Amitava Datta

Fast Fourier Transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression

This paper introduces a training-free FFT-based method for efficient and interpretable epigenomic data compression, achieving high fidelity and speed compared to autoencoders.

A novel deep learning method using contrastive learning enables interpretable outputs from relationships between gene expression and histone modification

The paper introduces a deep learning method using contrastive learning to predict gene expression from histone modifications without arbitrary thresholds, showing consistent performance across datasets.

Machine learning reveals novel targets for both glioblastoma and osteosarcoma

This study uses machine learning to identify novel shared targets in glioblastoma and osteosarcoma, revealing C2ORF72 as a potential therapeutic and diagnostic target.

Classical statistical methods are powerful for the identification of novel targets for the survival of breast cancer patients

This study uses classical statistical methods to identify survival-related genes in breast cancer, finding two novel targets and validating results through bootstrapping.

memerna: Sparse RNA Folding Including Coaxial Stacking

The paper introduces memerna, a fast RNA folding algorithm that incorporates coaxial stacking and sparsity, achieving the fastest exact RNA folding implementation among popular packages supporting this feature.

Deep learning models for RNA secondary structure prediction (probably) do not generalise across families

The paper shows that deep learning models for RNA secondary structure prediction do not generalize across families, using rigorous cross-validation methods.