Research interests:
- Computational biology
- Statistical and computational genomics
- Machine learning
- AI/ML in healthcare
Below is a partial list of my publications. For a full list see my CV or my Google Scholar profile.
PhD dissertation:
Rahmani, E. Capturing Hidden Signals From High-Dimensional Data and Applications to Genomics. University of California, Los Angeles. 2020. [pdf]
Selected Journal Publications:
Rahmani E*, Jew B*, Halperin E. The effect of model directionality on cell-type-specific differential DNA methylation analysis. Frontiers in Bioinformatics. 2022 (* - joint first authorship)} [link]
Alvarez M*, Rahmani E*, Jew B, Garske KM, Miao Z, Benhammou JN, Ye CJ, Pisegna JR, Pietilainen KH, Halperin E, Pajukanta P. Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM. Scientific Reports. 2020. (* - joint first authorship) [pdf]
Rahmani E, Schweiger R, Rhead B, Criswell LA, Barcellos LF, Eskin E, Rosset S, Sankararaman S, Halperin E. Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology. Nature Communications. 2019. [pdf]
Rahmani E, Schweiger R, Shenhav L, Wingert T, Hofer I, Gabel E, Eskin E, Halperin E. BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference. Genome Biology. 2018. [pdf]
Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, Oh S, Burchard EG, Eskin E, Zou J, Halperin E. Correcting for cell-type heterogeneity in DNA methylation: a comprehensive evaluation. Nature Methods. 2017. [link]
Rahmani E, Yedidim R, Shenhav L, Schweiger R, Weissbrod O, Zaitlen N, Halperin E. GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data. Bioinformatics. 2017. [link]
Rahmani E, Shenhav L, Schweiger R, Yousefi P, Huen K, Eskenazi B, Eng C, Huntsman S, Hu D, Galanter J, Oh SS et al. Genome-wide methylation data mirror ancestry information. Epigenetics & Chromatin. 2017. [pdf]
Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, Oh S, Burchard EG, Eskin E, Zou J, Halperin E. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nature Methods. 2016. [pdf]
Selected Conference Contributions:
Rahmani E*, Chen Z*, Halperin E. A unified model for cell-type resolution genomics from heterogeneous omics data. (* - joint first authorship) RECOMB-Genetics. 2023.
Gorla A, Sankararaman S, Burchard E, Flint J, Zaitlen N, Rahmani E. Phenotypic subtyping via contrastive learning. RECOMB. 2023.
Rahmani E, Jordan MI, Yosef N. Identifying systematic variation at the single-cell level by leveraging low-resolution population-level data. RECOMB. 2022.
Rahmani E, Halperin E, Jordan MI, Yosef N. Identifying systematic variation in gene-gene interactions at the single-cell level. ICML CompBio workshop. 2021.
Rahmani E, Schweiger R, Rosset S, Sankararaman S, Halperin E. Tensor Composition Analysis Detects Cell-Type Specific Associations in Epigenetic Studies. RECOMB. 2018.
Rahmani E, Schweiger R, Shenhav L, Eskin E, Halperin E. A Bayesian framework for estimating cell type composition from DNA methylation without the need for methylation reference. RECOMB. 2017.
Rahmani E, Schweiger R, Rosset S, Sankararaman S, Halperin E. Extracting 3D Information from 2D Data and Application for Detecting Cell-Type Specific Associations in Genomic Studies. NIPS MLCB Workshop. 2017.