Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence DOI

Aakanksha Biswas,

Aditi Kumari,

D. S. Gaikwad

et al.

OMICS A Journal of Integrative Biology, Journal Year: 2023, Volume and Issue: 27(12), P. 550 - 569

Published: Dec. 1, 2023

With climate emergency, COVID-19, and the rise of planetary health scholarship, binary human ecosystem has been deeply challenged. The interdependence nonhuman animal is increasingly acknowledged paving way for new frontiers in integrative biology. convergence genomics health, bioinformatics, agriculture, artificial intelligence (AI) ushered a era possibilities applications. However, sheer volume genomic/multiomics big data generated also presents formidable sociotechnical challenges extracting meaningful biological, ecological insights. Over past few years, AI-guided bioinformatics emerged as powerful tool managing, analyzing, interpreting complex biological datasets. advances AI, particularly machine learning deep learning, have transforming fields genomics, agriculture. This article aims to unpack explore range that result from such transdisciplinary integration, emphasizes its radically transformative potential health. integration these disciplines driving significant advancements precision medicine personalized care. an unprecedented opportunity deepen our understanding systems advance well-being all life ecosystems. Notwithstanding mind sociotechnical, ethical, critical policy challenges, multiomics, agriculture with opens up vast opportunities transnational collaborative efforts, sharing, analysis, valorization, interdisciplinary innovations sciences

Language: Английский

Applications of multi‐omics analysis in human diseases DOI Creative Commons

Chongyang Chen,

Jing Wang,

Donghui Pan

et al.

MedComm, Journal Year: 2023, Volume and Issue: 4(4)

Published: July 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.

Language: Английский

Citations

182
Fatty acid metabolism reprogramming in ccRCC: mechanisms and potential targets DOI
Sze Kiat Tan, Helen Y. Hougen, Jaime R. Merchan

et al.

Nature Reviews Urology, Journal Year: 2022, Volume and Issue: 20(1), P. 48 - 60

Published: Oct. 3, 2022

Language: Английский

Citations

82
Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies DOI Creative Commons
Fatemeh Haghayegh,

Alireza Norouziazad,

Elnaz Haghani

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: unknown

Published: July 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.

Language: Английский

Citations

25
A review of cancer data fusion methods based on deep learning DOI
Yuxin Zhao, Xiaobo Li, Changjun Zhou

et al.

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

Published: March 20, 2024

Language: Английский

Citations

19
Nanoparticles in gynecologic cancers: a bibliometric and visualization analysis DOI Creative Commons

Yunzhe Zhou,

Lizhang Chen, Tingting Wang

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 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.

Language: Английский

Citations

2
5-Hydroxymethylcytosine modifications in circulating cell-free DNA: frontiers of cancer detection, monitoring, and prognostic evaluation DOI Creative Commons

Danjun Song,

Zhou Zhang,

Jiaping Zheng

et al.

Biomarker Research, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 7, 2025

Abstract Developing accurate, clinically convenient, and non-invasive methods for early cancer detection, monitoring, prognosis assessment is essential improving patient survival rates, enhancing quality of life, reducing the socioeconomic burden associated with cancer. This goal critical in precision oncology. Genetic epigenetic alterations circulating cell-free DNA (cfDNA) have emerged as transformative tools advancing outcomes. Among these, 5-hydroxymethylcytosine (5hmC) modifications cfDNA stand out promising markers, offering insights into initiation, progression, metastasis, across various types, such lung cancer, colorectal hepatocellular carcinoma. review comprehensively explores biology sequencing methodologies 5hmC, emphasizing their potential screening, diagnosis, treatment prognostic assessment. It highlights recent advancements cfDNA-derived 5hmC signatures’ applications, addressing strengths limitations context clinical translation. Furthermore, this outlines key challenges future directions integrating routine practice, facilitating personalized management.

Language: Английский

Citations

2
A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women DOI Creative Commons
Blessed Ziyambe, Abid Yahya, Tawanda Mushiri

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(10), P. 1703 - 1703

Published: May 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.

Language: Английский

Citations

37
Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment DOI Open Access
Kirthika Senthil Kumar, Vanja Mišković, Agata Blasiak

et al.

American Society of Clinical Oncology Educational Book, Journal Year: 2023, Volume and Issue: 43

Published: May 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.

Language: Английский

Citations

36
A systematic review of computational approaches to understand cancer biology for informed drug repurposing DOI Creative Commons
Faheem Ahmed,

Anupama Samantasinghar,

Afaque Manzoor Soomro

et al.

Journal of Biomedical Informatics, Journal Year: 2023, Volume and Issue: 142, P. 104373 - 104373

Published: April 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.

Language: Английский

Citations

35
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data DOI Creative Commons
Magdalena Wysocka, Oskar Wysocki,

Marie Zufferey

et al.

BMC Bioinformatics, Journal Year: 2023, Volume and Issue: 24(1)

Published: May 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.

Language: Английский

Citations

30