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Reddy A, Reddy RP, Roghani AK, Garcia RI, Khemka S, Pattoor V, Jacob M, Reddy PH, Sehar U. Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements. Ageing Res Rev 2024; 99:102410. [PMID: 38972602 DOI: 10.1016/j.arr.2024.102410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.
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Affiliation(s)
- Aananya Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Ruhananhad P Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Aryan Kia Roghani
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Frenship High School, Lubbock, TX 79382, USA.
| | - Ricardo Isaiah Garcia
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Sachi Khemka
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Vasanthkumar Pattoor
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; University of South Florida, Tampa, FL 33620, USA.
| | - Michael Jacob
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Biology, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Public Health Department of Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department pf Speech, Language and Hearing Services, School Health Professions, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Ujala Sehar
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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Aljarallah NA, Dutta AK, Sait ARW. A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis. Int J Mol Sci 2024; 25:6422. [PMID: 38928128 PMCID: PMC11203850 DOI: 10.3390/ijms25126422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/29/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study's objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual's conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.
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Affiliation(s)
- Nasser Ali Aljarallah
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - Abdul Rahaman Wahab Sait
- Department of Documents and Archive, Center of Documents and Administrative Communication, King Faisal University, Al-Ahsa, Al Hofuf 31982, Saudi Arabia
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Gonzalez-Latapi P, Bustos B, Dong S, Lubbe S, Simuni T, Krainc D. Alterations in Blood Methylome as Potential Epigenetic Biomarker in Sporadic Parkinson's Disease. Ann Neurol 2024; 95:1162-1172. [PMID: 38563317 DOI: 10.1002/ana.26923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/03/2024] [Accepted: 02/19/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE To characterize DNA methylation (DNAm) differences between sporadic Parkinson's disease (PD) and healthy control (HC) individuals enrolled in the Parkinson's Progression Markers Initiative (PPMI). METHODS Using whole blood, we characterized longitudinal differences in DNAm between sporadic PD patients (n = 196) and HCs (n = 86) enrolled in PPMI. RNA sequencing (RNAseq) was used to conduct gene expression analyses for genes mapped to differentially methylated cytosine-guanine sites (CpGs). RESULTS At the time of patient enrollment, 5,178 CpGs were differentially methylated (2,683 hypermethylated and 2,495 hypomethylated) in PD compared to HC. Of these, 579 CpGs underwent significant methylation changes over 3 years. Several differentially methylated CpGs were found near the cytochrome P450 family 2 subfamily E member 1 (CYP2E1) gene. Additionally, multiple hypermethylated CpGs were associated with the N-myc downregulated gene family member 4 (NDRG4) gene. RNA-Seq analyses showed 75 differentially expressed genes in PD patients compared to controls. An integrative analysis of both differentially methylated sites and differentially expressed genes revealed 20 genes that exhibited hypomethylation concomitant with overexpression. Additionally, 1 gene, cathepsin H (CTSH), displayed hypermethylation that was associated with its decreased expression. INTERPRETATION We provide initial evidence of alterations in DNAm in blood of PD patients that may serve as potential epigenetic biomarker of disease. To evaluate the significance of these changes throughout the progression of PD, additional profiling at longer intervals and during the prodromal stages of disease will be necessary. ANN NEUROL 2024;95:1162-1172.
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Affiliation(s)
- Paulina Gonzalez-Latapi
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bernabe Bustos
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Siyuan Dong
- Biostatistics Collaboration Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Steven Lubbe
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tanya Simuni
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Dimitri Krainc
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Chapman MA, Sorg BA. A Systematic Review of Extracellular Matrix-Related Alterations in Parkinson's Disease. Brain Sci 2024; 14:522. [PMID: 38928523 PMCID: PMC11201521 DOI: 10.3390/brainsci14060522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
The role of the extracellular matrix (ECM) in Parkinson's disease (PD) is not well understood, even though it is critical for neuronal structure and signaling. This systematic review identified the top deregulated ECM-related pathways in studies that used gene set enrichment analyses (GSEA) to document transcriptomic, proteomic, or genomic alterations in PD. PubMed and Google scholar were searched for transcriptomics, proteomics, or genomics studies that employed GSEA on data from PD tissues or cells and reported ECM-related pathways among the top-10 most enriched versus controls. Twenty-seven studies were included, two of which used multiple omics analyses. Transcriptomics and proteomics studies were conducted on a variety of tissue and cell types. Of the 17 transcriptomics studies (16 data sets), 13 identified one or more adhesion pathways in the top-10 deregulated gene sets or pathways, primarily related to cell adhesion and focal adhesion. Among the 8 proteomics studies, 5 identified altered overarching ECM gene sets or pathways among the top 10. Among the 4 genomics studies, 3 identified focal adhesion pathways among the top 10. The findings summarized here suggest that ECM organization/structure and cell adhesion (particularly focal adhesion) are altered in PD and should be the focus of future studies.
