PlateUNLP is a semi-automatic software tool designed to assist in the processing of digitized spectral plate images. It automates key stages such as spectra detection, spectral extraction, wavelength calibration, and metadata generation. These tasks, traditionally performed manually, are significantly accelerated while still allowing user intervention when necessary. This work focuses on the integration of automation for the detection of spectra in images using deep learning techniques, as well as the extraction of one-dimensional spectra based on the image segments corresponding to each spectrum, and their subsequent wavelength calibration. The tool is being developed in collaboration with expert astronomers, demonstrating high reliability and practical utility in the recovery of historical astronomical data.

MOTIVATION: Large-scale cancer genome projects have generated genomic, transcriptomic, epigenomic and clinicopathological data from thousands of samples in almost every human tumor site. Although most omics data and their associated resources are publicly available, its full integration and interpretation to dissect the sources of gene expression modulation require specialized knowledge and software. RESULTS: We present Multiomix, an interactive cloud-based platform that allows biologists to identify genetic and epigenetic events associated with the transcriptional modulation of cancer-related genes through the analysis of multi-omics data available on public functional genomic databases or user-uploaded datasets. Multiomix consists of an integrated set of functions, pipelines and a graphical user interface that allows retrieval, aggregation, analysis and visualization of different omics data sources. After the user provides the data to be analyzed, Multiomix identifies all significant correlations between mRNAs and non-mRNA genomics features (e.g. miRNA, DNA methylation and CNV) across the genome, the predicted sequence-based interactions (e.g. miRNA-mRNA) and their associated prognostic values. AVAILABILITY AND IMPLEMENTATION: Multiomix is available at https://www.multiomix.org. The source code is freely available at https://github.com/omics-datascience/multiomix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

As the presence of artificial intelligence has increased in a variety of different areas, the use of machine learning and deep learning techniques for creative purposes has also risen significantly in recent years. Works of this kind within the area of natural language processing (NLP) are typically neural models used for fiction or lyrics generation. Those works are in most cases in English and adapting them to other languages is not feasible. In this work, we develop a Spanish text generator system for the rap sub-genre known as freestyle. Freestyle songs present unique challenges for text generation given that performers compete with one another in a lyric improvisation contest. Given the low availability of freestyle text, especially in Spanish, we collected two separate datasets, one with freestyle lyrics and the other, larger, with rap lyrics, which are more readily available. The rap dataset can be used for pretraining, and the freestyle dataset for finetuning on the generation task. Furthermore, we design a neural network-based generation model that takes into account both the structure of freestyle and the low data availability. The model was able to generate realistic freestyle verses in Spanish.