In the field of drug discovery, creating new drug-like compounds that can interact with therapeutic targets is crucial. For successful drug development, it is indispensable to have information about the therapeutic target, typically a disease-associated protein, as well as active ligands or molecules capable of effectively interacting with the target. Structure-based drug design (SBDD) holds promising potential to design ligands with high-binding affinity and rationalize their interaction with targets. By utilizing geometric knowledge of the three-dimensional (3D) structures of target binding sites, SBDD enhances the efficacy and selectivity of therapeutic agents by optimizing binding interactions at the molecular level.
Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptomic profiling of individual cells, uncovering cellular heterogeneity within tissues. It involves cell isolation, library preparation, sequencing, and computational analysis to identify cell types, states, and gene expression patterns. Key analytical steps include normalization, dimensionality reduction, clustering, and differential expression analysis. Advanced methods integrate multi-omics data, spatial transcriptomics, and temporal dynamics to provide deeper biological insights. Challenges remain in batch effects, data sparsity, and computational scalability. Our lab is trying to solve these challenges by developing improved data integration methods, AI-driven analysis tools, and novel approaches for single-cell perturbation studies. These advancements will refine our understanding of development, disease mechanisms, and therapeutic targets.
Our lab is working on deep learning-based multi-omics integration to enhance the analysis of complex biological systems. By leveraging deep learning models, we aim to integrate diverse omics data, such as transcriptomics, genomics, and epigenomics, to uncover hidden biological patterns. This approach improves the resolution of cellular states and interactions, providing a more comprehensive view of disease mechanisms. We focus on developing scalable and interpretable models to handle the high-dimensional nature of multi-omics data. Our research has the potential to advance precision medicine by identifying key biomarkers and therapeutic targets.
Our lab is working on Spatial Transcriptomics datasets to identify spatial domains, analyze their interactions, and address dropout imputation challenges. By leveraging deep learning and computational models, we aim to enhance the resolution and accuracy of spatial gene expression data. Our research focuses on uncovering tissue organization, cell-cell communication, and functional heterogeneity in complex biological systems. We develop advanced methods to integrate spatial and molecular information for improved biological interpretation. These efforts contribute to a deeper understanding of development, disease progression, and therapeutic strategies.
Small nucleolar RNAs (snoRNAs) play a crucial role in RNA modification and have been increasingly implicated in various diseases. Our lab leverages machine learning to uncover novel associations between snoRNAs and disease states, integrating multi-omics data for a comprehensive analysis. By employing advanced computational models, we aim to identify key snoRNA biomarkers and their regulatory mechanisms. This research has the potential to enhance disease diagnostics and therapeutic targeting. Our approach bridges computational biology and biomedical research to reveal new insights into snoRNA functions.
Non-coding RNAs (ncRNAs) play a critical role in gene regulation and are increasingly linked to diseases and drug responses. Our lab utilizes machine learning algorithms to analyze complex multi-omics data and uncover novel ncRNA-disease-drug associations. By integrating computational approaches with biomedical insights, we aim to identify key ncRNA biomarkers and therapeutic targets. This research enhances our understanding of ncRNA functions in disease mechanisms and drug interactions. Our work bridges bioinformatics and translational medicine, driving innovations in precision medicine.
Drug synergy plays a crucial role in optimizing combination therapies for improved treatment outcomes. Our lab leverages Large Language Models (LLMs) and deep learning algorithms to predict and analyze drug synergy mechanisms. By integrating multi-omics data and biomedical knowledge, we aim to enhance drug repurposing and precision medicine strategies. Our research focuses on uncovering novel drug interactions and their impact on disease treatment. This work bridges artificial intelligence and pharmacology to accelerate drug discovery and therapeutic advancements.
Our lab develops sample-specific network approaches for precision medicine, offering a novel way to analyze single-cell RNA sequencing (scRNA-seq) data. Instead of focusing solely on differentially expressed genes, our method uncovers latent biological knowledge by analyzing molecular interactions within gene association networks. This approach identifies key genes with low expression variability but significant regulatory influence. By integrating network-based machine learning models, we aim to enhance disease characterization and therapeutic targeting. Our research advances precision medicine by uncovering hidden molecular mechanisms in complex biological systems.
