Geoffrey Brian Golding

Ankh-score produces better sequence alignments than AlphaFold3

Ankh-score improves sequence alignment over AlphaFold3 structural alignments and traditional BLOSUM methods on benchmark datasets.

Protein Embeddings and Local Alignments

The paper introduces an embedding-based scoring method for protein sequence alignment, showing it outperforms BLOSUM matrices and existing tools.

C3PI: Component Puzzle Protein-Protein Interaction Prediction

C3PI is a deep learning framework for predicting protein-protein interactions that outperforms existing methods, especially on leakage-free datasets.

Scoring alignments by embedding vector similarity

The paper introduces a new amino acid similarity scoring method, E-score, based on contextual embeddings from deep learning, showing improved alignment quality compared to BLOSUM matrices.

Seq-InSite: sequence supersedes structure for protein interaction site prediction

Seq-InSite predicts protein interaction sites using sequence data, outperforming sequence-based models and matching structure-based predictors.

Reply to Barton et al: signatures of natural selection during the Black Death

The paper addresses statistical concerns about detecting natural selection during the Black Death using ancient genomic data, reaffirming findings related to immune loci and the ERAP2 variant.

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta-Diversity in Medically Relevant 16S Amplicon Sequencing Data

The paper uses decision tree ensembles with multivariate splits to analyze 16S amplicon sequencing data, improving the analysis of microbial community differences in Crohn's Disease and colorectal cancer patients.

DELPHI: accurate deep ensemble model for protein interaction sites prediction

DELPHI is a deep learning model that improves the accuracy of predicting protein-protein interaction sites by using an ensemble structure and novel features.