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Life
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Artificial Intelligence with Applications in Life Sciences
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Artificial Intelligence with Applications in Life Sciences

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 (20 January 2023) | Viewed by 14838

Special Issue Editor

Dr. Andrej Kastrin

E-Mail Website
Guest Editor
Faculty of Medicine, Institute for Biostatistics and Medical Informatics, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: biostatistics; medical informatics; artificial intelligence; text mining; literature-based discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the enormous expansion of high-throughput technologies, life sciences have entered the big data era. Massive, high-dimensional, and heterogeneous datasets have become woven into all areas of modern life sciences. The key challenge is how to gain insights and extract useful knowledge from such data. The recent decade has seen a surge in research on artificial intelligence (AI) methods and applications in the broader domain of life sciences. For example, PubMed, the largest bibliographic database in the field of life sciences, has listed more than 7000 records for the term “artificial intelligence” for the last year. AI methods have been applied to a broad spectrum of applications such as computational biology, information retrieval, bioinformatics, and computer vision. Despite this success, the field has not yet been thoroughly investigated and presents many challenges. It is therefore crucial to generating new ideas and developing new algorithms and methods to gain fresh insights in diverging directions.

This Special Issue will collect both review articles and original papers describing novel methods and applications of AI in life sciences. Papers presenting AI applications in the broader domain of life sciences are also welcome. The topics of interest for this Special Issue include, but are not limited to the following:

  • Novel models, algorithms, and tools for biomedicine, bioinformatics, neuroinformatics, and healthcare (e.g., statistical methods, data mining, machine learning, knowledge representations, natural language processing)
  • Computational approaches for diagnostic, prognostic, and therapeutic decisions
  • Novel deep-learning techniques
  • Explainable AI
  • Scalable methods for big data analytics and stream processing
  • Complex networks including network medicine, network embeddings, and integration of heterogeneous data sources
  • Evaluation methods and benchmark datasets
  • Computational creativity
  • Literature-based discovery

Dr. Andrej Kastrin
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

  • artificial intelligence
  • machine learning
  • knowledge representations
  • complex networks
  • literature-based discovery

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Published Papers (4 papers)

