Jialu Ma
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: Jiangnan University (2022–2025); Central University of Finance and Economics (2019); Central South University (2024); University of Electronic Science and Technology of China (2017–2021); Shenzhen University (2025); Chengdu University (2020); China Astronaut Research and Training Center (2015–2022); Shanghai Institute of Technology (2023); China Earthquake Administration (2024)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jialu Ma's research focuses on the application of molecular dynamics simulations and computational modeling to investigate complex biological and material systems. Ma has published work examining the therapeutic response of chronic myeloid leukemia through single-cell transcriptome analysis and network analysis, as well as exploring regulators and signaling pathways in lung adenocarcinoma progression. Another area of research involves numerical simulations of ion channel regulation by terahertz fields and the characterization of liquid sample permittivity using split-ring resonators. Ma also has work on inorganic fillers for proton exchange membranes and impact behavior of composite panels. Ma holds an h-index of 9 with 39 total publications and 242 total citations. Key collaborators include John R. Talburt and Mary Qu Yang, with whom Ma has co-authored three publications each.
Metrics
- h-index: 9
- Publications: 39
- Citations: 248
Selected Publications
-
Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression (2025)
-
A Deep Learning-Based Model for Gene Regulatory Network Inference (2023)
-
Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia (2022)
Collaboration Network
Top Collaborators
- Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia
- A Deep Learning-Based Model for Gene Regulatory Network Inference
- Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression
- Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia
- Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression
- Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia
- Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia
- Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia
- A Deep Learning-Based Model for Gene Regulatory Network Inference
- A Deep Learning-Based Model for Gene Regulatory Network Inference
- Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression
- Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression
Similar Researchers
Based on overlapping research topics