Noa Rappaport
Institute for Systems Biology
56 Papers
45 Citations
Noa Rappaport is an academic researcher from Institute for Systems Biology. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 13, co-authored 27 publications. Previous affiliations of Noa Rappaport include Weizmann Institute of Science.
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Papers
The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses
Gil Stelzer,Naomi Rosen,Inbar Plaschkes,Shahar Zimmerman,Michal Twik,Simon Fishilevich,Tsippi Iny Stein,Ron Nudel,Iris Lieder,Yaron Mazor,Sergey Kaplan,Dvir Dahary,David Warshawsky,Yaron Guan-Golan,Asher Kohn,Noa Rappaport,Marilyn Safran,Doron Lancet +17 more
TL;DR: GeneCards, the human gene compendium, enables researchers to effectively navigate and inter‐relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways and provides a stronger foundation for the GeneCards suite of companion databases and analysis tools.
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GeneHancer: genome-wide integration of enhancers and target genes in GeneCards
Simon Fishilevich,Ron Nudel,Noa Rappaport,Rotem Hadar,Inbar Plaschkes,Tsippi Iny Stein,Naomi Rosen,Asher Kohn,Michal Twik,Marilyn Safran,Doron Lancet,Dana Cohen +11 more
TL;DR: GeneHancer is presented, a novel database of human enhancers and their inferred target genes, in the framework of GeneCards, which assists in the mapping of non-coding variants to enhancers, and via the linked genes, forms a basis for variant–phenotype interpretation of whole-genome sequences in health and disease.
MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search.
Noa Rappaport,Michal Twik,Inbar Plaschkes,Ron Nudel,Tsippi Iny Stein,Jacob Levitt,Moran Gershoni,C. Paul Morrey,Marilyn Safran,Doron Lancet +9 more
TL;DR: The MalaCards human disease database is an integrated compendium of annotated diseases mined from 68 data sources and adopts a ‘flat’ disease-card approach, but each card is mapped to popular hierarchical ontologies and contains information about multi-level relations among diseases, thereby providing an optimal tool for disease representation and scrutiny.
Gut microbiome pattern reflects healthy ageing and predicts survival in humans
Tomasz Wilmanski,Christian Diener,Noa Rappaport,Sushmita Patwardhan,Jack Wiedrick,Jodi Lapidus,John C. Earls,Anat Zimmer,Gustavo Glusman,Max Robinson,James T. Yurkovich,Deborah M. Kado,Jane A. Cauley,Joseph M. Zmuda,Nancy E Lane,Andrew T. Magis,Jennifer C. Lovejoy,Leroy Hood,Sean M. Gibbons,Sean M. Gibbons,Eric S. Orwoll,Nathan D. Price +21 more
- 18 Feb 2021
TL;DR: In this paper, the authors leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream.
Blood metabolome predicts gut microbiome α-diversity in humans.
Tomasz Wilmanski,Noa Rappaport,John C. Earls,Andrew T. Magis,Ohad Manor,Jennifer C. Lovejoy,Gilbert S. Omenn,Leroy Hood,Sean M. Gibbons,Sean M. Gibbons,Nathan D. Price +10 more
TL;DR: The ability of the blood metabolome to predict gut microbiome α-diversity could pave the way to the development of clinical tests for monitoring gut microbial health and almost half of gut microbiome diversity in humans can be explained by 40 blood metabolites.