Author

M. Nishino

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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Ecology· 3 Cell Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 2 Approach:statistical model· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1 Approach:genetic perturbation / CRISPR· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

J. Kartesz· 2 shared A. Kuprijanov· 1 shared A. Nowak· 1 shared A. Patzelt· 1 shared A. Stein· 1 shared A. Taylor· 1 shared B. F. Atahan· 1 shared B. Goulet-Scott· 1 shared

Activity by Year

2022-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersEcology

J. Kartesz

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Histone methyltransferase SUV39H1 regulates the Golgi complex via the nuclear envelope-spanning LINC complex

M. Nishino, H. Imaizumi, Y. Yokoyama, J. Katahira, H. Kimura, N. Matsuura, M. Matsumura

This study shows that SUV39H1 regulates Golgi dispersion through the LINC complex, linking chromatin organization to cell motility.

bioRxiv · Mar 13, 2023Cell Biology

Genetic differentiation of water penny beetles may be associated with the formation process of the ancient Lake Biwa.

N. I. Kondo, M. Nishino, M. Uenishi, K. Ishikawa, N. Nakajima, N. Takamura

This study investigates genetic differentiation in water penny beetles around Lake Biwa, finding that E. ramicornis diverged 1.4 MYA while E. granicollis showed no genetic differentiation.

bioRxiv · Nov 15, 2022 · PreprintEcology

Machine learning improves global models of plant diversity

L. Cai, H. Kreft, A. Taylor, P. Denelle, J. Schrader, F. Essl, M. v. Kleunen, J. Pergl, P. Pysek, A. Stein, M. Winter, J. F. Barcelona, N. Fuentes, I. Inderjit, D. N. Karger, , P. Weigelt

The paper uses machine learning and statistical models to predict global plant diversity patterns based on environmental data, achieving high accuracy for species and phylogenetic richness.

bioRxiv · Apr 8, 2022Ecology

A dataset of 5 million city trees: species clustering and climate effects in urban forests

D. McCoy, B. Goulet-Scott, W. Meng, B. F. Atahan, H. Kiros, M. Nishino, J. Kartesz

The paper presents a dataset of 5 million city trees and analyzes species clustering, nativity status, and climate effects in urban forests.

bioRxiv · Mar 18, 2022 · PreprintEcology