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Article ; Online: Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.

Kumar, Alex / Kouznetsova, Valentina L / Kesari, Santosh / Tsigelny, Igor F

Frontiers in bioscience (Landmark edition)

2024 Volume 29, Issue 1, Page(s) 4

Abstract: Background: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.: Methods: We ... ...

Abstract	Background: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD. Methods: We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers. Results: The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis. Conclusions: The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.
MeSH term(s)	Humans ; Parkinson Disease/diagnosis ; Parkinson Disease/genetics ; Parkinson Disease/metabolism ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Deep Learning ; Machine Learning ; Biomarkers
Chemical Substances	MicroRNAs ; Biomarkers
Language	English
Publishing date	2024-02-10
Publishing country	Singapore
Document type	Journal Article
ZDB-ID	2704569-9
ISSN	2768-6698 ; 2768-6698
ISSN (online)	2768-6698
ISSN	2768-6698
DOI	10.31083/j.fbl2901004
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Article: Small molecular decoys in Alzheimer's disease.

Oasa, Sho / Kouznetsova, Valentina L / Tsigelny, Igor F / Terenius, Lars

Neural regeneration research

2023 Volume 19, Issue 8, Page(s) 1658–1659

Language	English
Publishing date	2023-12-11
Publishing country	India
Document type	Journal Article
ZDB-ID	2388460-5
ISSN	1876-7958 ; 1673-5374
ISSN (online)	1876-7958
ISSN	1673-5374
DOI	10.4103/1673-5374.389643
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Article ; Online: Laryngeal cancer diagnosis via miRNA-based decision tree model.

Arora, Aarav / Tsigelny, Igor F / Kouznetsova, Valentina L

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

2023 Volume 281, Issue 3, Page(s) 1391–1399

Abstract: Purpose: Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are ... ...

Abstract	Purpose: Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. Methods: In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a created series of miRNA attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs. Results: Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in our model to understand their relationship with cancer proliferation or suppression pathways. Conclusion: Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as an additional method for diagnosing laryngeal cancer.
MeSH term(s)	Humans ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Laryngeal Neoplasms/diagnosis ; Laryngeal Neoplasms/genetics ; Biomarkers ; Algorithms ; Decision Trees ; Gene Expression Regulation, Neoplastic
Chemical Substances	MicroRNAs ; Biomarkers
Language	English
Publishing date	2023-12-26
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1017359-6
ISSN	1434-4726 ; 0937-4477
ISSN (online)	1434-4726
ISSN	0937-4477
DOI	10.1007/s00405-023-08383-1
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Article: Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics.

Kuang, Alyssa / Kouznetsova, Valentina L / Kesari, Santosh / Tsigelny, Igor F

Metabolites

2023 Volume 14, Issue 1

Abstract: The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning ( ... ...

Abstract	The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.
Language	English
Publishing date	2023-12-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo14010011
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Article ; Online: Synergism in actions of HBV with aflatoxin in cancer development.

Jin, Joshua / Kouznetsova, Valentina L / Kesari, Santosh / Tsigelny, Igor F

Toxicology

2023 Volume 499, Page(s) 153652

Abstract: Aflatoxin B1 (AFB1) is a fungal metabolite found in animal feeds and human foods. It is one of the most toxic and carcinogenic of aflatoxins and is classified as a Group 1 carcinogen. Dietary exposure to AFB1 and infection with chronic Hepatitis B Virus ( ...

