Dylan Molho

ORCID: 0009-0008-9507-9693
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About
Contact & Profiles
Research Areas
  • Cell Image Analysis Techniques
  • Single-cell and spatial transcriptomics
  • Advanced Fluorescence Microscopy Techniques
  • Biosensors and Analytical Detection

Michigan State University
2022-2024

DANCE is the first standard, generic, and extensible benchmark platform for accessing evaluating computational methods across spectrum of datasets numerous single-cell analysis tasks. Currently, supports 3 modules 8 popular tasks with 32 state-of-art on 21 datasets. People can easily reproduce results supported algorithms major via minimal efforts, such as using only one command line. In addition, provides an ecosystem deep learning architectures tools researchers to facilitate their own...

10.1186/s13059-024-03211-z article EN cc-by Genome biology 2024-03-19

Single-cell technologies are revolutionizing the entire field of biology. The large volumes data generated by single-cell high dimensional, sparse, and heterogeneous have complicated dependency structures, making analyses using conventional machine learning approaches challenging impractical. In tackling these challenges, deep often demonstrates superior performance compared to traditional methods. this work, we give a comprehensive survey on in analysis. We first introduce background their...

10.1145/3641284 article EN ACM Transactions on Intelligent Systems and Technology 2024-01-26

Abstract In the realm of single-cell analysis, computational approaches have brought an increasing number fantastic prospects for innovation and invention. Meanwhile, it also presents enormous hurdles to reproducing results these models due their diversity complexity. addition, lack gold-standard benchmark datasets, metrics, implementations prevents systematic evaluations fair comparisons available methods. Thus, we introduce DANCE platform, first standard, generic, extensible platform...

10.1101/2022.10.19.512741 preprint EN cc-by-nd bioRxiv (Cold Spring Harbor Laboratory) 2022-10-21

Single-cell technologies are revolutionizing the entire field of biology. The large volumes data generated by single-cell high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging impractical. In tackling these challenges, deep often demonstrates superior performance compared to traditional methods. this work, we give a comprehensive survey on in analysis. We first introduce background their...

10.48550/arxiv.2210.12385 preprint EN cc-by-nc-nd arXiv (Cornell University) 2022-01-01
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