Khalique Newaz

Aberrant splicing in human cancer has a large-scale functional impact on transcription factors

The paper identifies 2,118 aberrant splicing events affecting 718 transcription factors across 14 cancer types, showing functional perturbations in TFs due to cancer-specific alternative splicing.

Transcription factor prediction using protein 3Dsecondary structures

The paper introduces a deep learning method, StrucTFactor, that uses 3D protein structures to predict transcription factors, outperforming existing methods by 17% in Matthews correlation coefficient.

Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer

The paper identifies prognostic exon-exon interactions in cancer by analyzing RNA-seq and 3D protein structures, showing their association with patient survival.

The power and limits of predicting exon-exon interactions using protein 3D structures

The paper evaluates existing computational methods for predicting exon-exon interactions using protein structures and finds they can achieve moderate accuracy for this task.