Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Ragunathan Mariappan"'
Autor:
Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S. Jain, Mirjana Efremova, Sarah A. Teichmann, Vaibhav Rajan, Xiuwei Zhang
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-16 (2023)
Many methods for single cell data integration have been developed, though mosaic integration remains challenging. Here the authors present scMoMaT, a mosaic integration method for single cell multi-modality data from multiple batches, that jointly le
Externí odkaz:
https://doaj.org/article/c9a327a9fd914f9a8225b56b9fdade17
Publikováno v:
JMIR Medical Informatics, Vol 10, Iss 1, p e28842 (2022)
BackgroundPatient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource-
Externí odkaz:
https://doaj.org/article/31d5a543ae824c098bf8d0023d64e68a
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 10, p e32730 (2021)
BackgroundAdverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilan
Externí odkaz:
https://doaj.org/article/e1d849333e4f47b49ecc911bfc3b5c06
Publikováno v:
Bioinformatics. 38:4554-4561
Motivation In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary colle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69cc7bf56d4106fe06be83d963da238a
https://doi.org/10.1093/bioinformatics/btac543
https://doi.org/10.1093/bioinformatics/btac543
scMoMaT: a unified framework for single cell mosaic integration and multi-modal bio-marker detection
Autor:
Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika Sarkin Jain, Mirjana Efremova, Sarah Teichmann, Vaibhav Rajan, Xiuwei Zhang
Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61e70030da7988bcc8879f2c99fdbd67
https://doi.org/10.21203/rs.3.rs-2022586/v1
https://doi.org/10.21203/rs.3.rs-2022586/v1
Autor:
Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S Jain, Mirjana Efremova, Sarah A Teichmann, Vaibhav Rajan, Xiuwei Zhang
Single cell data integration methods aim to integrate cells across data batches and modalities, and obtain a comprehensive view of the cells. Single cell data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09d1b6623d580f7c4d51586608b7dc27
https://doi.org/10.1101/2022.05.17.492336
https://doi.org/10.1101/2022.05.17.492336
BACKGROUND Adverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigila
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3f391e3f2b8698a1d4237be854d2024
https://doi.org/10.2196/preprints.32730
https://doi.org/10.2196/preprints.32730
Autor:
Ragunathan Mariappan, Vaibhav Rajan
Publikováno v:
Machine Learning. 108:1395-1420
Learning by integrating multiple heterogeneous data sources is a common requirement in many tasks. Collective Matrix Factorization (CMF) is a technique to learn shared latent representations from arbitrary collections of matrices. It can be used to s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee159f718703e0279bfee3c0fb3031bb
https://doi.org/10.1007/s10994-019-05801-6
https://doi.org/10.1007/s10994-019-05801-6
BACKGROUND Patient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f2ca835ff4c65e74eb4223d3f9f8680
https://doi.org/10.2196/preprints.28842
https://doi.org/10.2196/preprints.28842
Autor:
Ragunathan Mariappan, Vaibhav Rajan, Alicia Nanelia Tan Li Shi, Sajit Kumar, Adithya Rajagopal
Publikováno v:
JMIR Medical Informatics. 10:e28842
Background Patient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::938775c09372e3ce86b55680b16448a0
https://doi.org/10.2196/28842
https://doi.org/10.2196/28842