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Research

12 pages, 1500 KiB  
Article
Sleep Apnea Detection Based on Multi-Scale Residual Network
by Hengyang Fang, Changhua Lu, Feng Hong, Weiwei Jiang and Tao Wang
Life 2022, 12(1), 119; https://doi.org/10.3390/life12010119 - 14 Jan 2022
Cited by 4 | Viewed by 2044
Abstract
Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR [...] Read more.
Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR interval signals and R peak signals derived from the ECG signals as input. Then, a multi-scale residual network is used to extract the characteristics of the original signals in order to obtain sensitive characteristics from various angles. Because the residual structure is used in the model, the problem of model degradation can be avoided. Finally, a fully connected layer is introduced for SA detection. In order to overcome the impact of class imbalance, a focal loss function is introduced to replace the traditional cross-entropy loss function, which makes the model pay more attention to learning difficult samples in the training phase. Experimental results from the Apnea-ECG dataset show that the accuracy, sensitivity and specificity of the proposed multi-scale residual network are 86.0%, 84.1% and 87.1%, respectively. These results indicate that the proposed method not only achieves greater recognition accuracy than other methods, but it also effectively resolves the problem of low sensitivity caused by class imbalance. Full article
(This article belongs to the Special Issue Artificial Intelligence with Applications in Life Sciences)
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15 pages, 2634 KiB  
Article
PBPK Modeling and Simulation of Antibiotics Amikacin, Gentamicin, Tobramycin, and Vancomycin Used in Hospital Practice
by Abigail Ferreira, Helena Martins, José Carlos Oliveira, Rui Lapa and Nuno Vale
Life 2021, 11(11), 1130; https://doi.org/10.3390/life11111130 - 23 Oct 2021
Cited by 8 | Viewed by 3093
Abstract
The importance of closely observing patients receiving antibiotic therapy, performing therapeutic drug monitoring (TDM), and regularly adjusting dosing regimens has been extensively demonstrated. Additionally, antibiotic resistance is a contemporary concerningly dangerous issue. Optimizing the use of antibiotics is crucial to ensure treatment efficacy [...] Read more.
The importance of closely observing patients receiving antibiotic therapy, performing therapeutic drug monitoring (TDM), and regularly adjusting dosing regimens has been extensively demonstrated. Additionally, antibiotic resistance is a contemporary concerningly dangerous issue. Optimizing the use of antibiotics is crucial to ensure treatment efficacy and prevent toxicity caused by overdosing, as well as to combat the prevalence and wide spread of resistant strains. Some antibiotics have been selected and reserved for the treatment of severe infections, including amikacin, gentamicin, tobramycin, and vancomycin. Critically ill patients often require long treatments, hospitalization, and require particular attention regarding TDM and dosing adjustments. As these antibiotics are eliminated by the kidneys, critical deterioration of renal function and toxic effects must be prevented. In this work, clinical data from a Portuguese cohort of 82 inpatients was analyzed and physiologically based pharmacokinetic (PBPK) modeling and simulation was used to study the influence of different therapeutic regimens and parameters as biological sex, body weight, and renal function on the biodistribution and pharmacokinetic (PK) profile of these four antibiotics. Renal function demonstrated the greatest impact on plasma concentration of these antibiotics, and vancomycin had the most considerable accumulation in plasma over time, particularly in patients with impaired renal function. Thus, through a PBPK study, it is possible to understand which pharmacokinetic parameters will have the greatest variation in a given population receiving antibiotic administrations in hospital context. Full article
(This article belongs to the Special Issue Artificial Intelligence with Applications in Life Sciences)
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23 pages, 509 KiB  
Article
COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?
by Nina Ružić Gorenjec, Nataša Kejžar, Damjan Manevski, Maja Pohar Perme, Bor Vratanar and Rok Blagus
Life 2021, 11(10), 1045; https://doi.org/10.3390/life11101045 - 4 Oct 2021
Cited by 6 | Viewed by 4602
Abstract
During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among [...] Read more.
During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among the most affected countries. This was true even though strict non-pharmaceutical interventions (NPIs) to control the progression of the epidemic were being enforced. Using a semi-parametric Bayesian model developed for the purpose of this study, we explore if and how the changes in mobility, their timing and the activation of contact tracing can explain the differences in the epidemic progression of the two waves. To fit the model, we use data on daily numbers of deaths, patients in hospitals, intensive care units, etc., and allow transmission intensity to be affected by contact tracing and mobility (data obtained from Google Mobility Reports). Our results imply that though there is some heterogeneity not explained by mobility levels and contact tracing, implementing interventions at a similar stage as in the first wave would keep the death toll and the health system burden low in the second wave as well. On the other hand, sticking to the same timeline of interventions as observed in the second wave and focusing on enforcing a higher decrease in mobility would not be as beneficial. According to our model, the ‘dance’ strategy, i.e., first allowing the numbers to rise and then implementing strict interventions to make them drop again, has been played at too-late stages of the epidemic. In contrast, a 15–20% reduction of mobility compared to pre-COVID level, if started at the beginning and maintained for the entire duration of the second wave and coupled with contact tracing, could suffice to control the epidemic. A very important factor in this result is the presence of contact tracing; without it, the reduction in mobility needs to be substantially larger. The flexibility of our proposed model allows similar analyses to be conducted for other regions even with slightly different data sources for the progression of the epidemic; the extension to more than two waves is straightforward. The model could help policymakers worldwide to make better decisions in terms of the timing and severity of the adopted NPIs. Full article
(This article belongs to the Special Issue Artificial Intelligence with Applications in Life Sciences)
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16 pages, 2754 KiB  
Article
A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation
by Yuchai Wan, Zhongshu Zheng, Ran Liu, Zheng Zhu, Hongen Zhou, Xun Zhang and Said Boumaraf
Life 2021, 11(6), 582; https://doi.org/10.3390/life11060582 - 18 Jun 2021
Cited by 7 | Viewed by 2787
Abstract
Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, [...] Read more.
Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for liver lesion diagnosis on magnetic resonance images, termed as MMF-CNN. We introduce a multi-scale representation strategy to encode both the local and semi-local complementary information of the images. To take advantage of the complementary information of multi-scale representations, we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning. We further explore the explanation of the diagnosis decision of the deep neural network through visualizing the areas of interest of the network. A new scoring method is designed to evaluate whether the attention maps can highlight the relevant radiological features. The explanation and visualization make the decision-making process of the deep neural network transparent for the clinicians. We apply our proposed approach to various state-of-the-art deep learning architectures. The experimental results demonstrate the effectiveness of our approach. Full article
(This article belongs to the Special Issue Artificial Intelligence with Applications in Life Sciences)
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