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AI-Aided Single Cell Analysis Services

CD ComputaBio offers AI-aided single-cell analysis services to provide comprehensive insights into the heterogeneity and complexity of cellular systems. With cutting-edge artificial intelligence technologies and advanced single-cell analysis methods, we strive to help our clients gain a deeper understanding of cellular processes at the single-cell level. Our services are designed to meet the growing demand for high-throughput and high-resolution single-cell analysis, allowing for a more detailed and accurate examination of complex biological systems.

Which Areas of Single-cell Analysis Benefit from Machine Learning?

  • Disease Prediction
    The rapid development of single-cell sequencing analysis provides a more comprehensive overview of the genome, transcriptome, and epigenomic heterogeneity of tumor subpopulations. Compared with traditional bulk sequencing analysis, single cells are helpful for disease prediction.
  • Drug Response Prediction
    Deep learning models successfully extract features from complex large-scale sequence data to predict drug response. By extracting high-dimensional features through multi-layer perceptrons, deep learning models can infer drug target interactions and predict drug resistance.
  • Prediction of Drug Sensitivity at the Single-cell Level
    A detailed understanding of drug sensitivity at the single-cell level can guide the development of smaller combination therapies that kill tumor cells but not healthy cells. Analyzing specific features of treatment response can help discover new drug targets and design drugs for specific therapeutic responses.

Our Services

Fig 1: AI-aided single cell analysis services

Copy Number Variation (CNV) Analysis

Our CNV analysis service utilizes advanced AI algorithms to accurately quantify and analyze copy number variations within single cells.

Fig 2: AI-aided single cell analysis services

Single-Cell Heterogeneity Analysis

Our single-cell heterogeneity analysis service leverages AI-driven techniques to comprehensively characterize the heterogeneity present within a cell population.

Fig 3: AI-aided single cell analysis services

Single-Cell Trajectory Analysis

Our single-cell trajectory analysis services reconstruct and analyze the developmental trajectories of individual cells within a population.

Fig 4: Increased proliferation and recruitment of monocytes.

Phylogenetic Inference

By analyzing genetic and phenotypic data at the single-cell level, we can identify clonal expansions, genetic diversity, and evolutionary dynamics, providing valuable insights into tumor evolution, immune cell development, and microbial ecology.

Fig 5: Strategies for monitoring cell–cell interactions

Cell-Cell Interactions Analysis

Our cell-cell interactions analysis service utilizes AI-driven approaches to model and analyze the interactions between individual cells within a complex cellular network.

Fig 6: AI-aided single cell analysis services

Integration of Single-Cell Data

Our integration of single-cell data service integrates and analyzes multi-omic data from single cells, including genomics, transcriptomics, proteomics, and epigenomics.

Our comprehensive range of service items and analysis methods, combined with our streamlined service workflow, ensure that our clients receive high-quality, accurate, and actionable insights into the cellular processes under study. Contact us today to learn more about our AI-aided single-cell analysis services and how we can help advance your research and applications in the field of single-cell analysis.

References:

  • Bechtel T J, Reyes-Robles T, Fadeyi O O, et al. Strategies for monitoring cell–cell interactions[J]. Nature Chemical Biology, 2021, 17(6): 641-652.
  • Wu S, Li X, Hong F, et al. Integrative analysis of single-cell transcriptomics reveals age-associated immune landscape of glioblastoma[J]. Frontiers in Immunology, 2023, 14: 1028775.

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