This is the website for Bioinformatics Lab at Auburn University. Although it is a new lab, we have been involved in developing state of the art solutions to help tackle problems in the ever-evolving world of Bioinformatics. Much of the research in our lab builds on collaborations with others, both at the AU and also further field.
We have a diverse group focusing on different aspects of Bioinformatics. With the aim of solving different problems in the world that ranges from finding subtypes of diseases to research in single cell, we do not know what the future holds for the lab but it is certainly going to be interesting!
Interests
- Data Science
- Machine Learning
- Disease Subtyping
- Pathway Analysis
- Single-cell Analysis
Education
Ph.D., Computer Science, 2017
Wayne State University
M.S., Computer Science, 2008
Eotvos Lorand University
B.S., Computer Science, 2004
Eotvos Lorand University
Latest
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review
- AIPA
- CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome
- SSA: a novel method for Single-cell and Spatial transcriptomics Alignment
- RCPA
- RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization
- DeconBenchmark
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges
- Changing global epidemiology of chronic hepatitis C virus-related outcomes from 2010 to 2019: cirrhosis is the growing burden of hepatitis C virus-related disease
- Associate professor in CSSE part of $1.8 million NIH study to pioneer platform for the analysis of single-cell data
- Professor in computer science and software engineering uses data science, machine learning to combat cancer subtype misdiagnoses
- A novel approach for predicting upstream regulators (PURE) that affect gene expression
- Acyltransferase families that act on thioesters: Sequences, structures, and mechanisms
- Machine Learning Techniques for Cancer Subtype Discovery and Single-Cell RNA Sequencing Data Analysis
- Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering
- A call for standardized reporting of early-onset colorectal peritoneal metastases
- A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
- ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
- DWEN: A novel method for accurate estimation of cell type compositions from bulk data samples
- A comprehensive survey of the approaches for pathway analysis using multi-omics data integration
- The method scCAN is in the news of the Journal of Science and Development, Vietnamese Ministry of Science
- 7 faculty members receive CAREER Awards in support of early-career success
- Mouse genomic associations with in vitro sensitivity to simulated space radiation
- Quantitative proteomic analytic approaches to identify metabolic changes in the medial prefrontal cortex of rats exposed to space radiation
- scCAN
- scCAN: single-cell clustering using autoencoder and network fusion
- Computer science researcher plans to use machine learning to improve cancer treatments
- The Association between Serum Serine and Glycine and Related-Metabolites with Pancreatic Cancer in a Prospective Cohort Study
- Great Basin College Bioinformatics Workshop 2022
- DrGA: cancer driver gene analysis in a simpler manner
- A novel method for single-cell data imputation using subspace regression
- scISR
- Identification and Validation of a Novel Three Hub Long Noncoding RNAs With m6A Modification Signature in Low-Grade Gliomas
- Provenance documentation to enable explainable and trustworthy AI: A literature review
- Thioesterase enzyme families: Functions, structures, and mechanisms
- Single-cell RNA sequencing data imputation using similarity preserving network
- scIDS: Single-cell Imputation by combining Deep autoencoder neural networks and Subspace regression
- Re-Identification of Patient Subgroups in Uveal Melanoma
- SMRT
- SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis
- Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd
- The association between diabetes and gastric cancer: results from the Stomach Cancer Pooling Project Consortium
- CPA
- CPA: a web-based platform for consensus pathway analysis and interactive visualization
- Great Basin College Bioinformatics Workshop 2021
- New tool allows computer scientists to support life scientists
- Single-Cell RNA Sequencing Data Imputation Using Deep Neural Network
- Fast and precise single-cell data analysis using a hierarchical autoencoder
- scDHA
- Analysis of Short-read Aligners using Genome Sequence Complexity
- Disease subtyping using community detection from consensus networks
- Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer
- Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data
- A comprehensive survey of regulatory network inference methods using single-cell RNA sequencing data
- PINSPlus
- A Novel Method for Cancer Subtyping and Risk Prediction Using Consensus Factor Analysis
- SCFA
- NBIA: a network-based integrative analysis framework – applied to pathway analysis
- Pack Research Experience Program Mentoring
- Integrated Cancer Subtyping using Heterogeneous Genome-Scale Molecular Datasets
- RIA: a novel Regression-based Imputation Approach for single-cell RNA sequencing
- Identifying Significantly Impacted Pathways: A Comprehensive Review and Assessment
- GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis
- Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- MGKA: A genetic algorithm-based clustering technique for genomic data
- Functional analysis tools for post‐translational modification: a post‐translational modification database for analysis of proteins and metabolic pathways
- R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets
- Statistical Software
- A comprehensive survey of tools and software for active subnetwork identification
- Orthogonal approach to integrate independent omic data
- FAT-PTM
- A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures
- MIA: Multi-cohort Integrated Analysis for Biomarker Identification
- Robust Fuzzy Cluster Ensemble on Cancer Gene Expression Data
- PINSPlus: A tool for tumor subtype discovery in integrated genomic data
- Network‐Based Approaches for Pathway Level Analysis
- A survey of the approaches for identifying differential methylation using bisulfite sequencing data.
- A Systems Biology Approach for Unsupervised Clustering of High-Dimensional Data
- MicroRNA-augmented pathways (mirAP) and their applications to pathway analysis and disease subtyping
- TOMAS: A novel TOpology-aware Meta-Analysis approach applied to System biology
- PINS: A Perturbation Clustering Approach for Data Integration and Disease Subtyping
- A novel approach for data integration and disease subtyping
- Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data
- DANUBE: Data-Driven Meta-ANalysis Using UnBiased Empirical Distributions—Applied to Biological Pathway Analysis
- A novel bi-level meta-analysis approach: applied to biological pathway analysis
- BLMA
- MarkovBin: An Algorithm to Cluster Metagenomic Reads Using a Mixture Modeling of Hierarchical Distributions
- QSEA for fuzzy subgraph querying of KEGG pathways
- iQuant: A fast yet accurate GUI tool for transcript quantification
- SPATA: A highly accurate GUI tool for de novo transcriptome assembly
- SPATA: A seeding and patching algorithm for de novo transcriptome assembly