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Applied Sciences
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Machine Learning and Artificial Intelligence Technologies for Data Science
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Machine Learning and Artificial Intelligence Technologies for Data Science

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 907

Editor

Dr. Jacob Laurence Newman

E-Mail Website
Guest Editor
School of Computing Sciences, University of East Anglia, University Drive, Norwich NR4 7TJ, UK
Interests: artificial intelligence (AI); applications of deep neural networks; machine learning; speech recognition and signal processing

Special Issue Information

Dear Colleagues,

This Special Issue focuses on practical applications of Machine Learning and Artificial Intelligence within the field of Data Science. As data availability continues to grow, data-driven Machine Learning and Artificial Intelligence methods continue to advance in terms of applicability and efficacy. They are the foundation of a wide range of processes, such as data exploration, feature engineering, computational modelling, prediction and classification.

This issue will highlight recent progress in these core areas, with an emphasis on contributions demonstrating practical value rather than purely theoretical developments. We welcome research showcasing novel algorithms, frameworks and tools that address real-world data challenges across multiple domains such as healthcare and marine technologies.

We welcome work involving time-series analysis, clustering, pattern recognition and large-scale data mining. Submissions incorporating deep learning, explainable Artificial Intelligence and generative models are also encouraged. By drawing together applied methodologies from diverse areas, this Special Issue aims to provide a comprehensive view of the current research landscape in data science.

Dr. Jacob Laurence Newman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial intelligence
  • deep neural networks
  • time-series analysis
  • clustering
  • data mining
  • generative artificial intelligence
  • big data
  • predictive modelling

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Published Papers (1 paper)

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Research

28 pages, 705 KB  
Article
Evidence-Grounded Clinical Pharmacogenomics Decision Support: Integrating Fine-Tuned Large Language Models with Hybrid Retrieval-Augmented Generation
by Protiva Arafin, Md Moniruzzaman and Abedalrhman Alkhateeb
Appl. Sci. 2026, 16(18), 9148; https://doi.org/10.3390/app16189148 - 15 Sep 2026
Viewed by 356
Abstract
Pharmacogenomics (PGx) play a key role in personalized medicine by guiding drug and dosage selection based on genetics. However, the volume and complexity of PGx data hinder clinical decision-making. This research proposes a data-driven clinical decision support framework that combines large language models [...] Read more.
Pharmacogenomics (PGx) play a key role in personalized medicine by guiding drug and dosage selection based on genetics. However, the volume and complexity of PGx data hinder clinical decision-making. This research proposes a data-driven clinical decision support framework that combines large language models (LLMs) with hybrid retrieval-augmented generation (RAG) to improve answers to PGx queries. The proposed framework evaluates Meta-LLaMA-3.1-8B-Instruct and Qwen3-8B across various configurations, including base models, Low-Rank Adaptation (LoRA) fine-tuning, and hybrid RAG methods. To build a robust dataset, structured data from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and clinical guideline content from ClinPGx are prepared as JSON Lines (JSONL) resources, with CPIC-derived records used for instruction tuning and structured retrieval and ClinPGx guideline text used as a separate retrieval resource. The hybrid retrieval pipeline pairs lexical filtering with dense semantic similarity via sentence embeddings to maximize factual grounding. Evaluation relies on both automated metrics and human clinical review for correctness, relevance, completeness, and clarity. Results indicate that Meta-LLaMA-3.1-8B-Instruct benefits most consistently from the combined RAG and LoRA configuration, while Qwen3-8B shows more modest action-level classification performance but improved evidence-grounded text-generation quality when retrieval is added. Fine-tuning alone proved insufficient, highlighting the limitations of purely parametric knowledge. This study shows that combining retrieval methods with parameter-efficient fine-tuning enhances LLM reliability in clinical settings. The proposed methodology offers a scalable, trustworthy framework for AI-driven decision support in pharmacogenomics and broader healthcare applications. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence Technologies for Data Science)
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