Aniketh Janardhan Reddy

A unified framework for quantitative splicing and transcript prediction

This paper introduces SpliceAI2, a deep learning model that improves identification and quantification of splice-altering variants and their effects on transcripts and disease.

Fine-tuning sequence-to-expression models onpersonal genome and transcriptome data

This study fine-tunes sequence-to-expression models on personal genome and transcriptome data, showing improved performance on seen genes but not on unseen genes.

Sequence models conditioned on splicing factor expression predict splicing in unseen tissues

Splice Ninja predicts tissue-specific splicing events using splicing factor expression, achieving accurate results on unseen tissues.

ML-driven design of 3' UTRs for mRNA stability

The paper designs stable mRNA 3' UTRs using machine learning and experimental validation, achieving higher protein production in mouse models.

DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements

DNA-Diffusion uses diffusion models to generate cell type-specific regulatory DNA sequences that modulate chromatin accessibility and gene expression.

Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization

The paper introduces a framework for designing cell-type-specific promoters using model-based optimization, demonstrating improved specificity in blood cancer cell lines.

Strategies for effectively modelling promoter-driven gene expression using transfer learning

This paper develops transfer learning strategies to model promoter-driven gene expression in understudied cell types, showing improvements in performance with pretraining and transfer.

Can fMRI reveal the representation of syntactic structure in the brain?

This study investigates whether fMRI can reveal brain representations of syntactic structure by modeling brain activity using syntactic features from natural text.