Biology subjects

Yang, D. M.

Publications and source records attributed to Yang, D. M..

5 recordsLinked to original sources

bioRxiv2025

N-Power AI: A Specialized Agent Framework for Automated Sample Size and Power Analysis in Clinical Trial Design

BackgroundSample size and power analysis are essential in biomedical research and investigations, particularly in clinical trial design, as they ensure sufficient statistical power to detect meaningful effects. However, the complexity of these calculations often requires specialized statistical expertise, making the process inconvenient and limiting accessibility for researchers during early-stage study planning. MethodsWe developed N-Power AI, an agentic framework leveraging large language models (LLMs) to perform sample size and power calculations across diverse study designs. The framework consists of three specialized agents: the Function Agent, a perception and reasoning module that identifies appropriate statistical tests and corresponding R functions; the Calculation Agent, an action module that extracts parameters and executes precise computations; and the Reporting Agent, a presentation module that generates comprehensive, downloadable reports. N-Power AI and advanced LLMs (e.g., GPT-o1, Claude 3.5, Gemini 1.5 Pro) were evaluated against ground truths from statistical software (R) across six common clinical trial scenarios. ResultsDirect LLM outputs showed significant deviations from ground-truth values, particularly in complex scenarios like the Chi-Square Test and Cox Proportional Hazards Model. In contrast, N-Power AI achieved 100% agreement with ground truths across all scenarios. This accuracy is attributed to the Function Agents correct selection of statistical methods, the Calculation Agents accurate computations, and the Reporting Agents ability to produce clear and comprehensive summaries. ConclusionN-Power AI automates sample size and power analysis, offering an effective, efficient, and accessible solution for early-stage study planning. While human expertise remains crucial for high-level statistical planning, N-Power AI enhances accessibility and efficiency, streamlining the analysis process to generate reliable and reproducible results for a wide range of research scenarios.

bioinformatics↗

bioRxiv2024

An Artificial Intelligence Model for Profiling the Landscape of Antigen-binding Affinities of Massive BCR Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in cancers and other biological contexts. The capability to profile the landscape of antigen-binding affinities of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for detecting antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of binding between antibodies and antigens that can be scaled to high-throughput sequencing data. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data to predict the landscape of the antigen-binding affinities of the BCRs. We found that extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of immune-checkpoint inhibitor (ICI) treatment response. On the other hand, we found that, during immune-related adverse events (irAEs) caused by ICI treatment, humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. Overall, we developed a powerful tool for elucidating the antigen binding landscape of BCR repertoires, and it shall also propell antibody-centric translational applications for cancers and other diseases.

immunology↗

bioRxiv2023

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

immunology↗

bioRxiv2022

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research; however, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedure and produces massive data that capture tumor histological details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nuclei segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nuclei segmentation and TME quantification within image patches; however, existing algorithms are computationally intensive and time-consuming for WSI analysis. In this study, we present Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nuclei segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing methods for WSI analysis in nuclei detection and classification accuracy, as well as computation time.

pathology↗

bioRxiv2019

Computational staining of pathology images to study tumor microenvironment in lung cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally \"stain\" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.\n\nA Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.\n\nThis study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

bioinformatics↗