Narsis A. Kiani

The paper identifies a CTCF-binding site that regulates MYC expression plasticity through a negative feed-forward loop involving CCAT1 eRNA and MYC protein feedback.

This study characterizes the gene regulatory network of early human B-cell development and identifies regulatory elements linked to B-cell acute lymphoblastic leukemia.

This study evaluates methods for inferring Jacobian matrices from single-cell data to model gene regulatory dynamics, finding that while some methods capture behavioral properties, they fail to reliably capture structural properties.

This paper introduces a method to predict perturbation responses in unseen cell types using cell-type-specific graphs, showing improved performance over existing methods.

This paper introduces a denoising diffusion model to predict and interpolate spatial transcriptomics data, enabling generative modeling of tissue slices.

The paper predicts missing links in the human Protein-Protein Interaction network using a transformer model that combines semantic and structural features.

This study evaluates how single-cell foundation models handle imbalanced cell-type data and finds that scGPT and scBERT outperform Geneformer, with random oversampling improving performance.

LEP-AD uses a Transformer protein language model to predict drug-target interactions, achieving state-of-the-art results across multiple datasets.

This study uses Ising models and algorithmic complexity to analyze fMRI data and finds LSD increases brain complexity and moves it further from criticality.

scAEGAN unifies single-cell genomics data by adversarial learning of latent space correspondences, outperforming existing methods in integration and prediction tasks.

This paper introduces LIBRA, a neural network framework for integrating and predicting across paired single-cell multi-omics data, showing improved clustering resolution and predictive power.

scMomentum infers cell-type-specific regulatory networks and energy landscapes from single-cell transcriptomic data without needing temporal or perturbation experiments.

The paper introduces Nodewalk, a highly sensitive method to analyze chromatin networks, revealing that enhancers around MYC operate in a stochastic, mutually exclusive manner, impacting cancer cell fitness.

The paper introduces a new framework for causal discovery and system reprogramming using algorithmic information theory, applied to biological systems like E. coli and cell differentiation networks.