Chakravarthi Kanduri

What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study

This study benchmarks generative models for adaptive immune receptor repertoires, identifying their strengths and limitations in capturing biological features.

Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML

This paper introduces immuneML, a framework for unsupervised machine learning in adaptive immune receptor repertoires, demonstrating its use in generative modeling, clustering, and dataset analysis.

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood

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.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

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.

BioNumPy: Fast and easy analysis of biological data with Python

BioNumPy enables efficient array programming on biological data using Python, outperforming vanilla Python and other packages while matching C/C++ speeds for common bioinformatics tasks.

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification

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: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

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.

Mind your gaps: Overlooking assembly gaps confounds statistical testing in genome analysis

The paper shows that ignoring assembly gaps in statistical tests for genomic feature co-localization can lead to false positives and inflated significance.

Genome Build Information Is An Essential Part Of Genomic Track Files

The paper highlights the importance of including genome build information in genomic track files and provides a tool to predict missing build versions.