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Meryem Banu

As seen in: arXiv, Genome Biology
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Articles by Meryem Banu

MegIS: High-Performance, Energy-Efficient, and Low-Cost Metagenomic Analysis with In-Storage Processing

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By Nika Mansouri, Meryem Banu
| arXiv
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[2406.16153] RowPress Vulnerability in Modern DRAM Chips

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By A. Giray, Yahya Can, Meryem Banu
| arXiv
arXiv:2406.16153 (cs) View PDF HTML (experimental) Comments: Subjects: Hardware Architecture (cs.AR); Cryptography and Security (cs.CR) Cite as: arXiv:2406.16153 [cs.AR] (or arXiv:2406.16153v1 [cs.AR] for this version) Submission history From: Haocong Luo [ view email] [v1] Sun, 23 Jun 2024 16:26:35 UTC (1,155 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart...
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RUBICON: a framework for designing efficient deep learning-based genomic basecallers - Genome Biology

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By Meryem Banu
| Genome Biology Verified
References Ginsburg G, Phillips K. Precision medicine: from science to value. Health Aff. 2018;05(37):694–701. https://doi.org/10.1377/hlthaff.2017.1624. Article Google Scholar Aryan Z, Szanto A, Pantazi A, Reddi T, Rheinstein C, Powers W, et al. Moving genomics to routine care: an initial pilot in acute cardiovascular disease. Circ Genomic Precis Med. 2020;13(5):406–16. https://doi.org/10.1161/CIRCGEN.120.002961.
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RowPress: Amplifying Read Disturbance in Modern DRAM Chips

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By A. Giray, Yahya Can, Meryem Banu
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2211.03079] A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

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By Meryem Banu
| arXiv
arXiv:2211.03079 (cs) [Submitted on 6 Nov 2022 (v1), last revised 27 May 2023 (this version, v5)] Download a PDF of the paper titled A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers, by Gagandeep Singh and 7 other authors Download PDF Submission history From: Gagandeep Singh [ view email] [v1] Sun, 6 Nov 2022 10:47:05 UTC (34,995 KB) [v2] Mon, 21 Nov 2022 07:37:28 UTC (17,589 KB) [v3] Thu, 8 Dec 2022 14:45:40 UTC (23,314 KB) [v4] Fri, 14 Apr 2023 11:17:09 UTC (7,970...
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TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

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By Meryem Banu, Nika Mansouri
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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