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Affiliation(s)
| | - Barbara A. Sorg
- R.S. Dow Neurobiology, Legacy Research Institute, Portland, OR 97232, USA;
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Danek BP, Makarious MB, Dadu A, Vitale D, Lee PS, Singleton AB, Nalls MA, Sun J, Faghri F. Federated learning for multi-omics: A performance evaluation in Parkinson's disease. PATTERNS (NEW YORK, N.Y.) 2024; 5:100945. [PMID: 38487808 PMCID: PMC10935499 DOI: 10.1016/j.patter.2024.100945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/29/2024] [Accepted: 02/02/2024] [Indexed: 03/17/2024]
Abstract
While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open-source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.
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Affiliation(s)
- Benjamin P. Danek
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
| | - Mary B. Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Anant Dadu
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
| | - Dan Vitale
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
| | - Paul Suhwan Lee
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew B. Singleton
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mike A. Nalls
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Faraz Faghri
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
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Chen Y, Zhou X, Ji L, Zhao J, Xian H, Xu Y, Wang Z, Ge W. Construction and analysis of a joint diagnostic model of machine learning for cryptorchidism based on single-cell sequencing. Birth Defects Res 2024; 116:e2316. [PMID: 38459615 DOI: 10.1002/bdr2.2316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/29/2023] [Accepted: 01/21/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND Cryptorchidism is a condition in which one or both of a baby's testicles do not fully descend into the bottom of the scrotum. Newborns with cryptorchidism are at increased risk of developing infertility later in life. The aim of this study was to develop a novel diagnostic model for cryptorchidism and to identify new biomarkers associated with cryptorchidism. METHODS The study data were obtained from RNA sequencing data of cryptorchid patients from Nantong University Hospital and the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to obtain differentially expressed genes (DEGs) between the control and cryptorchid groups. These DEGs were analyzed for their functions by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using GSEA software. Random Forest algorithm was used to screen central genes based on these DEGs. Neuralnet software package was used to develop artificial neural network models. Based on clinical data, receiver operating characteristic (ROC) was used to validate the models. Single-cell sequencing analysis was used for the pathogenesis of cryptorchidism. RESULTS We obtained a total of 525 important DEGs related to cryptorchidism, which are mainly associated with biological functions such as supramolecular complexes and microtubule cytoskeleton. Random forest approach screening obtained eight hub genes. A neural network based on the hub genes showed a 100% success rate of the model. Finally, single-cell sequencing analysis validated the hub genes. CONCLUSION We developed a novel diagnostic model for cryptorchidism using artificial neural networks and validated its utility as an effective diagnostic tool.
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Affiliation(s)
- Yuehua Chen
- Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Xiaomeng Zhou
- Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Linghua Ji
- Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Jun Zhao
- Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Hua Xian
- Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Yunzhao Xu
- Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China
| | - Ziheng Wang
- Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, China
- Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau, China
| | - Wenliang Ge
- Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
- Department of Pediatric Surgery, School of Medicine, Nantong University, Nantong, China
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Danek B, Makarious MB, Dadu A, Vitale D, Lee PS, Nalls MA, Sun J, Faghri F. Federated Learning for multi-omics: a performance evaluation in Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.04.560604. [PMID: 37986893 PMCID: PMC10659429 DOI: 10.1101/2023.10.04.560604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.
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Affiliation(s)
- Benjamin Danek
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- DataTecnica, Washington, DC, 20037, USA
| | - Mary B. Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Anant Dadu
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- DataTecnica, Washington, DC, 20037, USA
| | - Dan Vitale
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- DataTecnica, Washington, DC, 20037, USA
| | - Paul Suhwan Lee
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- DataTecnica, Washington, DC, 20037, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Faraz Faghri
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- DataTecnica, Washington, DC, 20037, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
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Irmady K, Hale CR, Qadri R, Fak J, Simelane S, Carroll T, Przedborski S, Darnell RB. Blood transcriptomic signatures associated with molecular changes in the brain and clinical outcomes in Parkinson's disease. Nat Commun 2023; 14:3956. [PMID: 37407548 DOI: 10.1038/s41467-023-39652-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/23/2023] [Indexed: 07/07/2023] Open
Abstract
The ability to use blood to predict the outcomes of Parkinson's disease, including disease progression and cognitive and motor complications, would be of significant clinical value. We undertook bulk RNA sequencing from the caudate and putamen of postmortem Parkinson's disease (n = 35) and control (n = 40) striatum, and compared molecular profiles with clinical features and bulk RNA sequencing data obtained from antemortem peripheral blood. Cognitive and motor complications of Parkinson's disease were associated with molecular changes in the caudate (stress response) and putamen (endothelial pathways) respectively. Later and earlier-onset Parkinson's disease were molecularly distinct, and disease duration was associated with changes in caudate (oligodendrocyte development) and putamen (cellular senescence), respectively. Transcriptome patterns in the postmortem Parkinson's disease brain were also evident in antemortem peripheral blood, and correlated with clinical features of the disease. Together, these findings identify molecular signatures in Parkinson's disease patients' brain and blood of potential pathophysiologic and prognostic importance.
