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Application of Artificial Intelligence in Human Disease Understanding and Drug Discovery

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A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (19 May 2023) | Viewed by 24698

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Special Issue Editor

Dr. Hilal Tayara

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Guest Editor

School of International Engineering and Science, Joenbuk National University, Jeonju, Republic of Korea
Interests: deep learning; bioinformatics; drug discovery; image processing

Special Issue Information

Dear Colleagues,

Advances in sequencing techniques have resulted in large genomics datasets which can be accessed all around the world, and which can be studied and explored for human disease understanding and drug discovery. The use of genomic data to improve our understanding of human disease is an important approach in personalized medicine. In recent years, deep learning has shown outstanding performance in fields such as speech recognition, natural language processing, and image processing. These successes inspired researchers in bioinformatics and drug discovery to adopt this technique for solving complex biological tasks such as protein structure prediction and novel drug design. However, there are still many areas to explore in human disease understanding and drug discovery, and there are still limitations in the full utilization of deep learning which need to be solved in order to be effectively employed in human disease understanding and drug discovery.

This Special Issue will provide AI-enabled methods (e.g., machine learning and deep learning) that aid in human disease understanding and drug discovery. It will provide the recent practices in deep learning for improving the prediction performance and enhancing our understanding of human disease and drug discovery from genomics data. We welcome your contributions in the form of original research articles with sound and innovative methodology.

Dr. Hilal Tayara
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly 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 2600 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

the application of AI in genomic analysis
AI for epigenetic modifications
variant calling using AI
AI in therapeutic tasks
AI in precision medicine
AI in drug discovery
AI in drug response
AI in omics data integration
AI for protein function prediction

Published Papers (3 papers)

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Research

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16 pages, 1570 KiB

Open AccessArticle

Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images

by Osman Özkaraca, Okan İhsan Bağrıaçık, Hüseyin Gürüler, Faheem Khan, Jamil Hussain, Jawad Khan and Umm e Laila

Life 2023, 13(2), 349; https://doi.org/10.3390/life13020349 - 28 Jan 2023

Cited by 27 | Viewed by 4557

Abstract

Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis. They are also used as the most sensitive method in evaluating diseases of the pituitary gland, brain vessels, eye, and inner ear organs. Many medical image analysis methods based on deep learning techniques have been proposed for health monitoring and diagnosis from brain MRI images. CNNs (Convolutional Neural Networks) are a sub-branch of deep learning and are often used to analyze visual information. Common uses include image and video recognition, suggestive systems, image classification, medical image analysis, and natural language processing. In this study, a new modular deep learning model was created to retain the existing advantages of known transfer learning methods (DenseNet, VGG16, and basic CNN architectures) in the classification process of MR images and eliminate their disadvantages. Open-source brain tumor images taken from the Kaggle database were used. For the training of the model, two types of splitting were utilized. First, 80% of the MRI image dataset was used in the training phase and 20% in the testing phase. Secondly, 10-fold cross-validation was used. When the proposed deep learning model and other known transfer learning methods were tested on the same MRI dataset, an improvement in classification performance was obtained, but an increase in processing time was observed. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Human Disease Understanding and Drug Discovery)

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12 pages, 8694 KiB

Open AccessArticle

A Prospective Approach to Integration of AI Fracture Detection Software in Radiographs into Clinical Workflow

by Jonas Oppenheimer, Sophia Lüken, Bernd Hamm and Stefan Markus Niehues

Life 2023, 13(1), 223; https://doi.org/10.3390/life13010223 - 13 Jan 2023

Cited by 8 | Viewed by 3616

Abstract

Gleamer BoneView^© is a commercially available AI algorithm for fracture detection in radiographs. We aim to test if the algorithm can assist in better sensitivity and specificity for fracture detection by residents with prospective integration into clinical workflow. Radiographs with inquiry for fracture initially reviewed by two residents were randomly assigned and included. A preliminary diagnosis of a possible fracture was made. Thereafter, the AI decision on presence and location of possible fractures was shown and changes to diagnosis could be made. Final diagnosis of fracture was made by a board-certified radiologist with over eight years of experience, or if available, cross-sectional imaging. Sensitivity and specificity of the human report, AI diagnosis, and assisted report were calculated in comparison to the final expert diagnosis. 1163 exams in 735 patients were included, with a total of 367 fractures (31.56%). Pure human sensitivity was 84.74%, and AI sensitivity was 86.92%. Thirty-five changes were made after showing AI results, 33 of which resulted in the correct diagnosis, resulting in 25 additionally found fractures. This resulted in a sensitivity of 91.28% for the assisted report. Specificity was 97.11, 84.67, and 97.36%, respectively. AI assistance showed an increase in sensitivity for both residents, without a loss of specificity. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Human Disease Understanding and Drug Discovery)

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Review

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23 pages, 650 KiB

Open AccessReview

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

by Anu Maria Sebastian and David Peter

Life 2022, 12(12), 1991; https://doi.org/10.3390/life12121991 - 28 Nov 2022

Cited by 27 | Viewed by 15739

Abstract

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Human Disease Understanding and Drug Discovery)

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