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Haitham A. Elmarakeby, Ph.D.

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Biography
Virginia Tech , Blacksburg, VAPhD05/2017Computer Science
Cairo University, Cairo, EgyptMSc2010Computer Engineering
Al-Azhar University, Cairo, EgyptBSc2004Computer Engineering

Overview
I am an instructor at Dana-Farber Cancer Institute/Harvard Medical School and an affiliate Research Fellow at the Broad Institute of MIT and Harvard. I am trained in Computer Science and Machine Learning. My research is focused on developing computational models to tackle pressing health problems. I have expertise in machine learning and data mining with a special interest in applying cutting-edge computational techniques to better understand progression and drug resistance in cancer. I am interested in building interpretable models to discover novel biomarkers of clinical and biological outcomes. My machine learning models integrate multiple data modalities such as gene expression, mutations, copy number variations, and methylations to accurately predict outcomes in real patients and cell lines models.

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Bibliographic
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
  1. Elmarakeby HA, Hwang J, Arafeh R, Crowdis J, Gang S, Liu D, AlDubayan SH, Salari K, Kregel S, Richter C, Arnoff TE, Park J, Hahn WC, Van Allen EM. Biologically informed deep neural network for prostate cancer discovery. Nature. 2021 10; 598(7880):348-352. PMID: 34552244.
    Citations: 1     Fields:    
  2. Kehl KL, Groha S, Lepisto EM, Elmarakeby H, Lindsay J, Gusev A, Van Allen EM, Hassett MJ, Schrag D. Clinical Inflection Point Detection on the Basis of EHR Data to Identify Clinical Trial-Ready Patients With Cancer. JCO Clin Cancer Inform. 2021 06; 5:622-630. PMID: 34097438.
    Citations:    Fields:    Translation:Humans
  3. Eid FE, Elmarakeby HA, Chan YA, Fornelos N, ElHefnawi M, Van Allen EM, Heath LS, Lage K. Systematic auditing is essential to debiasing machine learning in biology. Commun Biol. 2021 02 10; 4(1):183. PMID: 33568741.
    Citations: 1     Translation:HumansAnimalsCells
  4. AlDubayan SH, Conway JR, Camp SY, Witkowski L, Kofman E, Reardon B, Han S, Moore N, Elmarakeby H, Salari K, Choudhry H, Al-Rubaish AM, Al-Sulaiman AA, Al-Ali AK, Taylor-Weiner A, Van Allen EM. Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma. JAMA. 2020 11 17; 324(19):1957-1969. PMID: 33201204.
    Citations: 1     Fields:    Translation:Humans
  5. Kehl KL, Xu W, Lepisto E, Elmarakeby H, Hassett MJ, Van Allen EM, Johnson BE, Schrag D. Natural Language Processing to Ascertain Cancer Outcomes From Medical Oncologist Notes. JCO Clin Cancer Inform. 2020 08; 4:680-690. PMID: 32755459.
    Citations: 4     Fields:    Translation:Humans
  6. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Arnon L, Zimmer L, Gutzmer R, Satzger I, Loquai C, Grabbe S, Vokes N, Margolis CA, Conway J, He MX, Elmarakeby H, Dietlein F, Miao D, Tracy A, Gogas H, Goldinger SM, Utikal J, Blank CU, Rauschenberg R, von Bubnoff D, Krackhardt A, Weide B, Haferkamp S, Kiecker F, Izar B, Garraway L, Regev A, Flaherty K, Paschen A, Van Allen EM, Schadendorf D. Author Correction: Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2020 Jul; 26(7):1147. PMID: 32561875.
    Citations: 2     Fields:    