Abstract	Aflatoxin B1 (AFB1) is a fungal metabolite found in animal feeds and human foods. It is one of the most toxic and carcinogenic of aflatoxins and is classified as a Group 1 carcinogen. Dietary exposure to AFB1 and infection with chronic Hepatitis B Virus (HBV) make up two of the major risk factors for hepatocellular carcinoma (HCC). These two major risk factors raise the probability of synergism between the two agents. This review proposes some collaborative molecular mechanisms underlying the interaction between AFB1 and HBV in accelerating or magnifying the effects of HCC. The HBx viral protein is one of the main viral proteins of HBV and has many carcinogenic qualities that are involved with HCC. AFB1, when metabolized by CYP450, becomes AFB1-exo-8,9-epoxide (AFBO), an extremely toxic compound that can form adducts in DNA sequences and induce mutations. With possible synergisms that exist between HBV and AFB1 in mind, it is best to treat both agents simultaneously to reduce the risk by HCC.
MeSH term(s)	Animals ; Humans ; Carcinoma, Hepatocellular/genetics ; Hepatitis B virus/metabolism ; Liver Neoplasms/genetics ; Hepatitis B, Chronic/complications ; Aflatoxins/toxicity ; Aflatoxin B1/toxicity ; Carcinogens/toxicity ; Carcinogenesis/chemically induced
Chemical Substances	Aflatoxins ; Aflatoxin B1 (9N2N2Y55MH) ; Carcinogens
Language	English
Publishing date	2023-10-18
Publishing country	Ireland
Document type	Journal Article ; Review
ZDB-ID	184557-3
ISSN	1879-3185 ; 0300-483X
ISSN (online)	1879-3185
ISSN	0300-483X
DOI	10.1016/j.tox.2023.153652
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Article ; Online: Diagnostics of ovarian cancer via metabolite analysis and machine learning.

Yao, Jerry Z / Tsigelny, Igor F / Kesari, Santosh / Kouznetsova, Valentina L

Integrative biology : quantitative biosciences from nano to macro

2023 Volume 15

Abstract: Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, ... ...

Abstract	Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data.
MeSH term(s)	Humans ; Female ; Ovarian Neoplasms/metabolism ; Metabolomics/methods ; Metabolic Networks and Pathways ; Biomarkers, Tumor/metabolism ; Machine Learning
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2023-03-24
Publishing country	England
Document type	Journal Article
ZDB-ID	2480063-6
ISSN	1757-9708 ; 1757-9694
ISSN (online)	1757-9708
ISSN	1757-9694
DOI	10.1093/intbio/zyad005
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Article ; Online: Targeting of insulin receptor endocytosis as a treatment to insulin resistance.

Tim, Bryce / Kouznetsova, Valentina L / Kesari, Santosh / Tsigelny, Igor F

Journal of diabetes and its complications

2023 Volume 37, Issue 11, Page(s) 108615

Abstract: Background: Insulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for ... ...

Abstract	Background: Insulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for degradation or for re-use. Objectives: Our goal was to discover insulin-resistance-related genes that play key roles in endocytosis which could serve as potential biological targets to enhance insulin sensitivity. Methods: The gene mutations related to insulin resistance were elucidated from ClinVar. These were used as the seed set. Using the GeneFriends program, the genes associated with this set were elucidated and used as an enriched set for the next step. The enriched gene set network was visualized by Cytoscape. After that, using the VisANT program, the most significant cluster of genes was identified. With the help of the DAVID program, the most important KEGG pathway corresponding to the gene cluster and insulin resistance was found. Eleven genes part of the KEGG endocytosis pathway were identified. Finally, using the ChEA3 program, seven transcription factors managing these genes were defined. Results: Thirty-two genes of pathogenic significance in insulin resistance were elucidated, and then co-expression data for these genes were utilized. These genes were organized into clusters, one of which was singled out for its high node count of 58 genes and low p-value (p = 4.117 × 10 Conclusion: We believe that delaying removal of insulin receptors from the cell surface would prolong signaling of glucose uptake and counteract the symptoms of insulin resistance.
MeSH term(s)	Humans ; Receptor, Insulin/genetics ; Receptor, Insulin/metabolism ; Insulin Resistance/genetics ; Endocytosis/genetics ; Clathrin/metabolism ; Insulin/metabolism ; Transcription Factors/metabolism ; Glucose ; Homeodomain Proteins ; DNA-Binding Proteins/metabolism ; Calcium-Binding Proteins ; Trans-Activators
Chemical Substances	Receptor, Insulin (EC 2.7.10.1) ; Clathrin ; Insulin ; Transcription Factors ; Glucose (IY9XDZ35W2) ; NKX6-2 protein, human ; Homeodomain Proteins ; CXXC5 protein, human ; DNA-Binding Proteins ; CAMTA2 protein, human ; Calcium-Binding Proteins ; Trans-Activators
Language	English
Publishing date	2023-09-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	1105840-7
ISSN	1873-460X ; 1056-8727
ISSN (online)	1873-460X
ISSN	1056-8727
DOI	10.1016/j.jdiacomp.2023.108615
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Article ; Online: Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning.

