{"id":975,"date":"2025-01-04T13:00:22","date_gmt":"2025-01-04T13:00:22","guid":{"rendered":"https:\/\/mellekastaging.com\/worldbrainmapping\/?post_type=sbmt-leadership&#038;p=975"},"modified":"2025-01-04T13:03:55","modified_gmt":"2025-01-04T13:03:55","slug":"pegah-khosravi","status":"publish","type":"sbmt-leadership","link":"https:\/\/mellekastaging.com\/worldbrainmapping\/sbmt-leadership\/pegah-khosravi\/","title":{"rendered":"Pegah Khosravi"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">About<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">My research focuses on the development of machine learning techniques and AI-based models for the innovation of medical data analysis. I am an Assistant Professor at New York City College of Technology (City Tech) and teach Biomedical Data Analytics. Also, I serve as Deputy Editor of the Journal of Magnetic Resonance Imaging (JMRI).<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Education:<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Ph.D., Bioinformatics, 2014<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran<\/li>\n\n\n\n<li>Thesis Title: &#8220;<em>Dynamical analysis of cellular networks via studying interaction and hub types<\/em>&#8220;<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Courses Taught at City Tech:<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>BIO 3450: Biomedical Data Analysis I<\/li>\n\n\n\n<li>BIO 4450: Biomedical Data Analysis II<\/li>\n\n\n\n<li>BIO 4550: Biomedical Informatics Colloquium<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Research Interests:<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Artificial Intelligence<\/li>\n\n\n\n<li>Bioinformatics\/Computational Biology<\/li>\n\n\n\n<li>Cancer Research<\/li>\n\n\n\n<li>Deep Learning<\/li>\n\n\n\n<li>Machine Learning<\/li>\n\n\n\n<li>Medical Data Analysis<\/li>\n\n\n\n<li>Pathology and Radiology Image Analysis<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Current and Previous Appointments:<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Assistant Professor (2022-present)<\/strong><br>Department of Biological Sciences, New York City College of Technology, CUNY, NY, USA.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Deputy Editor (2020-present)<\/strong><br>Journal of Magnetic Resonance Imaging (JMRI): New Developments and Future Direction, NY, USA.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Sr II Computational Biologist (2020-2022)<\/strong><br>Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY, USA.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Postdoctoral Associate (2017-2020)<\/strong><br>Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, NY, USA.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Postdoctoral Research Fellow (2014-2017)<\/strong><br>School of Biological Sciences of Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Visiting Researcher (2012-2013)<\/strong><br>Donnelly Center for Cellular and Biomolecular Research, Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Representative Publications:<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Khosravi P.<\/strong>, Lysandrou, M., Eljalby, M., Brendel, M., Li, Q., Kazemi, E., Zisimopoulos, P., Sigaras, A., Barnes, J., Ricketts, C., Meleshko, D., Yat, A., McClure, T. D., Robinson, B. D., Sboner, A., Elemento, O., Chughtai, B., Hajirasouliha, I., A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology\u2013Radiology Fusion, Journal of Magnetic Resonance Imaging, 54 (2021).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Boehm K. M.,&nbsp;<strong>Khosravi P.<\/strong>, Vanguri R., Gao J., Shah P. S., Harnessing multimodal data integration to advance precision oncology, Nature Reviews Cancer (2021), 22: 114-126.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Xu, Z., Verma, A., Naveed, U., Bakhoum, S.,&nbsp;<strong>Khosravi P.<\/strong>, Elemento, O., Using Histopathology Images to Predict Chromosomal Instability in Breast Cancer: A Deep Learning Approach, Iscience (2021), 3;24(5).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Asgari, Y.,&nbsp;<strong>Khosravi P.<\/strong>, Flux variability analysis reveals a tragedy of commons in cancer cells, SN Applied Sciences (2020), 2:1966.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Khosravi, P.<\/strong>, Kazemi, Zhan, Q., Toschi, M., Malmsten, J., Cooper, L., Hickman, C., Meseguer, M., Rosenwaks, Z., Elemento, O., Hajirasouliha I., Deep Learning Enables Robust Assessment and Selection of Human Blastocysts after In-vitro Fertilization, npj digital medicine-Nature (2019), 4;2:21.