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Affiliation(s)
- Krithi Irmady
- Laboratory of Molecular Neuro-oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.
| | - Caryn R Hale
- Laboratory of Molecular Neuro-oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Rizwana Qadri
- Laboratory of Molecular Neuro-oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - John Fak
- Laboratory of Molecular Neuro-oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Sitsandziwe Simelane
- Laboratory of Molecular Neuro-oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Thomas Carroll
- Bioinformatics Resource Center, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Serge Przedborski
- Department of Neurology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology & Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Neuroscience, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Robert B Darnell
- Laboratory of Molecular Neuro-oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.
- Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.
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Mohanraj L, Wolf H, Silvey S, Liu J, Toor A, Swift-Scanlan T. DNA Methylation Changes in Autologous Hematopoietic Stem Cell Transplant Patients. Biol Res Nurs 2023; 25:310-325. [PMID: 36321693 PMCID: PMC10236442 DOI: 10.1177/10998004221135628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Blood cancers may be potentially cured with hematopoietic stem cell transplantation (HCT); however, standard pre-assessments for transplant eligibility do not capture all contributing factors for transplant outcomes. Epigenetic biomarkers predict outcomes in various diseases. This pilot study aims to explore epigenetic changes (epigenetic age and differentially methylated genes) in patients before and after autologous HCT, that can serve as potential biomarkers to better predict HCT outcomes. METHODS This study used a prospective longitudinal study design to compare genome wide DNA methylation changes in 36 autologous HCT eligible patients recruited from the Cellular Immunotherapies and Transplant clinic at a designated National Cancer Center. RESULTS Genome-wide DNA methylation, measured by the Illumina Infinium Human Methylation 850K BeadChip, showed a significant difference in DNA methylation patterns post-HCT compared to pre-HCT. Compared to baseline levels of DNA methylation pre-HCT, 3358 CpG sites were hypo-methylated and 3687 were hyper-methylated. Identified differentially methylated positions overlapped with genes involved in hematopoiesis, blood cancers, inflammation and immune responses. Enrichment analyses showed significant alterations in biological processes such as immune response and cell structure organization, however no significant pathways were noted. Though participants had an advanced epigenetic age compared to chronologic age before and after HCT, both epigenetic age and accelerated age decreased post-HCT. CONCLUSION Epigenetic changes, both in epigenetic age and differentially methylated genes were observed in autologous HCT recipients, and should be explored as biomarkers to predict transplant outcomes after autologous HCT in larger, longitudinal studies.
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Affiliation(s)
- Lathika Mohanraj
- Department of Adult Health and Nursing
Systems, VCU School of Nursing, Richmond, VA, USA
| | - Hope Wolf
- Department of Human and Molecular Genetics, VCU School of Medicine, Richmond, VA, USA
| | - Scott Silvey
- Department of Biostatistics, VCU School of Medicine, Richmond, VA, USA
| | - Jinze Liu
- Department of Biostatistics, VCU School of Medicine, Richmond, VA, USA
| | - Amir Toor
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, USA
| | - Theresa Swift-Scanlan
- Endowed Professor and Director,
Biobehavioral Research Lab, VCU School of Nursing, Richmond, VA, USA
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Lesage S, Trinh J. Special Issue “Parkinson’s Disease: Genetics and Pathogenesis”. Genes (Basel) 2023; 14:genes14030737. [PMID: 36981007 PMCID: PMC10048160 DOI: 10.3390/genes14030737] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
Parkinson’s disease (PD) is a common and incurable neurodegenerative disease, affecting 1% of the population over the age of 65 [...]
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Affiliation(s)
- Suzanne Lesage
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute, ICM, INSERM U 1127, CNRS UMR 7225, Assistance Publique-Hôpitaux de Paris (AP-HP), 75013 Paris, France
- Correspondence: ; Tel.: +33-(0)-1-57-27-46-80
| | - Joanne Trinh
- Institute of Neurogenetics, University of Lübeck, 23562 Lübeck, Germany;
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Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:3080. [PMID: 36991790 PMCID: PMC10052105 DOI: 10.3390/s23063080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/19/2023]
Abstract
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
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Affiliation(s)
- Erkan Bostanci
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Engin Kocak
- Department of Analytical Chemistry, Faculty of Gülhane Pharmacy, University of Health Sciences, 06018 Ankara, Turkey
| | - Metehan Unal
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Mehmet Serdar Guzel
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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Gerraty RT, Provost A, Li L, Wagner E, Haas M, Lancashire L. Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs. Front Aging Neurosci 2023; 15:1076657. [PMID: 36861121 PMCID: PMC9968811 DOI: 10.3389/fnagi.2023.1076657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/16/2023] [Indexed: 02/17/2023] Open
Abstract
The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.
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Affiliation(s)
| | | | - Lin Li
- PharmaLex, Frederick, MD, United States
| | | | - Magali Haas
- Cohen Veterans Bioscience, New York, NY, United States
| | - Lee Lancashire
- Cohen Veterans Bioscience, New York, NY, United States,*Correspondence: Lee Lancashire, ✉
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