Iman's Computational Biology Lab @WCM
Welcome to Iman Hajirasouliha Lab for Computational Biology 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 and embryology mages.
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 accepted manuscripts and preprints
6) Danko, Meleshko, Bezdan, Mason, Hajirasouliha. Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Mietagenomics Genome research 29 (1), 116-124 (open access)
5) Nima Habibzadeh Motlagh, Mahboobeh Jannesary, HamidReza Aboulkheyr, Pegah Khosravi , Olivier Elemento, Mehdi Totonchi, Iman Hajirasouliha Breast Cancer Histopathological Image Classification: A Deep Learning Approach 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (bioRxiv 242818)
4) Toosi, Moieni, Hajirasouliha. BAMSE: Bayesian model selection for tumor phylogeny inference among multiple samples (accepted in ICCABS 2017 and BMC)
3) Simone Ciccolella , Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova, Iman Hajirasouliha, Paola Bonizzoni Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations (bioRxiv 268243)
2) Malikic, Ciccolella, Ricketts, Hach, Haghshenas, Rahman, Rashidi Mehrabadi, Seidman, Hajirasouliha*, Sahinalp*. SCePhy - A Constraint Satisfaction Approach for Tumor Phylogeny Reconstruction via Integrative use f 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 →