Divyanshi Srivastava

Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression...

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of...

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of...

This work reports the measurement of impedance variations under various humidity conditions at frequency ranges between 100 Hz and 5 MHz. An electrochemical polymerization process has been used in the...

The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive...

Background: Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with...

Although Hox genes encode for conserved transcription factors (TFs), they are further divided into anterior, central and posterior groups based on their DNA-binding domain similarity. The posterior Hox group expanded...

Transcription factors (TFs) selectively bind distinct sets of sites in different cell types. Such cell type-specific binding specificity is expected to result from interplay between the TF's intrinsic sequence preferences,...