  7. Rafiei S, Fitzpatrick K, Liu D, Cai MY, Elmarakeby HA, Park J, Ricker C, Kochupurakkal BS, Choudhury AD, Hahn WC, Balk SP, Hwang JH, Van Allen EM, Mouw KW. ATM Loss Confers Greater Sensitivity to ATR Inhibition Than PARP Inhibition in Prostate Cancer. Cancer Res. 2020 06 01; 80(11):2094-2100. PMID: 32127357.
    Citations: 13     Fields:    Translation:HumansCells
  8. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Arnon L, Zimmer L, Gutzmer R, Satzger I, Loquai C, Grabbe S, Vokes N, Margolis CA, Conway J, He MX, Elmarakeby H, Dietlein F, Miao D, Tracy A, Gogas H, Goldinger SM, Utikal J, Blank CU, Rauschenberg R, von Bubnoff D, Krackhardt A, Weide B, Haferkamp S, Kiecker F, Izar B, Garraway L, Regev A, Flaherty K, Paschen A, Van Allen EM, Schadendorf D. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019 12; 25(12):1916-1927. PMID: 31792460.
    Citations: 92     Fields:    Translation:HumansCells
  9. Vokes NI, Liu D, Ricciuti B, Jimenez-Aguilar E, Rizvi H, Dietlein F, He MX, Margolis CA, Elmarakeby HA, Girshman J, Adeni A, Sanchez-Vega F, Schultz N, Dahlberg S, Zehir A, Jänne PA, Nishino M, Umeton R, Sholl LM, Van Allen EM, Hellmann MD, Awad MM. Harmonization of Tumor Mutational Burden Quantification and Association With Response to Immune Checkpoint Blockade in Non-Small-Cell Lung Cancer. JCO Precis Oncol. 2019; 3. PMID: 31832578.
    Citations: 13     
  10. Kehl KL, Elmarakeby H, Nishino M, Van Allen EM, Lepisto EM, Hassett MJ, Johnson BE, Schrag D. Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports. JAMA Oncol. 2019 Oct 01; 5(10):1421-1429. PMID: 31343664.
    Citations: 23     Fields:    
  11. Conway JR, Kofman E, Mo SS, Elmarakeby H, Van Allen E. Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med. 2018 11 29; 10(1):93. PMID: 30497521.
    Citations: 35     Fields:    Translation:HumansAnimalsCells
  12. Elmarakeby H, Arefiyan M, Myers E, Li S, Grene R, Heath LS. Beacon Editor: Capturing Signal Transduction Pathways Using the Systems Biology Graphical Notation Activity Flow Language. J Comput Biol. 2017 Dec; 24(12):1226-1229. PMID: 28846457.
    Citations:    Fields:    Translation:HumansCells
  13. Ni Y, Aghamirzaie D, Elmarakeby H, Collakova E, Li S, Grene R, Heath LS. A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis. Front Plant Sci. 2016; 7:1936. PMID: 28066488.
    Citations: 9     
  14. Vinatzer BA, Weisberg AJ, Monteil CL, Elmarakeby HA, Sheppard SK, Heath LS. A Proposal for a Genome Similarity-Based Taxonomy for Plant-Pathogenic Bacteria that Is Sufficiently Precise to Reflect Phylogeny, Host Range, and Outbreak Affiliation Applied to Pseudomonas syringae sensu lato as a Proof of Concept. Phytopathology. 2017 Jan; 107(1):18-28. PMID: 27552324.
    Citations: 5     Fields:    Translation:AnimalsCells
  15. Schneider A, Aghamirzaie D, Elmarakeby H, Poudel AN, Koo AJ, Heath LS, Grene R, Collakova E. Potential targets of VIVIPAROUS1/ABI3-LIKE1 (VAL1) repression in developing Arabidopsis thaliana embryos. Plant J. 2016 Jan; 85(2):305-19. PMID: 26678037.
    Citations: 18     Fields:    Translation:Animals
  16. Weisberg AJ, Elmarakeby HA, Heath LS, Vinatzer BA. Similarity-based codes sequentially assigned to ebolavirus genomes are informative of species membership, associated outbreaks, and transmission chains. Open Forum Infect Dis. 2015 Jan; 2(1):ofv024. PMID: 26034773.
    Citations: 2     
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Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.