Fig. 1: Overview of the pum6a framework and its evaluation on the MNIST bag dataset. | Nature Communications

Fig. 1: Overview of the pum6a framework and its evaluation on the MNIST bag dataset.

From: Decoding the m6A epitranscriptomic landscape for biotechnological applications using a direct RNA sequencing approach

Fig. 1: Overview of the pum6a framework and its evaluation on the MNIST bag dataset.The alternative text for this image may have been generated using AI.

a Schematic of the pum6a framework, highlighting its key components for multi-instance learning and m6A site detection. b Dot plot illustrating attention weights and instance probabilities in positive bags. Dot size corresponds to attention weight, and color intensity represents the probability of specific digit identification, with the model focusing on digits 9, 7, and 4. c Comparative ROC AUC performance metrics of pum6a versus baseline models at varying label frequencies. Left: Bag-wise ROC AUC; Right: Instance-wise ROC AUC, demonstrating pum6a’s superior adaptability across complex datasets.

Back to article page