHIS. Our results show that the FCA-based classifier offers comparable accuracy to deep neural classifiers while providing transparency into the classification process, an important factor in clinical decision-making.