Jordan Anaya

Characterization of non-monotonic relationships between tumor mutational burden and clinical outcomes

This study introduces a two-cutoff approach and neural networks to model non-monotonic relationships between tumor mutational burden and clinical outcomes, finding that non-monotonic relationships can exist.

Aggregation Tool for Genomic Concepts (ATGC): A deep learning framework for somatic mutations and other sparse genomic measures

The paper introduces a deep learning framework for genomic data that aggregates and predicts somatic mutations and other sparse genomic measures using attention-based multiple instance learning.

Probabilistic mixture models improve calibration of panel-derived tumor mutational burden in the context of both tumor-normal and tumor-only sequencing

This paper introduces probabilistic mixture models to calibrate panel-derived tumor mutational burden (TMB) to exomic TMB, showing improved accuracy over linear regression.

Read depth correction for somatic mutations

The paper estimates the true mutation VAF distributions from low-depth sequencing by comparing with high-depth data, finding TCGA MC3 underreports mutations by 17%.