Kim et al 2023a - Revision history

Tomamil: Tomamil moved page Review 636429891875 to Kim et al 2023a

2023-07-29T23:08:59Z

Tomamil moved page Review 636429891875 to Kim et al 2023a

Bkim0902 at 23:35, 19 July 2023

2023-07-19T23:35:32Z

Bkim0902 at 23:30, 19 July 2023

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Bkim0902: Bkim0902 moved page Draft Kim 152773742 to Review 636429891875

2023-06-28T23:00:49Z

Bkim0902 moved page Draft Kim 152773742 to Review 636429891875

Bkim0902 at 22:56, 28 June 2023

2023-06-28T22:56:04Z

Bkim0902 at 22:55, 28 June 2023

2023-06-28T22:55:55Z

Bkim0902 at 22:52, 28 June 2023

2023-06-28T22:52:58Z

Bkim0902 at 22:52, 28 June 2023

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Bkim0902: Bkim0902 moved page Review 687453188095 to Draft Kim 152773742

2023-06-28T22:50:15Z

Bkim0902 moved page Review 687453188095 to Draft Kim 152773742

Bkim0902: Bkim0902 moved page Draft Kim 903923737 to Review 687453188095

2023-06-09T01:25:05Z

Bkim0902 moved page Draft Kim 903923737 to Review 687453188095

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 '''Abstract – '''Multiple myeloma (MM) is a clonal cell cancer characterized by excessive cell division of plasma cells in the bone marrow, which then produce abnormal antibodies that lead to end organ damage. The worldwide incidence of MM amounted to 160,000 cases in 2018 and 106,000 patients have succumbed to the disease. Currently, the lack of specific treatment, and use of only generic proteasome inhibitors, contribute to high mortality rates. By identifying differentially expressed genes found in malignant plasma cells, scientists can develop new and stronger therapies tailored to driver genes. This study takes a machine learning approach to identify driver genes of MM. In this study, single-cell RNA sequencing data was obtained from the Gene Expression Omnibus database containing 29,367 plasma cells and 22,088 genes. This study evaluated the performance of three machine learning models: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with RF achieving the highest accuracy of 95.61% of correctly diagnosing a cell to a stage of MM. Principal components identified ANKRD28, CXCR4, HLA-DPA1 among several other potential driver genes that have been cross-validated with previous literature. Notably, the models identified RP5-1171I10.5–a gene not yet established to be associated with multiple myeloma which shows potential to be further studied for research.
-'''Keyword''' ''s:'' ''Multiple myeloma, cancer, machine learning, single-cell RNA sequencing, principal component analysis ''
+'''Keywords''''':'' ''Multiple myeloma, cancer, machine learning, single-cell RNA sequencing, principal component analysis ''
 <div class="center" style="width: auto; margin-left: auto; margin-right: auto;">

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 == Abstract ==
-Multiple myeloma (MM) is a clonal cell cancer characterized by excessive cell division of plasma cells in the bone marrow, which can then overcrowd healthy cells. As a result, end organ damage to kidneys, bones, and the liver occurs. The worldwide incidence of MM amounted to 160,000 cases in 2018 and 106,000 patients have succumbed to the disease. MM is diagnosed relatively well by detecting M monoclonal protein produced from cancerous cells, yet mortality rates remain high because there is a lack of a specific treatment. By identifying differentially expressed genes found in malignant plasma cells, scientists can develop new and stronger therapies tailored to potential driver genes. This study takes a novel machine learning approach to identify driver genes of MM. In this study, single-cell RNA sequencing data was obtained from the Gene Expression Omnibus database containing 26 patients in various disease stages and 9 healthy donors, totaling 29,367 plasma cells and 22,088 genes. This study evaluated the performance of three machine learning models: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with RF achieving the highest accuracy of 95.61% of correctly diagnosing a cell to a stage of MM. Principal components identified ANKRD28, CXCR4, HLA-DPA1 among several other potential driver genes that have been cross-validated with previous literature. Notably, the models identified RP5-1171I10.5–a gene not yet established to be associated with multiple myeloma which shows potential to be further studied for research. These genes show potential to be further studied for specific targeted genetic therapy.
 == Full document ==
-<pdf>Media:Draft_Kim_152773742-2732-document.pdf</pdf>
+<pdf>Media:Draft_Kim_152773742-4472-document.pdf</pdf>

