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Nan Lin
Researcher at Harvard University
Publications - 1220
Citations - 65601
Nan Lin is an academic researcher from Harvard University. The author has contributed to research in topics: Medicine & Breast cancer. The author has an hindex of 105, co-authored 687 publications receiving 54545 citations. Previous affiliations of Nan Lin include University of Michigan & Fujian Medical University.
Papers
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COF-DNA Bicolor Nanoprobes for Imaging Tumor-Associated mRNAs in Living Cells.
TL;DR: The freezing-method-prepared nanoprobe has higher signal intensities in target-overexpressed cells compared to the room-temperature-pre prepared probe, while their signals in cells with low target expression are similar, and is a promising tool for improved cancer diagnostic imaging.
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Correlating Electrochemical Kinetic Parameters of Single LiNi1/3Mn1/3Co1/3O2 Particles with the Performance of Corresponding Porous Electrodes.
TL;DR: In this paper , a physics-based model for extracting the solid-phase diffusion coefficient (Ds) and exchange current density (i0) from electrochemical impedance measurements was developed.
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An iteration normalization and test method for differential expression analysis of RNA-seq data
Yan Zhou,Nan Lin,Baoxue Zhang +2 more
TL;DR: In this article, the authors developed a normalization method based on iterating median of M-values (IMM) for detecting the differentially expressed (DE) genes, which improved the accuracy of DE detection.
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Barriers to Clinical Trial Accrual: Perspectives of Community-Based Providers
Lauren P. Knelson,Anthony R. Cukras,Jennifer Savoie,Ankit Agarwal,Hao Guo,Jiani Hu,Geoffrey Fell,Ruth Lederman,Melissa E. Hughes,Eric P. Winer,Nan Lin,Sara M. Tolaney +11 more
TL;DR: A follow-up survey revealed that only 18% of the participants used the web-based tool as discussed by the authors, indicating that patients valued trial participation for their patients but found it difficult to manage.
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Adjustment of Measuring Devices With Linear Models
TL;DR: It is found that the superiority in parameter estimation of the MLE does not always result in better adjustments, and the simple regression method often performs better than the multivariate MLE in adjusting the measurements from a cruder device despite its bias problem.