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Artificial Intelligence in Biomarker Discovery 2.0
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Artificial Intelligence in Biomarker Discovery 2.0

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 3824

Special Issue Editor

Prof. Dr. Ekaterini Chatzaki

E-Mail Website
Guest Editor
Laboratory of Pharmacology, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Interests: biomarker; classifier; liquid biopsy; epigenetics; cell culture; solid tumor; diagnosis; moniroting; neuropeptide; drug target; drug response; metabolic disease; diabetes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomarkers are the cornerstone of precision medicine: identified as measurable indicators of some biological state or condition, they promise to offer solutions for accurate diagnosis, prognosis, and therapeutic monitoring. Over the last two decades, high-throughput technology in biomedicine has demonstrated significant advancements, leading to a vast accumulation of publicly available precious x-omics high-dimension biological datasets, presenting welcoming opportunities for new science. Innovative bioinformatic artificial intelligence (AI) tools promise to enable the exploitation of these data, shifting the global scientific trend from hypothesis- to data-driven approaches. Modern statistical and machine learning methods perform predictive modeling and knowledge discovery. This brings unprecedented added value in different medical conditions, gaining intuition into the molecular pathological mechanisms of disease, identifying novel drug targets, or designing emerging cost-effective assays for improving diagnoses, prognoses, or response prediction, readily available for clinical application. This Special Issue plans to offer an overview of recent advances in data-driven-approach-aided state-of-the-art AI in diverse areas of biomedical research, with an emphasis on biomarker discovery.

Potential topics include, but are not limited to, machine learning for biomarkers in precision oncology, machine learning tools for feature selection or dimensionality reduction in molecular biology, text mining tools and pathway analysis for biomarker exploration, artificial intelligence in microbiome analysis, artificial intelligence in epigenetic biomarkers and epitranscriptomics, machine learning tools in drug discovery, biomarker retrieval and knowledge reasoning systems in molecular biology, and data integration in biomedical or biomarker research.

Due to the success of the first edition, we would like to add more results and new insights from recent research projects. You can find the first edition at the following link:
https://www.mdpi.com/journal/ijms/special_issues/Artificial_Intelligence_Biomarker_Discovery.

Prof. Dr. Ekaterini Chatzaki
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
  • biomarker
  • precision medicine
  • artificial intelligence
  • text mining
  • automation
  • genomics
  • epigenomics
  • proteomics
  • big data
  • clinical
  • diagnostic
  • bioinformatic

Related Special Issue

Published Papers (3 papers)

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Research

16 pages, 2776 KiB  
Article
Mitochondrial Fraction of Circulating Cell-Free DNA as an Indicator of Human Pathology
by Maria Panagopoulou, Makrina Karaglani, Konstantina Tzitzikou, Nikoleta Kessari, Konstantinos Arvanitidis, Kyriakos Amarantidis, George I. Drosos, Spyros Gerou, Nikolaos Papanas, Dimitrios Papazoglou, Stavroula Baritaki, Theodoros C. Constantinidis and Ekaterini Chatzaki
Int. J. Mol. Sci. 2024, 25(8), 4199; https://doi.org/10.3390/ijms25084199 - 10 Apr 2024
Viewed by 412
Abstract
Circulating cell-free DNA (ccfDNA) of mitochondrial origin (ccf-mtDNA) consists of a minor fraction of total ccfDNA in blood or in other biological fluids. Aberrant levels of ccf-mtDNA have been observed in many pathologies. Here, we introduce a simple and effective standardized Taqman probe-based [...] Read more.
Circulating cell-free DNA (ccfDNA) of mitochondrial origin (ccf-mtDNA) consists of a minor fraction of total ccfDNA in blood or in other biological fluids. Aberrant levels of ccf-mtDNA have been observed in many pathologies. Here, we introduce a simple and effective standardized Taqman probe-based dual-qPCR assay for the simultaneous detection and relative quantification of nuclear and mitochondrial fragments of ccfDNA. Three pathologies of major burden, one malignancy (Breast Cancer, BrCa), one inflammatory (Osteoarthritis, OA) and one metabolic (Type 2 Diabetes, T2D), were studied. Higher levels of ccf-mtDNA were detected both in BrCa and T2D in relation to health, but not in OA. In BrCa, hormonal receptor status was associated with ccf-mtDNA levels. Machine learning analysis of ccf-mtDNA datasets was used to build biosignatures of clinical relevance. (A) a three-feature biosignature discriminating between health and BrCa (AUC: 0.887) and a five-feature biosignature for predicting the overall survival of BrCa patients (Concordance Index: 0.756). (B) a five-feature biosignature stratifying among T2D, prediabetes and health (AUC: 0.772); a five-feature biosignature discriminating between T2D and health (AUC: 0.797); and a four-feature biosignature identifying prediabetes from health (AUC: 0.795). (C) a biosignature including total plasma ccfDNA with very high performance in discriminating OA from health (AUC: 0.934). Aberrant ccf-mtDNA levels could have diagnostic/prognostic potential in BrCa and Diabetes, while the developed multiparameter biosignatures can add value to their clinical management. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomarker Discovery 2.0)
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13 pages, 1090 KiB  
Article
Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma
by Antonio Lacalamita, Grazia Serino, Ester Pantaleo, Alfonso Monaco, Nicola Amoroso, Loredana Bellantuono, Emanuele Piccinno, Viviana Scalavino, Francesco Dituri, Sabina Tangaro, Roberto Bellotti and Gianluigi Giannelli
Int. J. Mol. Sci. 2023, 24(20), 15286; https://doi.org/10.3390/ijms242015286 - 18 Oct 2023
Cited by 2 | Viewed by 1015
Abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and the number of cases is constantly increasing. Early and accurate HCC diagnosis is crucial to improving the effectiveness of treatment. The aim of the study is to develop a supervised learning [...] Read more.
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and the number of cases is constantly increasing. Early and accurate HCC diagnosis is crucial to improving the effectiveness of treatment. The aim of the study is to develop a supervised learning framework based on hierarchical community detection and artificial intelligence in order to classify patients and controls using publicly available microarray data. With our methodology, we identified 20 gene communities that discriminated between healthy and cancerous samples, with an accuracy exceeding 90%. We validated the performance of these communities on an independent dataset, and with two of them, we reached an accuracy exceeding 80%. Then, we focused on two communities, selected because they were enriched with relevant biological functions, and on these we applied an explainable artificial intelligence (XAI) approach to analyze the contribution of each gene to the classification task. In conclusion, the proposed framework provides an effective methodological and quantitative tool helping to find gene communities, which may uncover pivotal mechanisms responsible for HCC and thus discover new biomarkers. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomarker Discovery 2.0)
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16 pages, 4118 KiB  
Article
Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning
by Biaojie Huang, Qiurui Chen, Zhiyun Ye, Lin Zeng, Cuibing Huang, Yuting Xie, Rongxin Zhang and Han Shen
Int. J. Mol. Sci. 2023, 24(17), 13175; https://doi.org/10.3390/ijms241713175 - 24 Aug 2023
Cited by 2 | Viewed by 1615
Abstract
Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were [...] Read more.
Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-β-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomarker Discovery 2.0)
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