A photo of Frank Huang.

Assistant Professor, UC Department of Pediatrics

513-517-1084

Biography & Affiliation

Biography

Dr. Huang's research focuses on developing advanced computational architecture (machine learning and deep learning) and statistical methods to integrate multiple types of omics data, including DNA-seq(Whole genome sequencing), RNA-seq, Chip-seq, Methylation, large-scale pharmaco-genomics data and in-house electronic health records, for driver signaling pathway analysis, drug repositioning, drug combination prediction, stromal/immune-tumor cell communication and drug-resistant mechanisms and validating the computational discoveries using in-vivo xenograft models. Meanwhile, He has been collaborating closely with clinicians from different disciplines (Cancer, Immunology, developmental, and Neurology) to seek the computational solutions to the cutting-edge medical problem arising from clinical practice.

Dr. Huang has designed a non-parametric, bootstrapping based simulated annealing (NBPSA) approach to identify driver signaling pathways or predict cell-cell interactions for cancer patients by integrating multiple personal genomic profiling (DNA-seq, RNA-seq, Copy number, Chip-seq, Methylation). This work sheds light on driver signaling pathways with genes that show simultaneous mutation, methylation, copy number, and gene expression alterations are likely to play important roles in tumor metastasis and drug-resistance. Based on the driver signaling pathway analysis, He has developed a DSNI-DFN approach for drug repositioning. DSNI-DFN identified Cardiac Glycosides that inhibit the Groups 3 and 4 medulloblastoma tumor from 1,309 candidate drugs. One of the Cardiac Glycosides, digoxin, is validated in Groups 3 and 4 medulloblastomas in vivo. He has also developed two systems biology platforms, i.e. DrugComboRanker and DrugComboExplorer, in order to reduce drug resistance in cancer therapy and identify the mechanisms of drug synergism. These drug combination tools have demonstrated a strong predictive ability for specific cancer types (non-small cell lung cancer, estrogen receptor-positive breast cancer, Ewing Sarcoma, and AR-negative prostate cancer). The predicted drug combinations are further validated by in-vivo experiments. Meanwhile, such work uncovers the potential mechanisms of action of drugs and synergistic effects of drug combinations from a driver signaling pathway perspective.

Research Interests

Single-cell RNA-seq analysis; single-cell ATAC-seq analysis; cell-cell interactions; stromal/immune-tumor cell communication; gene regulatory network analysis; multi-omics integration analysis; whole-genome sequencing/whole exome sequencing and driver mutation analysis; copy number variation; whole-genome bisulfite sequencing (WGBS) or methylation analysis; chip-seq analysis; genomics; epigenetics; drug repositioning; drug combination prediction; bioinformatics and systems biology; GWAS analysis; enhancer-target/transcription factor-target network analysis; driver signaling pathway analysis; network or pathway visualization; machine learning and artificial intelligence(AI); statistics; deep learning; imaging analysis; brain tumor, medulloblastoma, glioblastoma, ependymoma and diffuse intrinsic pontine glioma (DIPG); neuro-degenerative diseases; developmental biology; immunology; leukemia

Academic Affiliation

Assistant Professor, UC Department of Pediatrics

Divisions

Cancer and Blood Diseases, Experimental Hematology and Cancer Biology

Education

PhD: Machine Learning and Computational Biology, Peking University, China, 2015.

Research Fellow/Postdoc/Research Associate: Computational and Systems Biology, Houston Methodist Cancer Center/Research Institute and Weill Cornell Medicine of Cornell University, Houston, TX, 2012-2019.

Publications

Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data. Zhang, J; Zhu, W; Wang, Q; Gu, J; Huang, LF; Sun, X. PLoS Computational Biology. 2019; 15:e1007435-e1007435.

Driver network as a biomarker: Systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Huang, L; Brunell, D; Stephan, C; Mancuso, J; Yu, X; He, B; Thompson, TC; Zinner, R; Kim, J; Davies, P; et al. Bioinformatics. 2019; 35:3709-3717.

TWIST1 Heterodimerization with E12 Requires Coordinated Protein Phosphorylation to Regulate Periostin Expression. Mikheeva, SA; Camp, ND; Huang, L; Jain, A; Jung, SY; Avci, AG; Tokita, M; Wolf-Yadlin, A; Zhang, J; Tapscott, SJ; et al. Cancers. 2019; 11:1392-1392.

Single-Cell Transcriptomics in Medulloblastoma Reveals Tumor-Initiating Progenitors and Oncogenic Cascades during Tumorigenesis and Relapse. Zhang, L; He, X; Liu, X; Zhang, F; Huang, LF; Potter, AS; Xu, L; Zhou, W; Zheng, T; Luo, Z; et al. Cancer Cell. 2019; 36:302-318.e7.

Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction. Liu, Q; Muglia, LJ; Huang, LF. Genes. 2019; 10:602-602.

Systems biology-based drug repositioning identifies digoxin as a potential therapy for groups 3 and 4 medulloblastoma. Huang, L; Injac, SG; Cui, K; Braun, F; Lin, Q; Du, Y; Zhang, H; Kogiso, M; Lindsay, H; Zhao, S; et al. Science Translational Medicine. 2018; 10:eaat0150-eaat0150.

Targeting TWIST1 through loss of function inhibits tumorigenicity of human glioblastoma. Mikheev, AM; Mikheeva, SA; Severs, LJ; Funk, CC; Huang, L; McFaline-Figueroa, JL; Schwensen, J; Trapnell, C; Price, ND; Wong, S; et al. Molecular Oncology. 2018; 12:1188-1202.

Eight proteins play critical roles in RCC with bone metastasis via mitochondrial dysfunction. Wang, J; Zhao, X; Qi, J; Yang, C; Cheng, H; Ren, Y; Huang, L. Clinical and Experimental Metastasis. 2015; 32:605-622.