Zobrazeno 1 - 10
of 70
pro vyhledávání: '"David M Lin"'
Autor:
Yingxin Lin, Lipin Loo, Andy Tran, David M Lin, Cesar Moreno, Daniel Hesselson, G Gregory Neely, Jean Y H Yang
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 10, p e1010495 (2022)
COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunct
Externí odkaz:
https://doaj.org/article/5c8f5e59fff84e1194187012d9339953
Autor:
Yingxin Lin, Yue Cao, Hani Jieun Kim, Agus Salim, Terence P Speed, David M Lin, Pengyi Yang, Jean Yee Hwa Yang
Publikováno v:
Molecular Systems Biology, Vol 16, Iss 6, Pp 1-16 (2020)
Abstract Automated cell type identification is a key computational challenge in single‐cell RNA‐sequencing (scRNA‐seq) data. To capitalise on the large collection of well‐annotated scRNA‐seq datasets, we developed scClassify, a multiscale c
Externí odkaz:
https://doaj.org/article/71435eb9e5d94b0db1e8fa3e180304ab
Publikováno v:
eLife, Vol 7 (2018)
The delta-protocadherins (δ-Pcdhs) play key roles in neural development, and expression studies suggest they are expressed in combination within neurons. The extent of this combinatorial diversity, and how these combinations influence cell adhesion,
Externí odkaz:
https://doaj.org/article/cb0de718a019401f82d1850f77239a78
Autor:
Xiaohang Fu, Yingxin Lin, David M. Lin, Daniel Mechtersheimer, Chuhan Wang, Farhan Ameen, Shila Ghazanfar, Ellis Patrick, Jinman Kim, Jean Y. H. Yang
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcri
Externí odkaz:
https://doaj.org/article/9e3ea5cddd3a493ea0d1a894b632c3ba
Autor:
Eric O Williams, Heather M Sickles, Alison L Dooley, Sierra ePalumbos, Adam J Bisogni, David M Lin
Publikováno v:
Frontiers in Neural Circuits, Vol 5 (2011)
Olfactory sensory neurons (OSNs) are thought to use activity-dependent and independent mechanisms to regulate the expression of axon guidance genes. However, defining the molecular mechanisms that underlie activity-dependent OSN guidance has remained
Externí odkaz:
https://doaj.org/article/f9a7e41ec6a84bbaa6345d038ccc1173
Supplementary Tables 1-2 from Shmt1 Heterozygosity Impairs Folate-Dependent Thymidylate Synthesis Capacity and Modifies Risk of Apcmin-Mediated Intestinal Cancer Risk
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba6c25be981096fcfc1ed3cac05efbda
https://doi.org/10.1158/0008-5472.22385582.v1
https://doi.org/10.1158/0008-5472.22385582.v1
Autor:
Hani Jieun Kim, Kevin Wang, Carissa Chen, Yingxin Lin, Patrick P. L. Tam, David M. Lin, Jean Y. H. Yang, Pengyi Yang
Publikováno v:
Nature Computational Science. 1:784-790
The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-speci
Publikováno v:
UIST
Understanding which hand a user holds a smartphone with can help improve the mobile interaction experience. For instance, the layout of the user interface (UI) can be adapted to the holding hand. In this paper, we present HandyTrak, an AI-powered sof
Autor:
Yingxin Lin, Lipin Loo, Andy Tran, David M. Lin, Cesar Moreno, Daniel Hesselson, G. Gregory Neely, Jean Y. H. Yang
Publikováno v:
PLoS computational biology. 18(10)
COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunct
Autor:
Kevin Wang, Patrick P.L. Tam, Jean Yh Yang, Hani Jieun Kim, Yingxin Lin, Carissa Chen, Pengyi Yang, David M. Lin
We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a678bb0f7a2dbb1f2812238377b1140
https://doi.org/10.1101/2021.01.10.426138
https://doi.org/10.1101/2021.01.10.426138