Maria Chernigovskaya

The paper identifies TCR subsequence motifs associated with type 1 diabetes using deep learning and shows their enrichment in T1D individuals based on HLA risk alleles.

This study benchmarks mass spectrometry-based antibody sequencing methods, revealing biases related to sequence, concentration, protease, and bioinformatics tools.

This study shows that the composition of training data, especially negative examples, affects machine learning model performance and biological rule discovery in antibody-antigen binding.

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

The paper analyzes the sequence distribution of epitope-specific TCR repertoires and shows how this affects machine learning model performance.

The paper quantifies the plasticity of antibody developability by analyzing sequence and structure-based parameters in natural and human-engineered antibodies.

The paper evaluates ML models for adaptive immune receptor repertoire classification, finding that simple methods like L1-penalized logistic regression perform well even with rare immune signals.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.