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CellBiAge: Improved single-cell age classification using data binarization

https://doi.org/10.1016/j.celrep.2023.113500

Yu, Doudou ; Li, Manlin ; Linghu, Guanjie ; Hu, Yihuan ; Hajdarovic, Kaitlyn H ; Wang, An ; Singh, Ritambhara ; Webb, Ashley E ( December 2023 , Cell Reports)

Free, publicly-accessible full text available December 1, 2024
LETTER FROM THE EDITORS: THE EMERGING NEED FOR BIOLOGICALLY INSPIRED AND MATHEMATICALLY GUIDED MACHINE LEARNING FOR KNOWLEDGE DISCOVERY IN BIOLOGY

https://doi.org/10.1615/JMachLearnModelComput.2023049680

Singh, Ritambhara ; Seshaiyer, Padmanabhan ( January 2023 , Journal of Machine Learning for Modeling and Computing)

Full Text Available
Comparative analysis of animal lifespan

https://doi.org/10.1007/s11357-023-00984-2

Riddle, Nicole C. ; Biga, Peggy R. ; Bronikowski, Anne M. ; Walters, James R. ; Wilkinson, Gerald S. ; IISAGE Consortium ; Duan, Jingyue Ellie ; Gamble, Anthony ; Larschan, Erica ; Meisel, Richard P. ; et al ( October 2023 , GeroScience)

Abstract
Comparative studies of aging are a promising approach to identifying general properties of and processes leading to aging. While to date, many comparative studies of aging in animals have focused on relatively narrow species groups, methodological innovations now allow for studies that include evolutionary distant species. However, comparative studies of aging across a wide range of species that have distinct life histories introduce additional challenges in experimental design. Here, we discuss these challenges, highlight the most pressing problems that need to be solved, and provide suggestions based on current approaches to successfully carry out comparative aging studies across the animal kingdom.

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SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation

https://doi.org/10.1089/cmb.2022.0270

Demetci, Pinar ; Santorella, Rebecca ; Chakravarthy, Manav ; Sandstede, Bjorn ; Singh, Ritambhara ( November 2022 , Journal of Computational Biology)

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Unsupervised Integration of Single-Cell Multi-omics Datasets with Disproportionate Cell-Type Representation

https://doi.org/10.1007/978-3-031-04749-7_1

Demetçi, Pinar ; Santorella, Rebecca ; Sandstede, Bjorn ; Singh, Ritambhara ( April 2022 , RECOMB 2022: Research in Computational Molecular Biology)

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Single-Cell Multiomics Integration by SCOT

https://doi.org/10.1089/cmb.2021.0477

Demetci, Pinar ; Santorella, Rebecca ; Sandstede, Björn ; Noble, William Stafford ; Singh, Ritambhara ( January 2022 , Journal of Computational Biology)

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SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport

https://doi.org/10.1089/cmb.2021.0446

Demetci, Pinar ; Santorella, Rebecca ; Sandstede, Björn ; Noble, William Stafford ; Singh, Ritambhara ( January 2022 , Journal of Computational Biology)

Full Text Available