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Musaddiq Lodi

As seen in: MDPI, BioRxiv
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Articles

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Subgroup

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By Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared View Duplicates (142) Issue with article?

Bone Marrow Stromal Cells Generate a Pro-Healing Inflammasome When Cultured on Titanium-Aluminum-Vanadium Surfaces with Microscale/Nanoscale Structural Features

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By David Cohen, Jingyao Deng, Musaddiq Lodi, Michelle Gallagher
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared Issue with article?

CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification

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By Musaddiq Lodi, Muzammil Lodi, Kezie Osei, Vaishnavi Ranganathan
| BioRxiv
Abstract Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering.
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