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Khosravi, P.<\/strong>, Kazemi, E., Imielinski, M., Elemento, O., Hajirasouliha I., Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images, EBioMedicine (2018), 27: 317-328.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Habibi, M.,&nbsp;<strong>Khosravi, P.<\/strong>, Disruption of the Protein Complexes from Weighted Complex Networks, IEEE\/ACM transactions on computational biology and bioinformatics (2018).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Asgari, Y.,&nbsp;<strong>Khosravi, P.<\/strong>, Zabihinpour, Z., Habibi, M., Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis, Integrative Biology (2018), 10:113-120.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aghdam, R., Baghfalaki, T.,&nbsp;<strong>Khosravi, P.<\/strong>, Ansari, E. S., The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer, Genomics, Proteomics &amp; Bioinformatics (2017), 15: 396-404.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Emamjomeh A., Robat E. S., Zahiri J., Solouki M.,&nbsp;<strong>Khosravi P.<\/strong>, Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data, Plant Biotechnology Reports (2017), 1:6.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aghdam, R.,&nbsp;<strong>Khosravi, P.<\/strong>, Ansari, E. S., Comparative Analysis of Gene Regulatory Networks Concepts in Normal and Cancer Groups, Bioinformatics and Biocomputational Research (2016), 1: 42-45.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Khosravi P.<\/strong>, Gazestani V.H., Pirhaji L., Law B., Sadeghi M., Bader G., Goliaei B., Inferring interaction type in gene regulatory networks using co-expression data, Algorithm for molecular Biology (2015), 10:23.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Khosravi P.<\/strong>, Gazestani V.H., Asgari Y., Law B., Sadeghi M., Goliaei B., Network-based approach reveals Y chromosome influences prostate cancer susceptibility, Computers in Biology and Medicine (2014), 54:24-31.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Hosseinpour B., Bakhtiarizadeh M.R.,&nbsp;<strong>Khosravi P.<\/strong>, Ebrahimie E., Predicting distinct organization of transcription factor binding sites on the promoter regions; a new genome-based approach to expand human embryonic stem cell regulatory network. Gene (2013), 531:212-9.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">My Google Scholar:<\/h4>\n\n\n\n<figure class=\"wp-block-embed\"><div class=\"wp-block-embed__wrapper\">\nhttps:\/\/scholar.google.com\/citations?user=lHM6ZCwAAAAJ&#038;hl=en&#038;oi=ao\n<\/div><\/figure>\n","protected":false},"author":4,"featured_media":979,"template":"","archive-award-gala":[],"active-gfc-awards-gala":[],"sbmt_staff":[],"leadership-awards":[],"leadership-group":[],"scientific-committee":[13,33],"class_list":["post-975","sbmt-leadership","type-sbmt-leadership","status-publish","has-post-thumbnail","hentry","scientific-committee-active","scientific-committee-epilepsy-eeg-meg-and-neuroradiology"],"_links":{"self":[{"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/sbmt-leadership\/975","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/sbmt-leadership"}],"about":[{"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/types\/sbmt-leadership"}],"author":[{"embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/users\/4"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/media\/979"}],"wp:attachment":[{"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/media?parent=975"}],"wp:term":[{"taxonomy":"archive-award-gala","embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/archive-award-gala?post=975"},{"taxonomy":"active-gfc-awards-gala","embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/active-gfc-awards-gala?post=975"},{"taxonomy":"sbmt_staff","embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/sbmt_staff?post=975"},{"taxonomy":"leadership-awards","embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/leadership-awards?post=975"},{"taxonomy":"leadership-group","embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/leadership-group?post=975"},{"taxonomy":"scientific-committee","embeddable":true,"href":"https:\/\/mellekastaging.com\/worldbrainmapping\/wp-json\/wp\/v2\/scientific-committee?post=975"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}