Applications of multi‐omics analysis in human diseases

MedComm, Год журнала: 2023, Номер 4(4)

Опубликована: Июль 31, 2023

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell proteomics and metabolomics, spatial so on, which play a great role in promoting study human diseases. Most current reviews focus on describing development multi-omics technologies, data integration, particular disease; however, few them provide comprehensive systematic introduction multi-omics. This review outlines existing technical categories multi-omics, cautions for experimental design, focuses integrated analysis methods especially approach machine learning deep integration corresponding tools, medical researches (e.g., cancer, neurodegenerative diseases, aging, drug target discovery) as well open-source tools databases, finally, discusses challenges future directions precision medicine. With algorithms, important disease research, also provided detailed introduction. will guidance researchers, who are just entering into research.

Язык: Английский

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Fatty acid metabolism reprogramming in ccRCC: mechanisms and potential targets

Nature Reviews Urology, Год журнала: 2022, Номер 20(1), С. 48 - 60

Опубликована: Окт. 3, 2022

Язык: Английский

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Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Advanced Science, Год журнала: 2024, Номер unknown

Опубликована: Июль 3, 2024

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting outcomes, to also include reducing the risk comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery new markers for various health conditions. Integration wearables with intelligent frameworks represents ground-breaking innovations automation operations, conducting advanced large-scale data analysis, generating predictive models, facilitating remote guided clinical decision-making. These substantially alleviate socioeconomic burdens, creating a paradigm shift diagnostics, revolutionizing medical assessments technology development. This review explores critical topics recent progress development 1) systems solutions physiological monitoring, as well 2) discussing current trends adoption smart technologies within settings developing biological assays, ultimately 3) exploring utilities platforms discovery. Additionally, translation from research labs broader applications. It addresses associated risks, biases, challenges widespread Artificial Intelligence (AI) integration diagnostics systems, while systematically outlining potential prospects, challenges, opportunities.

Язык: Английский

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A review of cancer data fusion methods based on deep learning

Information Fusion, Год журнала: 2024, Номер 108, С. 102361 - 102361

Опубликована: Март 20, 2024

Язык: Английский

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Global burden of gynaecological cancers in 2022 and projections to 2050

Journal of Global Health, Год журнала: 2024, Номер 14

Опубликована: Авг. 16, 2024

Abstract Background The incidence and mortality of gynaecological cancers can significantly impact women's quality life increase the health care burden for organisations globally. objective this study was to evaluate global inequalities in 2022, based on Global Cancer Observatory (GLOBOCAN) 2022 estimates. future (GCs) 2050 also projected. Methods Data regarding total cases deaths related cancer, as well pertaining different subtypes GCs, gathered from GLOBOCAN database year 2022. Predictions number were derived demographic projections, categorised by world region Human Development Index (HDI). Results In there 1 473 427 new GCs 680 372 deaths. gynecological cancer reached 30.3 per 100 000, rate hit 13.2 000. age-standardised Eastern Africa is higher than 50 whereas Northern 17.1 highest rates found East (ASMR (age-standardised rates) 35.3 000) lowest Australia New Zealand 8.1 000). These are endemic areas HIV HPV. Very High HDI countries had with ASIR 34.8 low second rate, an 33.0 Eswatini (105.4 000; 71.1 Yemen (5.8 4.4 If current trends morbidity maintained, female reproductive tract tumours projected over next two decades. Conclusions accounted globally, significant regional disparities rates. observed very high HDI, recording most severe statistics. continue, expected rise decades, highlighting urgent need effective interventions.

