AI in Genomic Data Analysis for Drug Development DOI Open Access

Shanavaz Mohammed

IJIREEICE, Год журнала: 2024, Номер 12(7)

Опубликована: Июнь 20, 2024

The drug development industry has greatly benefitted from the application of artificial intelligence (AI) in genomic data analysis.Genomic is useful understanding diseases genetics as well making drugs to counter diseases.However, this process quite challenging it requires a lot and analysis which makes complex.This aspect study made be time-consuming resource intensive necessitated better more efficient tool for analysis.AI advanced machine learning deep algorithms that enable an effective option process.AI offers great solutions processing large sets with accurate outcomes.As such, benefited reduced costs, saving time, research data.It also ensured therapies are tailor-made patients, including appropriate treatments specific genetic profile, ensuring treatment outcomes.It therefore without doubt been transformative force continues facilitate innovation advancement process.

Язык: Английский

Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy DOI Creative Commons
Hossein Azadinejad, Mohammad Farhadi Rad, Ahmad Shariftabrizi

и другие.

Diagnostics, Год журнала: 2025, Номер 15(3), С. 397 - 397

Опубликована: Фев. 6, 2025

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, durable treatment, making it effective in cancers resistant conventional therapies. Advances artificial intelligence (AI) present opportunities enhance RIT by improving precision, efficiency, personalization. AI plays critical role patient selection, planning, dosimetry, response assessment, while also contributing drug design classification. review explores the integration of into RIT, emphasizing its potential optimize entire process advance personalized care.

Язык: Английский

Процитировано

3
Impact and Challenges of Artificial Intelligence Integration in the African Health Sector: A Review DOI Open Access
Elijah Kolawole Oladipo, Stephen Feranmi Adeyemo,

Glory Jesudara Oluwasanya

и другие.

Trends in Medical Research, Год журнала: 2024, Номер 19(1), С. 220 - 235

Опубликована: Июнь 11, 2024

Artificial intelligence has proven to be a game-changing force in health sectors throughout Africa offering prospects for significant development.In sub-Saharan Africa, using AI healthcare, especially areas with limited resources, holds valuable promise transforming and improving healthcare.This article takes an excellent look at how is being integrated into the African sector, as well examining policy frameworks, challenges future possibilities.This begins by giving overview of highlighting groundbreaking impact technologies combating addressing healthcare that occur within countries.Ranges from mobile-based diagnostics precision medicine, artificial its potential capabilities diagnosing, treating operations providing solutions resource constraints accessibility challenges.However, despite these advancements, there are still obstacles such infrastructure limitations, concerns about data privacy gaps professionals' training hinder realization AI's envisions where adoption fully incorporated community initiatives enhanced access services betterment across countries.While barriers like unequal persist, need governments stakeholders prioritize digital catalysts sector Africa.

Язык: Английский

Процитировано

8
The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research DOI

Bugude Laxmi,

P. Uma Maheswari Devi,

Naveen Thanjavur

и другие.

Current Microbiology, Год журнала: 2024, Номер 81(8)

Опубликована: Июнь 21, 2024

Язык: Английский

Процитировано

7
Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery DOI Creative Commons

Yingcan Li,

Yu Shen,

Yezi Cai

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

1
Artificial intelligence in vaccine research and development: an umbrella review DOI Creative Commons
Rabie Adel El Arab,

Mansour Alkhunaizi,

Yousef N. Alhashem

и другие.

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Май 8, 2025

The rapid development of COVID-19 vaccines highlighted the transformative potential artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months. Nevertheless, specific roles and effectiveness AI enhancing vaccine research, development, distribution, acceptance remain dispersed across various reviews, underscoring need for a unified synthesis. We conducted an umbrella review consolidate evidence on AI's contributions discovery, optimization, clinical testing, supply-chain logistics, public acceptance. Five databases were systematically searched up January 2025 systematic, scoping, narrative, as well meta-analyses explicitly focusing contexts. Quality assessments performed using ROBIS AMSTAR 2 tools evaluate risk bias methodological rigor. Among 27 traditional machine learning approaches-random forests, support vector machines, gradient boosting, logistic regression-dominated tasks antigen discovery epitope prediction supply‑chain optimization. Deep architectures, including convolutional recurrent neural networks, generative adversarial variational autoencoders, proved instrumental multiepitope design adaptive trial simulations. AI‑driven multi‑omic integration accelerated mapping, shrinking by months, while predictive analytics optimized manufacturing workflows operations (including temperature‑controlled, "cold‑chain" logistics). Sentiment analysis conversational demonstrated promising capabilities real‑time monitoring attitudes tailored communication address hesitancy. Nonetheless, persistent challenges emerged-data heterogeneity, algorithmic bias, limited regulatory frameworks, ethical concerns over transparency equity. These findings illustrate lifecycle but underscore that translating promise into practice demands five targeted action areas: robust data governance multi‑omics consortia harmonize share high‑quality datasets; comprehensive frameworks featuring transparent model explainability, standardized performance metrics, interdisciplinary ethics committees ongoing oversight; adoption designs simulations enable safety silico process modeling; AI‑enhanced engagement strategies-such routinely audited chatbots, sentiment dashboards, culturally messaging-to mitigate hesitancy; concerted focus global equity pandemic preparedness through capacity building, digital infrastructure expansion, routine audits, sustained funding low‑resource settings. This confirms pivotal role efficacy safety, bolstering Realizing these benefits requires not only investments stakeholder also documentation, oversight, audits. Moreover, bridging gap real‑world impact large‑scale validation studies methods can accommodate heterogeneous evidence, ensuring innovations deliver equitable health outcomes reinforce preparedness.

