Shuai Liu

Orcid: 0000-0003-2867-4683

Affiliations:
  • Jilin University, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, BioKnow Health Informatics Lab, Changchun, China


According to our database1, Shuai Liu authored at least 10 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species.
Comput. Biol. Medicine, June, 2023

2022
Transforming OMIC features for classification using siamese convolutional networks.
J. Bioinform. Comput. Biol., 2022

2021
RIFS2D: A two-dimensional version of a randomly restarted incremental feature selection algorithm with an application for detecting low-ranked biomarkers.
Comput. Biol. Medicine, 2021

Computational pan-cancer characterization of model-based quantitative transcription regulations dysregulated in regional lymph node metastasis.
Comput. Biol. Medicine, 2021

A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear.
Briefings Bioinform., 2021

Survival Time Prediction of Breast Cancer Patients Using Feature Selection Algorithm Crystall.
IEEE Access, 2021

2020
Selection of features for patient-independent detection of seizure events using scalp EEG signals.
Comput. Biol. Medicine, 2020

FeSTwo, a two-step feature selection algorithm based on feature engineering and sampling for the chronological age regression problem.
Comput. Biol. Medicine, 2020

ELMO: An Efficient Logistic Regression-Based Multi-Omic Integrated Analysis Method for Breast Cancer Intrinsic Subtypes.
IEEE Access, 2020

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI.
IEEE Access, 2020


  Loading...