Laura Martinez

TL;DR: This work generates primary data, creates bioinformatics tools and provides analysis to support the work of expert manual gene annotators and automated gene annotation pipelines to identify and characterise gene loci to the highest standard.

TL;DR: The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics as mentioned in this paper. But the annotation process does not support the creation of transcript structures and the determination of their function.

TL;DR: An in-depth investigation on the 2764 genes classified as coding by one or more sets of manual curators and not coding by others suggests that most are not under protein-like purifying selection and so are unlikely to code for functional proteins.

TL;DR: The platform combines the security of Intel Software Guarded eXtensions (SGX), transparency of blockchain technology, and verifiability of open algorithms and source codes to allow AI models to be trained on medical data securely.

TL;DR: This paper presents a meta-analyses of the proteomics results obtained in a pilot study at the Barcelona Supercomputing Center using the Higgs boson gene, a type of “supercomputer” designed to distinguish between “clean” and “dirty” DNA samples.