Università degli studi di Modena e Reggio Emilia

The Inferior Alveolar Nerve (IAN) is of main interest in the maxillofacial field, as an accurate localization of such nerve reduces the risks of injury during surgical procedures. Although recent literature has focused on developing novel deep learning techniques to produce accurate segmentation masks of the canal containing the IAN, there are still strong limitations due to the scarce amount of publicly available 3D maxillofacial datasets. In this paper, we present an improved version of a previously released tool, IACAT (Inferior Alveolar Canal Annotation Tool), today used by medical experts to produce 3D ground truth annotation. In addition, we release a new dataset, ToothFairy, which is part of the homonymous MICCAI2023 challenge hosted by the Grand-Challenge platform, as an extension of the previously released Maxillo dataset, which was the only publicly available. With ToothFairy, the number of annotations has been increased as well as the quality of existing data.

Background and objectives: In the latest years, the prediction of gene expression levels has been crucial due to its potential applications in the clinics. In this context, Xpresso and others methods based on Convolutional Neural Networks and Transformers were firstly proposed to this aim. However, all these methods embed data with a standard one-hot encoding algorithm, resulting in impressively sparse matrices. In addition, post-transcriptional regulation processes, which are of uttermost importance in the gene expression process, are not considered in the model.Methods: This paper presents Transformer DeepLncLoc, a novel method to predict the abundance of the mRNA (i.e., gene expression levels) by processing gene promoter sequences, managing the problem as a regression task. The model exploits a transformer-based architecture, introducing the DeepLncLoc method to perform the data embedding. Since DeepLncloc is based on word2vec algorithm, it avoids the sparse matrices problem.Results: Post-transcriptional information related to mRNA stability and transcription factors is included in the model, leading to significantly improved performances compared to the state-of-the-art works. Transformer DeepLncLoc reached 0.76 of R-2 evaluation metric compared to 0.74 of Xpresso.Conclusion: The Multi-Headed Attention mechanisms which characterizes the transformer methodology is suitable for modeling the interactions between DNA's locations, overcoming the recurrent models. Finally, the integration of the transcription factors data in the pipeline leads to impressive gains in predictive power. (C) 2022 Elsevier B.V. All rights reserved.