Язык: Английский

Процитировано

1
High-performance computing in healthcare: An automatic literature analysis perspective DOI
Jieyi Li, Shuai Wang, Stevan Rudinac

и другие.

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 2, 2024

Язык: Английский

Процитировано

6
Current Treatments, Emerging Therapeutics, and Natural Remedies for Inflammatory Bowel Disease DOI Creative Commons
Karma Yeshi, Tenzin Jamtsho, Phurpa Wangchuk

и другие.

Molecules, Год журнала: 2024, Номер 29(16), С. 3954 - 3954

Опубликована: Авг. 21, 2024

Inflammatory bowel disease (IBD) is a chronic, lifelong disorder characterized by inflammation of the gastrointestinal (GI) tract. The exact etiology IBD remains incompletely understood due to its multifaceted nature, which includes genetic predisposition, environmental factors, and host immune response dysfunction. Currently, there no cure for IBD. This review discusses available treatment options challenges they present. Importantly, we examine emerging therapeutics, such as biologics immunomodulators, that offer targeted strategies While many patients do not respond adequately most biologics, recent clinical trials combining with small-molecule drugs (SMDs) have provided new insights into improving landscape. Furthermore, numerous novel specific therapeutic targets been identified. high cost poses significant barrier treatment, but this challenge may be alleviated development more affordable biosimilars. Additionally, point-of-care protein biomarkers from serum plasma are showing potential enhancing precision diagnosis prognosis. Several natural products (NPs), including crude extracts, small molecules, peptides, demonstrated promising anti-inflammatory activity in high-throughput screening (HTS) systems advanced artificial intelligence (AI)-assisted platforms, molecular docking ADMET prediction. These platforms advancing search alternative therapies derived sources, potentially leading safer fewer side effects.

Язык: Английский

Процитировано

6
Artificial Intelligence Applications in Smart Healthcare: A Survey DOI Creative Commons
Xian Gao, Peixiong He, Yi Zhou

и другие.

Future Internet, Год журнала: 2024, Номер 16(9), С. 308 - 308

Опубликована: Авг. 27, 2024

The rapid development of AI technology in recent years has led to its widespread use daily life, where it plays an increasingly important role. In healthcare, been integrated into the field develop new domain smart healthcare. opportunities and challenges coexist. This article provides a comprehensive overview past developments progress this area. First, we summarize definition characteristics Second, explore that brings healthcare from macro perspective. Third, categorize specific applications ten domains discuss their technological foundations individually. Finally, identify key these face existing solutions for each.

Язык: Английский

Процитировано

5
Artificial Intelligence (AI) and Machine Learning (ML) Implemented Drug Delivery Systems: A paradigm shift in the Pharmaceutical industry DOI Creative Commons
Goutam Kumar Jena, Chinam Niranjan Patra, Sruti Jammula

и другие.

Journal of Bio-X Research, Год журнала: 2024, Номер 7

Опубликована: Янв. 1, 2024

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the pharmaceutical industry, particularly in drug development delivery. These technologies enable precision medicine by analyzing extensive datasets to optimize formulations predict patient responses. AI-driven models enhance nanoparticle-based carriers, improving their stability, bioavailability, targeting accuracy. ML also facilitates real-time monitoring adaptive control of release, ensuring better therapeutic outcomes. This review explores integration AI delivery, highlighting potential accelerate development, reduce costs, advance personalized medicine.

Язык: Английский

Процитировано

5
Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives DOI Open Access
Amiya Kumar Tripathy, Akshata Y. Patne, Subhra Mohapatra

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(22), С. 12368 - 12368

Опубликована: Ноя. 18, 2024

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, data processing. ML enhances nanotechnology by facilitating the processing of dataset nanomaterial synthesis, characterization, optimization nanoscale properties. Conversely, improves speed efficiency computing power, which is crucial for algorithms. Although capabilities still their infancy, a review research literature provides insights into exciting frontiers these suggests that integration can be transformative. Future directions include developing tools manipulating nanomaterials ensuring ethical unbiased collection models. This emphasizes importance coevolution technologies mutual reinforcement to advance scientific societal goals.

Язык: Английский

Процитировано

4