Язык: Английский

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Nanoparticles in gynecologic cancers: a bibliometric and visualization analysis

Frontiers in Oncology, Год журнала: 2025, Номер 14

Опубликована: Янв. 8, 2025

Gynecological cancers are characterized by uncontrolled cell proliferation within the female reproductive organs. These pose a significant threat to women's health, impacting life expectancy, quality of life, and fertility. Nanoparticles, with their small size, large surface area, high permeability, have become key focus in targeted cancer therapy. The aim this study is review recent advancements nanoparticles applied gynecologic cancers, providing valuable insights for future research. We retrieved all literature on from Web Science Core Collection (WOSCC) database between January 1, 2004, June 4, 2024. Data analysis visualization were conducted using R software (version 4.4.0), VOSviewer 1.6.19.0), CiteSpace 6.1). A total 2,843 publications 2024 searched. Over past 20 years, there has been increase publications. leading countries institutions terms productivity China Chinese Academy Sciences. most prolific author co-cited Sood, K Siegel, Rl. top journals International Journal Nanomedicine (n=97), followed ACS Applied Materials & Interfaces (n=72) Chemistry B (n=53). Keyword shows current research focuses two main areas: application drug delivery broader applications cancers. Future will likely "silver nanoparticles," "gold "green synthesis." decades, rapidly advanced field Research primarily focused applications. trends point toward optimizing synthesis techniques advancing preclinical studies clinical applications, particularly silver gold nanoparticles. findings provide scientific researchers.

Язык: Английский

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A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Diagnostics, Год журнала: 2023, Номер 13(10), С. 1703 - 1703

Опубликована: Май 11, 2023

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to often vague inconsistent initial symptoms. Current diagnostic methods, such biomarkers, biopsy, imaging tests, face limitations, including subjectivity, inter-observer variability, extended testing times. This study proposes novel convolutional neural network (CNN) algorithm for predicting diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on histopathological image dataset, divided into training validation subsets augmented before training. The model achieved remarkable accuracy 94%, with 95.12% cancerous cases correctly identified 93.02% healthy cells accurately classified. significance lies overcoming challenges associated human expert examination, higher misclassification rates, analysis presents more accurate, efficient, reliable approach cancer. Future research should explore recent advances field enhance effectiveness proposed method further.

Язык: Английский

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Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

American Society of Clinical Oncology Educational Book, Год журнала: 2023, Номер 43

Опубликована: Май 1, 2023

Recently, a wide spectrum of artificial intelligence (AI)–based applications in the broader categories digital pathology, biomarker development, and treatment have been explored. In domain these included novel analytical strategies for realizing new information derived from standard histology to guide selection development predict response. therapeutics, AI-driven drug target discovery, design repurposing, combination regimen optimization, modulated dosing, beyond. Given continued advances that are emerging, it is important develop workflows seamlessly combine various segments AI innovation comprehensively augment diagnostic interventional arsenal clinical oncology community. To overcome challenges remain with regard ideation, validation, deployment oncology, recommendations toward bringing this workflow fruition also provided clinical, engineering, implementation, health care economics considerations. Ultimately, work proposes frameworks can potentially integrate domains sustainable adoption practice-changing by community drive improved patient outcomes.

Язык: Английский

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A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Journal of Biomedical Informatics, Год журнала: 2023, Номер 142, С. 104373 - 104373

Опубликована: Апрель 27, 2023

Cancer is the second leading cause of death globally, trailing only heart disease. In United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, success rate drug development remains less than 10%, making disease particularly challenging. This low largely attributed to complex poorly understood nature etiology. Therefore, it critical find alternative approaches understanding biology developing effective treatments. One such approach repurposing, which offers a shorter timeline lower costs while increasing likelihood success. this review, we provide comprehensive analysis computational biology, including systems multi-omics, pathway analysis. Additionally, examine use these methods repurposing in cancer, databases tools that are used research. Finally, present case studies discussing their limitations offering recommendations future research area.

Язык: Английский

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A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

BMC Bioinformatics, Год журнала: 2023, Номер 24(1)

Опубликована: Май 15, 2023

There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework oncology. However, most direct applications DL will deliver models with limited transparency and explainability, which constrain their deployment biomedical settings.

Язык: Английский

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