Gantla, Maanaskumar R / Tsigelny, Igor F / Kouznetsova, Valentina L

Medicine in drug discovery

2022 Volume 17, Page(s) 100148

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines ... ...

Abstract	Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID‑19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.
Language	English
Publishing date	2022-11-29
Publishing country	Netherlands
Document type	Journal Article
ISSN	2590-0986
ISSN (online)	2590-0986
DOI	10.1016/j.medidd.2022.100148
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Article ; Online: Potential SARS-CoV-2 nonstructural proteins inhibitors: drugs repurposing with drug-target networks and deep learning.

Azmoodeh, Shayan K / Tsigelny, Igor F / Kouznetsova, Valentina L

Frontiers in bioscience (Landmark edition)

2022 Volume 27, Issue 4, Page(s) 113

Abstract: Background: In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to quickly developing a treatment for the disease.: Methods: In this study, we used a dataset ... ...

Abstract	Background: In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to quickly developing a treatment for the disease. Methods: In this study, we used a dataset consisting of sequences of viral proteins and chemical structures of pharmaceutical drugs for known drug-target interactions (DTIs) and artificially generated non-interacting DTIs to train a binary classifier with the ability to predict new DTIs. Random Forest (RF), deep neural network (DNN), and convolutional neural networks (CNN) were tested. The CNN and RF models were selected for the classification task. Results: The models generalized well to the given DTI data and were used to predict DTIs involving SARS-CoV-2 nonstructural proteins (NSPs). We elucidated (with the CNN) 29 drugs involved in 82 DTIs with a 97% probability of interaction, 44 DTIs of which had a 99% probability of interaction, to treat COVID-19. The RF elucidated 6 drugs involved in 17 DTIs with a 90% probability of interacting. Conclusions: These results give new insight into possible inhibitors of the viral proteins beyond pharmacophore models and molecular docking procedures used in recent studies.
MeSH term(s)	COVID-19/drug therapy ; Deep Learning ; Drug Repositioning ; Humans ; Molecular Docking Simulation ; Network Pharmacology ; Pandemics ; SARS-CoV-2 ; Viral Proteins
Chemical Substances	Viral Proteins
Language	English
Publishing date	2022-04-18
Publishing country	Singapore
Document type	Journal Article
ZDB-ID	2704569-9
ISSN	2768-6698 ; 2768-6698
ISSN (online)	2768-6698
ISSN	2768-6698
DOI	10.31083/j.fbl2704113
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Article ; Online: Machine-learning-based virtual screening to repurpose drugs for treatment of Candida albicans infection.

Gao, Andrew / Kouznetsova, Valentina L / Tsigelny, Igor F

Mycoses

2022 Volume 65, Issue 8, Page(s) 794–805

Abstract: Background: Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3-beta- ... ...

Abstract	Background: Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3-beta-glucan synthase is an essential enzyme that builds the cell walls of Candida. Objectives: Targeting CaFKS1, a subunit of the synthase, could be used to fight Candida. Methods: In the present study, a machine-learning model based on chemical descriptors was trained to recognise drugs that inhibit CaFKS1. The model attained 96.72% accuracy for classifying between active and inactive drug compounds. Descriptors for FDA-approved and other drugs were calculated, and the model was used to predict the potential activity of these drugs against CaFKS1. Results: Several drugs, including goserelin and icatibant, were detected as active with high confidence. Many of the drugs, interestingly, were gonadotrophin-releasing hormone (GnRH) antagonists or agonists. A literature search found that five of the predicted drugs inhibit Candida experimentally. Conclusions: This study yields promising drugs to be repurposed to combat Candida albicans infection. Future steps include testing the drugs on fungal cells in vitro.
MeSH term(s)	Antifungal Agents/pharmacology ; Antifungal Agents/therapeutic use ; Candida ; Candida albicans ; Candidiasis/drug therapy ; Candidiasis/microbiology ; Humans ; Machine Learning ; Microbial Sensitivity Tests
Chemical Substances	Antifungal Agents
Language	English
Publishing date	2022-06-19
Publishing country	Germany
Document type	Journal Article
ZDB-ID	392487-7
ISSN	1439-0507 ; 0933-7407
ISSN (online)	1439-0507
ISSN	0933-7407
DOI	10.1111/myc.13475
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