You can find a full publication list at Google Scholar.
Underline: trainees from Mostafavi lab; *: co-first authorship; $: co-senior authorship.
How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?
Cell-subtype specific effects of genetic variation in the aging and Alzheimer cortex
ExplaiNN: interpretable and transparent neural networks for genomics
Selected Published Articles
In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm
Cross-Linked Unified Embedding for cross-modality representation learning
Obtaining genetics insights from deep learning via explainable artificial intelligence
Mosaic loss of chromosome Y in aged human microglia
A practical guide to applying machine learning to infant EEG data
CEWAS: Cascading Epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants
Biologically relevant transfer learning improves transcription factor binding prediction
Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes
Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies
Deep learning decodes the principles of differential gene expression
Deep learning of immune cell differentiation
Deconvolving the contributions of cell-type heterogeneity on cortical gene expression
metPropagate: network-guided propagation of metabolomic information for prioritization of metabolic disease genes.
Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.
Somatic mosaicism of sex chromosomes in the blood and brain.
The cis-Regulatory Atlas of the Mouse Immune System.
A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease
An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome.
Allele-specific expression reveals interactions between genetic variation and environment
Parsing the interferon transcriptional network and its disease associations
Sharing and Specificity of Co-expression Networks across 35 Human Tissues
Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes
Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals
Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge
Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing
Using prior biological knowledge when constructing gene regulatory networks
Labeling nodes using three degrees of propagation
Predicting node characteristics from molecular networks
Fast integration of heterogeneous data sources for predicting gene function with limited annotation.
Using the Gene Ontology hierarchy when predicting gene function
GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function
Advancing our understanding of genome regulation via optimization of stem cell differentiation and interpretable deep learning
Robust methods for identifying cluster structure in single-cell RNA-sequecing data
Improving the estimation of Y chromosome loss using high-throughput sequencing
Understanding gene regulatory mechanisms of mouse immune cells using a convolutional neural network
Integration of genomic and metabolomic data for the prioritization of rare disease variants.
Molecular interpretation of genome-wide association studies using multiomics analysis.
Estimating cell type proportions in human cord blood samples from DNAm arrays.
Congratulations to Xinming for the acceptance of his CLUE paper at NeurIPS.
Congratulations to Mike for acceptance of his LoY paper, Genome Research.
CEWAS papper is out in PLOS Genetics
MLCB conference coming Nov 22-23
Two papers accepted for publication! Congratulations Gherman, and Nicasia.