Chris Chandler

TL;DR: DeepFRI as mentioned in this paper is a graph convolutional network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures, which scales to the size of current sequence repositories.

TL;DR: This work presents a deep learning Graph Convolutional Network (GCN) trained on sequence and structural data and evaluates it on ~40k proteins with known structures and functions from the Protein Data Bank (PDB).

TL;DR: It is shown that the structural space is continuous and largely saturated, highlighting the need for shifting the focus from obtaining structures to putting them into context, to transform all branches of biology, including a shift from sequence-based to sequence-structure-function based meta-omics analyses.

TL;DR: A new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling and deep learning-based functional annotations from DeepFRI is developed, contributing to novel understanding of the functional signature of the human gut microbiome in health and disease as well as guide future metagenomics studies.

TL;DR: Foote et al. as discussed by the authors evaluated co-occurrence and clonality patterns between frequently altered genes in mucinous appendiceal adenocarcinoma (MAAP) to establish five molecular subtypes of MAAP: RAS mutated-only, GNAS mutated, TP53 and none (all wild-type).