Work

Identifying Colorectal Tumor For Single Cell RNA Sequence Using Rectified Linear Unit With Stochastic Gradient Descent

Year: 2023

Type: conference-paper

Abstract: To detect cell clusters in whole human colorectal tumor cells, mechanisms based on single-cell RNA sequencing have been reported. To address such issues, a deep learning technique for single-cell analysis was recently developed, with promising results. Single- cell RNA sequence data contains detailed information about the transcriptome profile. Through gene expression, this information is presented in the form of patterns. The neural network is shown here to help understand these data representatives. Then we use the differential expression to identify distinct cell types and gene markers. Finally, the proposed identifying colorectal tumor for single-cell RNA sequencing that Rectified Linear Unit (ReLu) with Stochastic Gradient Descent(SGD) is compared to other recently developed models such as ReLu with Adam, ReLu with Limited-BFGS-B, TanH with Adam, TanH with SGD, TanH with Limited-BFGS-B, Sigmoid with Adam, Sigmoid with SGD, Sigmoid with Limited-BFGS-B, Linear with Adam, Linear with Limited-BFGS-B and Linear with SGD. At the moment, the results from identifying colorectal tumor for single-cell RNA sequencing that ReLu with SGD performs better than other recently developed models.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2023.01.001

Authors: Mothe Rajesh, Biswajit Senapati, Ranjita Das, Sheshikala Martha

Institution: SR University

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Department of Science and Technology, Ministry of Science and Technology, India, Science and Engineering Research Board