Last year I dedicated three blogposts to mass spectrometry-based proteomics (MS-proteomics) data analysis: PEPTIDE SEARCH ENGINES THE PEPTIDE-SPECTRUM MATCH DATA LOSS AND WAYS TO CONTROL IT To continue with this subject, I now discuss how using deep learning algorithms to predict fragment (MS2) ion mass intensities increases the number of peptide sequences identified by a peptide search engine. From the handful of deep learning-based MS2 ion intensity predictors reported so far, I discuss...

In this blogpost I explain the architecture of DeepVariant (Google Brain team). Published in 2018, DeepVariant is a neural network-based variant caller that discovers genomic variants with 99.9% accuracy. A universal SNP and small-indel variant caller using deep neural networks   —2018 Unlike Bayesian-based variant callers (e.g., HaplotypeCaller), DeepVariant discovers SNPs and INDELs regardless of the sequencing technology used: Illumina short reads PacBio long reads Oxford Nanopore...

The deep learning architecture named Transformer first appeared in the literature 2017. You can check out the publication by clicking on this hyperlink: Attention Is All You Need — 2017 Conceived to overcome the limitations of the natural language processing models of the day, the Transformer architecture went from making text translation and next-word prediction more accurate, to inspiring the development of chatGPT (OpenAI). (GPT stands for Generative Pre-trained Transformer.) Language...