Author

Andrey S. Lando

Yandex (Russia)

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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Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Assay:whole-genome sequencing· 2 Organism:yeast· 1

Frequent Co-Authors

Abdul Muntakim RafiiD· 2 shared Arsenii O. ZinkevichiD· 2 shared Carl G. de BoeriD· 2 shared Daria NoginaiD· 2 shared Dmitry PenzariD· 2 shared Georgy MeshcheryakoviD· 2 shared Ivan V. KulakovskiyiD· 2 shared Aayush Prakash· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenomics

Abdul Muntakim RafiORCID

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2 papers

Recent·Most cited

Evaluation and optimization of sequence-based gene regulatory deep learning models

Abdul Muntakim Rafi, Daria Nogina, Dmitry Penzar, Dohoon Lee, Danyeong Lee, N. S. KIM, Sangyeup Kim, Dohyeon Kim, Yeojin Shin, Il‐Youp Kwak, Georgy Meshcheryakov, Andrey S. Lando, Arsenii O. Zinkevich, Byeongchan Kim, Juhyun Lee, , Carl G. de Boer

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

Nature Biotechnology · Oct 11, 2024Peer-reviewedGenomics

LegNet: a best-in-class deep learning model for short DNA regulatory regions

Dmitry Penzar, Daria Nogina, Elizaveta Noskova, Arsenii O. Zinkevich, Georgy Meshcheryakov, Andrey S. Lando, Abdul Muntakim Rafi, Carl G. de Boer, Ivan V. Kulakovskiy

LegNet is a deep learning model for predicting gene expression from DNA sequences, outperforming existing models and enabling promoter sequence design.

Bioinformatics · Jul 25, 2023Peer-reviewedBioinformatics