Artificial intelligence assists precision medicine in cancer treatment

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 12

Published: Jan. 4, 2023

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of same drugs or surgical methods in patients with tumor may have different curative effects, leading need for more accurate treatment tumors and personalized treatments patients. The precise essential, which renders obtaining an in-depth understanding changes that undergo urgent, including their genes, proteins cancer cell phenotypes, order develop targeted strategies Artificial intelligence (AI) based on big data can extract hidden patterns, important information, corresponding knowledge behind enormous amount data. For example, ML deep learning subsets AI be used mine deep-level information genomics, transcriptomics, proteomics, radiomics, digital pathological images, other data, make clinicians synthetically comprehensively understand tumors. In addition, find new biomarkers from assist screening, detection, diagnosis, prognosis prediction, so as providing best individual improving clinical outcomes.

Language: Английский

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A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Aug. 9, 2022

A fused method using a combination of multi-omics data enables comprehensive study complex biological processes and highlights the interrelationship relevant biomolecules their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing generated from large number samples.

Language: Английский

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The Rise of Gastrointestinal Cancers as a Global Phenomenon: Unhealthy Behavior or Progress?

International Journal of Environmental Research and Public Health, Journal Year: 2023, Volume and Issue: 20(4), P. 3640 - 3640

Published: Feb. 18, 2023

The overall burden of cancer is rapidly increasing worldwide, reflecting not only population growth and aging, but also the prevalence spread risk factors. Gastrointestinal (GI) cancers, including stomach, liver, esophageal, pancreatic, colorectal represent more than a quarter all cancers. While smoking alcohol use are factors most commonly associated with development, growing consensus includes dietary habits as relevant for GI Current evidence suggests that socioeconomic development results in several lifestyle modifications, shifts from local traditional diets to less-healthy Western diets. Moreover, recent data indicate increased production consumption processed foods underlies current pandemics obesity related metabolic disorders, which directly or indirectly emergence various chronic noncommunicable conditions However, environmental changes restricted patterns, unhealthy behavioral features should be analyzed holistic view lifestyle. In this review, we discussed epidemiological aspects, gut dysbiosis, cellular molecular characteristics cancers explored impact behaviors, diet, physical activity on developing context progressive societal changes.

Language: Английский

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Multimodal data integration for oncology in the era of deep neural networks: a review

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: July 25, 2024

Cancer research encompasses data across various scales, modalities, and resolutions, from screening diagnostic imaging to digitized histopathology slides types of molecular clinical records. The integration these diverse for personalized cancer care predictive modeling holds the promise enhancing accuracy reliability screening, diagnosis, treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short capturing complex heterogeneous nature data. advent deep neural networks has spurred development sophisticated multimodal fusion techniques capable extracting synthesizing information disparate sources. Among these, Graph Neural Networks (GNNs) Transformers have emerged as powerful tools learning, demonstrating significant success. This review presents foundational principles learning including oncology taxonomy strategies. We delve into recent advancements in GNNs oncology, spotlighting key studies their pivotal findings. discuss unique challenges such heterogeneity complexities, alongside opportunities it a more nuanced comprehensive understanding cancer. Finally, we present some latest pan-cancer By surveying landscape our goal is underline transformative potential Transformers. Through technological methodological innovations presented this review, aim chart course future promising field. may be first that highlights current state applications using transformers, sources, sets stage evolution, encouraging further exploration care.

Language: Английский

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A review of cancer data fusion methods based on deep learning

Information Fusion, Journal Year: 2024, Volume and Issue: 108, P. 102361 - 102361

Published: March 20, 2024

Language: Английский

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A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Briefings in Functional Genomics, Journal Year: 2024, Volume and Issue: 23(5), P. 549 - 560

Published: April 10, 2024

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed this context over years. This review aims comprehensively summarize and categorize these advancements, focusing on integration of multi-omics data, which includes genomics, transcriptomics, proteomics metabolomics, alongside clinical data. We discuss various machine techniques computational methodologies used for integrating distinct omics datasets provide valuable insights into their application. The emphasizes both challenges opportunities present integration, medicine patient stratification, offering practical recommendations method selection scenarios. Recent advances deep network-based are also explored, highlighting potential harmonize information layers. Additionally, we roadmap oncology, outlining advantages, implementation difficulties. Hence offers thorough overview current literature, providing researchers with particularly oncology. Contact: [email protected].

Language: Английский

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Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 24(2)

Published: Jan. 25, 2023

Abstract Due to the high heterogeneity and complexity of cancers, patients with different cancer subtypes often have distinct groups genomic clinical characteristics. Therefore, discovery identification are crucial diagnosis, prognosis treatment. Recent technological advances accelerated increasing availability multi-omics data for subtyping. To take advantage complementary information from data, it is necessary develop computational models that can represent integrate layers into a single framework. Here, we propose decoupled contrastive clustering method (Subtype-DCC) based on integration identify subtypes. The idea learning introduced deep neural networks learn clustering-friendly representations. Experimental results demonstrate superior performance proposed Subtype-DCC model in identifying over currently available state-of-the-art methods. strength also supported by survival analysis.

Language: Английский

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Challenges and best practices in omics benchmarking

Nature Reviews Genetics, Journal Year: 2024, Volume and Issue: 25(5), P. 326 - 339

Published: Jan. 12, 2024

Language: Английский

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Molecular similarity: Theory, applications, and perspectives

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(2), P. 100077 - 100077

Published: Aug. 31, 2024

Molecular similarity pervades much of our understanding and rationalization chemistry. This has become particularly evident in the current data-intensive era chemical research, with measures serving as backbone many Machine Learning (ML) supervised unsupervised procedures. Here, we present a discussion on role molecular drug design, space exploration, "art" generation, representations, more. We also discuss more recent topics similarity, like ability to efficiently compare large libraries.

Language: Английский

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A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Heliyon, Journal Year: 2024, Volume and Issue: 10(3), P. e25369 - e25369

Published: Feb. 1, 2024

In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power algorithmic development position Machine Learning (ML) Deep (DL) as crucial players predicting Leukemia, blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this deluge. This study introduces Leukemia diagnosis approach, analyzing accuracy ML DL algorithms. techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), methods such Recurrent Neural Networks (RNN) Feedforward (FNN) are compared. GB achieved 97 % ML, while RNN outperformed by achieving 98 DL. approach filters unclassified effectively, demonstrating the significance leukemia prediction. The testing validation was based 17 different features patient age, sex, mutation type, treatment methods, chromosomes, others. Our compares techniques chooses best technique that gives optimum results. emphasizes implications high-throughput technology healthcare, offering

Language: Английский

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