Deep learning for peptide identification from metaproteomics datasets (open access)

Deep learning for peptide identification from metaproteomics datasets

This article explores a proposed deep-learning-based algorithm called DeepFilter for improving peptide identifications from a collection of tandem mass spectra. The authors find that DeepFilter is believed to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification from metaproteomics data.
Date: July 8, 2021
Creator: Feng, Shichao; Sterzenbach, Ryan & Guo, Xuan
System: The UNT Digital Library
Event-Driven Deep Learning for Edge Intelligence (EDL-EI) (open access)

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

Article on deep-learning framework for edge intelligence EDL-EI (event-driven deep learning for edge intelligence). To verify the proposed framework, the authors include a case study of air-quality scenarios based for the most polluted cities in South Korea and China.
Date: September 8, 2021
Creator: Shah, Sayed Khushal; Tariq, Zeenat; Lee, Jeehwan & Lee, Yugyung
System: The UNT Digital Library
Protein functional module identification method combining topological features and gene expression data (open access)

Protein functional module identification method combining topological features and gene expression data

Article conducting an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. The experimental results show that the ECTG algorithm can detect protein functional modules better.
Date: June 8, 2021
Creator: Zhao, Zihao; Xu, Wenjun; Chen, Aiwen; Han, Yueyue; Xia, Shengrong; Xiang, ChuLei et al.
System: The UNT Digital Library
Computing microRNA-gene interaction networks in pan-cancer using miRDriver (open access)

Computing microRNA-gene interaction networks in pan-cancer using miRDriver

This article is a study where the authors integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach.
Date: March 8, 2022
Creator: Bose, Banabithi; Moravec, Matthew & Bozdag, Serdar
System: The UNT Digital Library