Gain insights into TopLapGBT's performance across mutation regions and types, shedding light on protein engineering advancements.

Discover how TopLapGBT, a cutting-edge model integrating structure-based and sequence-based features, revolutionizes protein solubility prediction.

Enhance your understanding of protein solubility prediction with the integration of Transformer and Persistent Laplacians.

The supporting information for this research includes supplementary tables, figures, and additional methods.

Access protein structures, source code, and computational resources to delve deeper into TopLabGBT's methodology.

Discover how TopLapGBT surpasses state-of-the-art methods, offering a nuanced approach and transformative insights for advancing molecular biology research

Explore spectral graph theory, simplicial complex, persistent Laplacian, and transformer features, unraveling the computational framework behind TopLabGBT.

Mutational signatures based on single base substitutions are defined using the mutated base and its two neighboring bases

Mutational signatures connect characteristic mutational patterns in the genome with biological processes that take place in the tumor tissues.

In this section, we perform validation and evaluation of our proposed models on several benchmark datasets. We develop two types of GGL-PPI models

We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning.

We used all signatures from version 3.0 of the COSMIC catalog, in particular the SBS signatures for human genome assembly GRCh38. OSMICv3 contains 67 mutational

The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance

In this representation, atoms with assigned labels are organized into subgraphs, and the colored edges between them represent atom-specific interactions.