Solutions for Full-Stack AI Multi-Omics

Artificial intelligence (AI) and machine learning technologies are transforming the way life science research is conducted. Within Sequanta’ multi-omics technology platform, AI serves not only as a powerful tool for data analysis but also as a “microscope” and “navigator” for uncovering clinical insights from complex biological information. By integrating genomics, transcriptomics, proteomics, and single-cell spatial and temporal data, AI algorithms overcome the limitations of traditional statistical approaches and enable a transition from “data accumulation” to “knowledge discovery.” These capabilities support accurate identification of efficacy biomarkers in tumor immune microenvironment studies, dynamic tracking of clone-level immune responses in mRNA vaccine development, and the construction of high-dimensional predictive models in proteomic analyses. AI-driven multi-omics approaches not only enhance the efficiency and accuracy of biomarker discovery but also provide intelligent decision support throughout the drug development process, from target validation and patient stratification to efficacy prediction, ultimately transforming multi-omics data into actionable insights for clinical applications.

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Cases

AI-Vax: Intelligent Immune Response Evaluation Platform for mRNA Vaccines — Enabling Clonal-Level Efficacy Assessment and Combination Therapy Guidance

Addressing three key questions in mRNA vaccine development — whether an immune response is generated, which cells contribute to the response, and how immune functions evolve over time — AI-Vax leverages machine learning to enable accurate identification of vaccine-induced antigen-specific T cell responses, characterization of responding clones and their functional states, discovery of dynamic immune evolution patterns, and evaluation of vaccine efficacy to support optimal decision-making.

AI-Immuno: An AI-powered Tumor Immune Microenvironment Analysis Platform — Decoding the Hidden Complexity of Tumors

To accurately evaluate anti-tumor drug response and resistance, traditional RNA-seq primarily provides gene-level expression profiles, while AI-Immuno integrates advanced deconvolution algorithms (such as CIBERSORTx and MCP-counter) and signature scoring systems to perform differential analysis, efficacy prediction, and immune cell subset quantification. This enables comprehensive characterization of key features within the tumor immune microenvironment.

AI-Proteo: An AI-powered Proteomic Biomarker Discovery Engine — Unlocking the “Hidden Code” in Plasma

Proteins are key executors of biological functions. In large-scale cohort studies and clinical trials, identifying truly disease-associated targets from thousands of proteins remains a “needle in a haystack” challenge that is difficult to address using traditional statistical approaches. AI-Proteo leverages machine learning-based dimensionality reduction and feature selection algorithms to deeply analyze multi-platform proteomic datasets, enabling an intelligent workflow from differential protein identification to clinical biomarker translation.

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