Author

K. Katija

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Animal Behavior & Cognition· 1 Biophysics· 1 Ecology· 1 Zoology· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Research Focus:cell type annotation· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:development & morphogenesis· 1 Research Focus:differential expression· 1

Frequent Co-Authors

A. K. Fahimipour· 1 shared A. M. Hein· 1 shared A. Manjunath· 1 shared B. Burford· 1 shared B. T. Belcher· 1 shared B. Vaca· 1 shared C. Fannjiang· 1 shared D. Mann· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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1 shared paperEcology

A. K. Fahimipour

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4 papers

Recent·Most cited

Nectophore coordination and kinematics by physonect siphonophores

S. Strock, J. H. Costello, J. Daniels, K. Katija, S. Colin

This study examines the kinematics and coordination of nectophores in physonect siphonophores, finding metachronal coordination on the same side of the nectosomal axis and similar pulsing frequencies on opposite sides.

bioRxiv · Apr 12, 2023 · PreprintZoology

Flow Physics Explains Morphological Diversity of Ciliated Organs

F. Ling, T. Essock-Burns, M. McFall-Ngai, K. Katija, J. C. Nawroth, E. Kanso

The paper shows that ciliated organ designs follow functional constraints, connecting carpet and flame types through lumen diameter and cilia-to-lumen ratio.

bioRxiv · Feb 12, 2023Biophysics

Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools

B. T. Belcher, E. H. Bower, B. Burford, M. R. Celis, A. K. Fahimipour, I. L. Guevara, K. Katija, Z. Khokhar, A. Manjunath, S. Nelson, S. Olivetti, E. Orenstein, M. H. Saleh, B. Vaca, S. Valladares, , A. M. Hein

This paper provides a practical guide to applying image-based machine learning for marine research, demonstrating data preparation, model training, and troubleshooting techniques with a case study.

bioRxiv · Dec 24, 2022 · PreprintEcology

Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens

C. Fannjiang, T. A. Mooney, S. Cones, D. Mann, K. A. Shorter, K. Katija

The paper uses biologging and supervised machine learning to study in situ behavior of jellyfish, identifying tag data corruption and classifying behaviors for long-term activity analysis.

bioRxiv · Jun 3, 2019Animal Behavior & Cognition