Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization

Cancer Biology and Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: Jan. 2, 2025

Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In AI can process vast amounts genomic multi-omic data to identify biomarkers associated with immunotherapy responses disease prognosis, thus providing strong support for personalized treatments. analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed (PET/CT) images discover tumor heterogeneity, treatment response, progression, thereby enabling non-invasive, real-time assessments therapy. Pathomics leverages deep analysis digital pathology images, uncover subtle changes tissue microenvironments, cellular characteristics, morphological features, offer unique insights into response prediction biomarker discovery. These AI-driven technologies not only enhance speed, accuracy, robustness discovery but also improve precision, personalization, effectiveness clinical treatments, are driving a shift empirical medicine. Despite challenges such as quality, model interpretability, integration multi-modal data, privacy protection, ongoing advancements AI, coupled interdisciplinary collaboration, poised further AI’s roles prediction. improvements expected lead more accurate, strategies ultimately better patient outcomes, marking significant step forward evolution

Language: Английский

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Enhancing prime editing in hematopoietic stem and progenitor cells by modulating nucleotide metabolism

Nature Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: May 28, 2024

Language: Английский

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TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

Genome biology, Journal Year: 2024, Volume and Issue: 25(1)

Published: June 6, 2024

Abstract Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning incomplete omics inference. This enhances sample empowers various downstream oncology tasks with datasets. By employing interpretable learning, characterize contributions of distinct features to clinical outcomes. The TMO-Net serves as versatile framework cross-modal oncology, paving way tumor omics-specific foundation models.

Language: Английский

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Foundation models in bioinformatics

National Science Review, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and successfully addressed many historical challenges, such as pre-training frameworks, model evaluation interpretability. FMs demonstrate notable proficiency managing large-scale, unlabeled datasets, because experimental procedures are costly labor intensive. In various downstream tasks, have consistently achieved noteworthy results, demonstrating high levels accuracy representing biological entities. A new era computational biology been ushered by application FMs, focusing on both general specific issues. this review, we introduce recent advancements employed a variety including genomics, transcriptomics, proteomics, drug discovery single-cell analysis. Our aim is to assist scientists selecting appropriate bioinformatics, according four types: language vision graph multimodal FMs. addition understanding molecular landscapes, AI technology can establish theoretical practical for continued innovation biology.

Language: Английский

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Machine learning integrative approaches to advance computational immunology

Genome Medicine, Journal Year: 2024, Volume and Issue: 16(1)

Published: June 11, 2024

Abstract The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification encompass more detailed view cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements multiple components—transcriptome, proteome, chromatin, epigenetic modifications metabolites—within single including spatial contexts within tissues. This led generation complex multiscale datasets that can include multimodal from same cells or mix paired unpaired modalities. Modern machine learning (ML) techniques for integration “omics” data without need extensive independent modelling each modality. review focuses recent ML integrative approaches applied immunological studies. We highlight importance methods creating unified representation collections, particularly profiling technologies. Finally, we discuss challenges holistic how they will be instrumental development common coordinate framework studies, thereby accelerating research enabling discoveries computational immunology field.

Language: Английский

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A joint analysis of single cell transcriptomics and proteomics using transformer

npj Systems Biology and Applications, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 2, 2025

CITE-seq provides a powerful method for simultaneously measuring RNA and protein expression at the single-cell level. The integrated analysis of in identical cells is crucial revealing cellular heterogeneity. However, high experimental costs associated with limit its widespread application. In this paper, we propose scTEL, deep learning framework based on Transformer encoder layers, to establish mapping from sequenced unobserved same cells. This computation-based approach significantly reduces sequencing. We are now able predict using sequencing (scRNA-seq) data, which well-established available lower cost. Moreover, our scTEL model offers unified integrating multiple datasets, addressing challenge posed by partial overlap panels across different datasets. Empirical validation public datasets demonstrates outperforms existing methods.

Language: Английский

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Multidimensional single-cell analysis: Diverse strategies and emerging applications in the life sciences

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118170 - 118170

Published: Jan. 1, 2025

Language: Английский

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Benchmarking single-cell cross-omics imputation methods for surface protein expression

Genome biology, Journal Year: 2025, Volume and Issue: 26(1)

Published: March 4, 2025

Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions heterogeneity. However, high costs technical complexity protocols like CITE-seq REAP-seq constrain large-scale dataset generation. To overcome this limitation, protein data imputation methods emerged to predict abundances from scRNA-seq data. We present a comprehensive benchmark twelve state-of-the-art across eleven datasets six scenarios. Our analysis evaluates methods' accuracy, sensitivity training size, robustness experiments, usability terms running time, memory usage, popularity, user-friendliness. With experiments diverse scenarios evaluation framework results, our study offers valuable performance applicability research. Based on Seurat v4 (PCA) v3 demonstrate exceptional performance, promising avenues for further research omics.

Language: Английский

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Network-based multi-omics integrative analysis methods in drug discovery: a systematic review

BioData Mining, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 28, 2025

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate data, systematic evaluation and comparison these remain challenging. This review aims analyze approaches for evaluate their applications in We conducted a comprehensive literature (2015-2024) on discovery, categorized into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, inference models. also discussed the three scenario including target identification, response prediction, repurposing, finally evaluated performance by highlighting advantages limitations specific applications. shown promise challenges computational scalability, integration, biological interpretation. Future developments should focus incorporating temporal spatial dynamics, improving model interpretability, establishing standardized frameworks.

Language: Английский

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New horizons at the interface of artificial intelligence and translational cancer research

Cancer Cell, Journal Year: 2025, Volume and Issue: 43(4), P. 708 - 727

Published: April 1, 2025

Language: Английский

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