Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets
Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far fail...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/12/10/1444 |
| _version_ | 1797474920193589248 |
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| author | Oscar Alcazar Mitsunori Ogihara Gang Ren Peter Buchwald Midhat H. Abdulreda |
| author_facet | Oscar Alcazar Mitsunori Ogihara Gang Ren Peter Buchwald Midhat H. Abdulreda |
| author_sort | Oscar Alcazar |
| collection | DOAJ |
| description | Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using <i>Ingenuity Pathway Analysis (IPA)</i> software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the <i>Canonical Pathway</i> and <i>Diseases and Functions</i> modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The <i>Biomarker Prediction</i> module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited. |
| first_indexed | 2024-03-09T20:36:51Z |
| format | Article |
| id | doaj.art-4ecb4ee4a58340a28d7baccf618cd6ac |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-09T20:36:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-4ecb4ee4a58340a28d7baccf618cd6ac2023-11-23T23:08:46ZengMDPI AGBiomolecules2218-273X2022-10-011210144410.3390/biom12101444Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human DatasetsOscar Alcazar0Mitsunori Ogihara1Gang Ren2Peter Buchwald3Midhat H. Abdulreda4Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USAInstitute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USAInstitute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USADiabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USADiabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USABackground: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using <i>Ingenuity Pathway Analysis (IPA)</i> software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the <i>Canonical Pathway</i> and <i>Diseases and Functions</i> modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The <i>Biomarker Prediction</i> module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.https://www.mdpi.com/2218-273X/12/10/1444artificial intelligence (AI)algorithmbig datadata imputation and amplificationearly biomarker signaturesearly diagnosis |
| spellingShingle | Oscar Alcazar Mitsunori Ogihara Gang Ren Peter Buchwald Midhat H. Abdulreda Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets Biomolecules artificial intelligence (AI) algorithm big data data imputation and amplification early biomarker signatures early diagnosis |
| title | Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets |
| title_full | Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets |
| title_fullStr | Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets |
| title_full_unstemmed | Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets |
| title_short | Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets |
| title_sort | exploring computational data amplification and imputation for the discovery of type 1 diabetes t1d biomarkers from limited human datasets |
| topic | artificial intelligence (AI) algorithm big data data imputation and amplification early biomarker signatures early diagnosis |
| url | https://www.mdpi.com/2218-273X/12/10/1444 |
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