Role of Data Mining in Bioinformatics

Chavda, Vivek P.; Sorathiya, Amit; Valu, Disha; Marwadi, Swati

doi:10.1002/9781119654803.ch4

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…There are several prevalent unsupervised tasks, including clustering, dimensionality reduction, visualization, finding association rules, and anomaly detection. Various unsupervised learning tasks can be addressed using popular techniques such as clustering algorithms (e.g., hierarchical clustering, K-means, K-medoids, single linkage, complete linkage, BOTS), association learning algorithms, and feature selection and extraction techniques (e.g., Pearson correlation, principal component analysis) based on the data’s characteristics [ 44 , 45 ]. Unsupervised learning techniques in AI can be valuable for pharmaceutical applications, particularly for exploratory analysis, pattern recognition, and data visualization, as described below: Clustering: Clustering algorithms group data points based on their similarities, allowing the identification of natural groupings or clusters within the data.…”

Section: Current Pharmaceutical Challenges and The Role Of Aimentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

Self Cite

242

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Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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Section: Current Pharmaceutical Challenges and The Role Of Aimentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

Self Cite

242

View full text Add to dashboard Cite

show abstract

“…The main purpose of encapsulation is to protect the active drug from degradation by the immune system 96 , 98 . Numerous biological applications, particularly in the field of drug administration, have been made possible by the distinctive qualities of liposomes, such as biocompatibility, biodegradability, amphiphilicity, less toxic effects, non-ionicity, prolonged drug release, and site-specific action 96 , 99 . Liposomes are mainly used as drug carriers because of their superior carrier qualities and mobility.…”

Section: Different Nanoparticles Used In Nanotheranosticsmentioning

confidence: 99%

Interventional nanotheranostics in hepatocellular carcinoma

Chavda¹,

Balar²,

Patel³

2023

Nanotheranostics

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Interventional nanotheranostics is a system of drug delivery that does a dual function; along with the therapeutic action, it also does have diagnostic features. This method helps in early detection, targeted delivery, and the least chances of damage to surrounding tissue. It ensures the highest efficiency for the management of the disease. Imaging is the near future for the quickest and most accurate detection of disease. After combing both effective measures, it ensures the most meticulous drug delivery system. Nanoparticles such as Gold NPs, Carbon NPs, Silicon NPS, etc. The article emphasizes on effect of this delivery system in the treatment of Hepatocellular Carcinoma. It is one of the widely spreading diseases and theranostics is trying to make the scenario better. The review suggests the pitfall of the current system and how theranostics can help. It describes the mechanism used to generate its effect and believes that interventional nanotheranostics do have a future with rainbow color. The article also describes the current hindrance to the flourishing of this miraculous technology.

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Regulatory Insights into Artificial Intelligence in Drug Delivery and Medical Devices

Sharma,

Bisht,

Sontakke

et al. 2024

AI Innovations in Drug Delivery and Pharmaceutical Sciences; Advancing Therapy Through Technology

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The pharmaceutical industry is grappling with challenges that impede the sustainability of drug development programs, primarily due to escalating research and development costs coupled with diminishing efficiency. This chapter explores the potential of leveraging artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), to bring about a transformative impact on the drug development process. ML, characterized by its capacity to learn from data with or without explicit programming, holds promise for addressing the complexities inherent in pharmaceutical research. DL, employing artificial neural networks (ANNs) as a multi-objective simultaneous optimization technique, has demonstrated efficacy in optimizing drug delivery systems. AI has the potential to transform drug discovery, clinical trials, drug delivery, and medical devices, emphasizing alignment with regulatory guidelines. However, challenges such as data quality and model complexity limit its transformative impact on medicine delivery and device development. This chapter is structured into three parts, each addressing a distinct aspect of AI in the pharmaceutical landscape. The first part provides a foundational introduction to AI in the pharmaceutical industry, elucidating its role in overcoming inherent challenges. The second part delves into the diverse applications of AI-based tools and systems, encompassing drug discovery, various drug delivery systems, and the development of medical devices. Finally, the third part of the chapter sheds light on the regulatory challenges associated with AI-based drug delivery and medical device development, offering insights into the evolving regulatory landscape.

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Role of Data Mining in Bioinformatics

Cited by 3 publications

References 32 publications

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Interventional nanotheranostics in hepatocellular carcinoma

Regulatory Insights into Artificial Intelligence in Drug Delivery and Medical Devices

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