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Biomolecules
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Learn from Machine Learning: The Identification of Biomarkers and Therapeutic...
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Learn from Machine Learning: The Identification of Biomarkers and Therapeutic Targets in Neurodegeneration

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 3301

Special Issue Editors

Dr. Haibo Wang

E-Mail Website
Guest Editor
Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Research Institute, Houston, TX, USA
Interests: neurodegeneration; biomarker; therapeutic target; machine learning; data mining
Dr. Chen Huang

E-Mail Website
Guest Editor
Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, USA
Interests: bioinformatics; systems biology; machine learning; data mining

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit original research articles, short reports, or reviews to a Special Issue entitled “Learn from Machine Learning: The Identification of Biomarkers and Therapeutic Targets in Neurodegeneration” prepared for the journal Biomolecules.

Neurodegeneration refers to the progressive atrophy and loss of function of neurons in the central nervous system and is a hallmark of many neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD). Due to the complex etiology, it has now been appreciated that a systematic understanding of disease progression empowered by large-scale omics data is necessary to delineate the pathological landscape and identify effective treatment strategies. The recent advancements in data mining and machine/deep learning algorithms have presented an unprecedented opportunity to translate large data collected from animal models and primary patients into biological and clinical insights, including, but not limited to: the identification of diagnostic biomarkers and therapeutic targets, prediction of disease progress and patient survival, treatment regimen personalization, drug repurposing, and disease model optimization. This Special Issue aims to provide a platform for reviewing advances and progression in machine learning and deep learning in neurodegenerative disease research. Other computational innovations in data mining that contribute to the mechanistic understanding, diagnosis, and treatment of neurodegeneration are also welcome.

Dr. Haibo Wang
Dr. Chen Huang
Guest Editors

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. Biomolecules 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 2700 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
  • neurodegeneration
  • biomarkers
  • therapeutic targets
  • data mining

Published Papers (2 papers)

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Research

20 pages, 13019 KiB  
Article
Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia
by Oxana Semyachkina-Glushkovskaya, Konstantin Sergeev, Nadezhda Semenova, Andrey Slepnev, Anatoly Karavaev, Alexey Hramkov, Mikhail Prokhorov, Ekaterina Borovkova, Inna Blokhina, Ivan Fedosov, Alexander Shirokov, Alexander Dubrovsky, Andrey Terskov, Maria Manzhaeva, Valeria Krupnova, Alexander Dmitrenko, Daria Zlatogorskaya, Viktoria Adushkina, Arina Evsukova, Matvey Tuzhilkin, Inna Elizarova, Egor Ilyukov, Dmitry Myagkov, Dmitry Tuktarov and Jürgen Kurthsadd Show full author list remove Hide full author list
Biomolecules 2023, 13(11), 1605; https://doi.org/10.3390/biom13111605 - 2 Nov 2023
Viewed by 1337
Abstract
Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt [...] Read more.
Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt the integrity of the blood–brain barrier (BBB), leading to neuroinflammation and neurotoxicity. However, there are no widely used methods for real-time BBB monitoring during surgery. The development of technologies for an express diagnosis of the opening of the BBB (OBBB) is a challenge for reducing post-surgical/anesthesia consequences. In this study on male rats, we demonstrate a successful application of machine learning technology, such as artificial neural networks (ANNs), to recognize the OBBB induced by isoflurane, which is widely used in surgery. The ANNs were trained on our previously presented data obtained on the sound-induced OBBB with an 85% testing accuracy. Using an optical and nonlinear analysis of the OBBB, we found that 1% isoflurane does not induce any changes in the BBB, while 4% isoflurane caused significant BBB leakage in all tested rats. Both 1% and 4% isoflurane stimulate the brain’s drainage system (BDS) in a dose-related manner. We show that ANNs can recognize the OBBB induced by 4% isoflurane in 57% of rats and BDS activation induced by 1% isoflurane in 81% of rats. These results open new perspectives for the development of clinically significant bedside technologies for EEG-monitoring of OBBB and BDS. Full article
(This article belongs to the Special Issue Learn from Machine Learning: The Identification of Biomarkers and Therapeutic Targets in Neurodegeneration)
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13 pages, 2862 KiB  
Article
A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer’s Disease
by Umit Murat Akkaya and Habil Kalkan
Biomolecules 2023, 13(11), 1563; https://doi.org/10.3390/biom13111563 - 24 Oct 2023
Viewed by 1278
Abstract
Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of [...] Read more.
Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data. Full article
(This article belongs to the Special Issue Learn from Machine Learning: The Identification of Biomarkers and Therapeutic Targets in Neurodegeneration)
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