Hajirasouliha Lab @WCM
Welcome to IH Computational Genomics Lab at Weill Cornell Medicine
Research & Activities
We are passionate about developing new algorithms, machine learning and deep learning methods, and their applications to genomics, metagenomics and cancer research. Some of the current projects in the lab include characterizing human genomes and metagenomes sequenced by exciting new technologies, quantifying cancer evolution, study of tumor heterogeneity using genomics and digital pathology images.
View Research & Other Activities →
Developing algorithms for characterizing structural variations focused on complex and repetitive elements, rare variants and clinical relevant variants.
Quantifying cancer evolution in multiple samples and reconstruction of tumor lineage trees using novel computational methods. Also, building deep learning based classifiers to discriminate heterogeneous sample, using imaging data.
Advancing computational methods in metagenomics for the purpose of deconvolving mixtures, discovering variants and de novo assembly.
Most recent manuscripts
6) Danko, Meleshko, Bezdan, Mason, Hajirasouliha. Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Mietagenomics (bioRxiv 217869)
5) Khosravi, Kazemi, Imielinski, Elemento*, Hajirasouliha*. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images EBioMedicine
4) Toosi, Moieni, Hajirasouliha. BAMSE: Bayesian model selection for tumor phylogeny inference among multiple samples (accepted in ICCABS 2017 and BMC)
3) Ricketts, Popic, Toosi, Hajirasouliha. Using LICHeE and BAMSE for reconstructing cancer phylogenetic trees (In press. Current Protocols in Bioinformatics)
2) Malikic, Ciccolella, Ricketts, Hach, Haghshenas, Rahman, Rashidi Mehrabadi, Seidman, Hajirasouliha*, Sahinalp*. SCePhy - A Constraint Satisfaction Approach for Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data
1) Meleshko, Mohimani, Hajirasouliha, Medema, Korobeynikov, Pevzner. Reconstructing Biosynthetic Gene Clusters From Assembly Graphs
Below you can see some highlights of our published work. Please also check publications page →