CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

Background: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non-periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. Conclusions: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time

Visualization and Analysis of Transformer Attention

The capability to select the relevant portion of the input is a key feature to limit the sensory input and focus on the most informative collected part. The transformer architecture is among the most performing deep neural network architectures due to the attention mechanism. The attention allows us to spot relevant connections between portions of the images and highlight these connections. Since the model is complex, it is not easy to determine which are these connections and the important areas. We discuss a technique to show these areas and highlight the regions most relevant for label attribution

Fuzzy Clustering of Histopathological Images Using Deep Learning Embeddings

Metric learning is a machine learning approach that aims to learn a new distance metric by increasing (reducing) the similarity of examples belonging to the same (different) classes. The output of these approaches are embeddings, where the input data are mapped to improve a crisp or fuzzy classification process. The deep metric learning approaches regard metric learning, implemented by using deep neural networks. Such models have the advantage to discover very representative nonlinear embeddings. In this work, we propose a triplet network deep metric learning approach, based on ResNet50, to find a representative embedding for the unsupervised fuzzy classification of benign and malignant histopathological images of breast cancer tissues. Experiments computed on the BreakHis benchmark dataset, using Fuzzy C-Means Clustering, show the benefit of using very low dimensional embeddings found by the deep metric learning approach

An Online Multilingual Medical Vocabulary/Thesaurus/Dictionary (MED-VTD) for Facilitating Understanding of Medical Texts

Medical texts (e.g., reports and medicine leaflets) are usually written by professionals (physicians, medical researchers, etc.) who use their own language and communication style. On the other hand, they are often read by health consumers or other medical professionals who do not have the same vocabularies and can have difficulties in text comprehension. Thus, to help a generic user in understanding a medical text, it would be desirable to have an online medical vocabulary/thesaurus/dictionary that he/she can easily look for finding the plain equivalent of any medical (technical) term and a definition of the term with the same kind of language. In this work, we present an online multilingual medical vocabulary/thesaurus/dictionary (MED-VTD) that helps the generic user in understanding any medical text written either in English or in Italian. To this end, we have built an integrated system that uses medical vocabularies for creating a list of medical (technical) terms, consumer health vocabularies (CHVs) for translating the technical terms into their consumer equivalents and consumer dictionaries for finding supplementary information on the terms. MED-VTD contains both English and Italian resources so that a translation of terms between the two languages can also be automatically performed. Moreover, other languages can easily be added providing that the related vocabularies, thesauri and dictionaries are available

A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes

The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 by of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time

Targeted sequencing of BRAF by MinION in archival Formalin-Fixed Paraffin-Embedded specimens allows to discriminate between Hairy Cell Leukemia and Hair Cell Leukemia Variant

Targeted sequencing of BRAF by MinION in archival Formalin-Fixed Paraffin-Embedded specimens allows to discriminate between Hairy Cell Leukemia and Hair Cell Leukemia Varian

Associations between Notch-2, Akt-1 and HER2/neu expression in invasive human breast cancer: a tissue microarray immunophenotypic analysis on 98 patients.

Objective: We aimed to investigate the existence of associations between well-established and newly recognized biological and phenotypic features of breast cancer involved in tumor progression and prognosis. Methods: Ninety-eight cases of invasive breast cancer were assessed for the immunohistochemical expression of estrogen and progesterone receptors, Ki-67, HER2, Akt-1, and Notch-2, using the tissue microarray technique. Data regarding tumor histotype, histological grade, tumor size and lymph node status were collected for each patient and included in the analysis. Results: Several significant associations between histological and/or immunophenotypic features came from the analysis of our data. Positive associations were observed between estrogen and progesterone receptors, tumor grade and proliferation index, tumor grade and HER2, Akt-1 and estrogen receptors, and Notch-2 and HER2. Inverse associations were noted between hormone receptors and tumor grade, hormone receptors and HER2, Akt-1 and tumor grade, and Akt-1 and nodal invasion. Conclusions: Our results, showing the existence of a number of estrogen receptor-positive tumors with Akt-1 expression, better degree of differentiation, and no lymph node involvement, along with the presence of HER2- positive tumors with strong Notch-2 expression, support the role of Notch and Akt in breast cancer progression and suggest that they may also represent new appealing therapeutic targets

Dataset for studying Atomic Learned Indexes

This repository stores the datsets used for the github named "A Benchmarking Platform for Atomic Learned Indexes

Alignment Free Dissimilarities for Nucleosome Classification

Epigenetic mechanisms such as nucleosome positioning, histone modications and DNA methylation play an important role in the regulation of cell type-specic gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have shown a role of DNA sequences in recruitment of epigenetic regulators. For this reason, the use of more suitable similarities or dissimilarity between DNA sequences could help in the context of epigenetic studies. In particular, alignment-free dissimilarities have already been successfully applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic proles. In this work, we focalize the study on the problem of nucleosome classification, providing a benchmark study of 6 alignment free dissimilarity measures between sequences, belonging to the categories of geometricbased, correlation-based, information-based and compression based. Their comparisons have been done versus an alignment based dissimilarity, by measuring the performance of several nearest neighbour classiers that incorporate each one the considered dissimilarities. Results computed on three dataset of nucleosome forming and inhibiting sequences, shows that among the alignment free dissimilarities, the geometric and correlation are the more suitable for the purpose of nucleosome classication, making them a more ecient alternative to the alignment-based similarity measures, which nevertheless are yet the preferred choice when dealing with sequence similarity measurement

A genetic algorithm for image segmentation

The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose a fitness function, based on the similarity between images, has been defined. The similarity is function of both the intensity and spatial position of pixels. Preliminary result, obtained using real images, show a good performance of the segmentation algorithm