@@ Line 1: / Line 1: @@
 == Abstract ==
-Abstract: Multiple myeloma (MM) is a clonal cell cancer characterized by excessive cell division of plasma cells in the bone marrow, which can then overcrowd healthy cells. As a result, end organ damage to kidneys, bones, and the liver occurs. The worldwide incidence of MM amounted to 160,000 cases in 2018 and 106,000 patients have succumbed to the disease. MM is diagnosed relatively well by detecting M monoclonal protein produced from cancerous cells, yet mortality rates remain high because there is a lack of a specific treatment. By identifying upregulated genes found in malignant plasma cells, scientists can develop new and stronger therapies tailored to potential driver genes. This study takes a novel machine learning approach to identify driver genes of MM. A single-cell RNA sequencing dataset obtained from Gene Expression Omnibus containing data from 29,367 plasma cells and 22,088 genes was utilized in this study. This study evaluated the performance of three machine learning models: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with RF achieving the highest accuracy of 95.61%.To name a few genes, the models identified ANKRD28 and HLA-DPA1 as potential driver genes that have been cross-validated with previous literature. Notably, the models identified RP5-1171I10.5–a gene not yet established to be associated with multiple myeloma which shows potential to be further studied for research. These genes show potential to be further studied for specific targeted genetic therapy.
+Multiple myeloma (MM) is a clonal cell cancer characterized by excessive cell division of plasma cells in the bone marrow, which can then overcrowd healthy cells. As a result, end organ damage to kidneys, bones, and the liver occurs. The worldwide incidence of MM amounted to 160,000 cases in 2018 and 106,000 patients have succumbed to the disease. MM is diagnosed relatively well by detecting M monoclonal protein produced from cancerous cells, yet mortality rates remain high because there is a lack of a specific treatment. By identifying differentially expressed genes found in malignant plasma cells, scientists can develop new and stronger therapies tailored to potential driver genes. This study takes a novel machine learning approach to identify driver genes of MM. In this study, single-cell RNA sequencing data was obtained from the Gene Expression Omnibus database containing 26 patients in various disease stages and 9 healthy donors, totaling 29,367 plasma cells and 22,088 genes. This study evaluated the performance of three machine learning models: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with RF achieving the highest accuracy of 95.61% of correctly diagnosing a cell to a stage of MM. Principal components identified ANKRD28, CXCR4, HLA-DPA1 among several other potential driver genes that have been cross-validated with previous literature. Notably, the models identified RP5-1171I10.5–a gene not yet established to be associated with multiple myeloma which shows potential to be further studied for research. These genes show potential to be further studied for specific targeted genetic therapy.
 == Full document ==
 <pdf>Media:Draft_Kim_152773742-2732-document.pdf</pdf>

@@ Line 1: / Line 1: @@
 == Abstract ==
+Abstract: Multiple myeloma (MM) is a clonal cell cancer characterized by excessive cell division of plasma cells in the bone marrow, which can then overcrowd healthy cells. As a result, end organ damage to kidneys, bones, and the liver occurs. The worldwide incidence of MM amounted to 160,000 cases in 2018 and 106,000 patients have succumbed to the disease. MM is diagnosed relatively well by detecting M monoclonal protein produced from cancerous cells, yet mortality rates remain high because there is a lack of a specific treatment. By identifying upregulated genes found in malignant plasma cells, scientists can develop new and stronger therapies tailored to potential driver genes. This study takes a novel machine learning approach to identify driver genes of MM. A single-cell RNA sequencing dataset obtained from Gene Expression Omnibus containing data from 29,367 plasma cells and 22,088 genes was utilized in this study. This study evaluated the performance of three machine learning models: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with RF achieving the highest accuracy of 95.61%.To name a few genes, the models identified ANKRD28 and HLA-DPA1 as potential driver genes that have been cross-validated with previous literature. Notably, the models identified RP5-1171I10.5–a gene not yet established to be associated with multiple myeloma which shows potential to be further studied for research. These genes show potential to be further studied for specific targeted genetic therapy.
 Multiple myeloma (MM) is a clonal cell cancer characterized by excessive cell division of plasma cells in the bone marrow, which can then overcrowd healthy cells. As a result, end organ damage to kidneys, bones, and the liver occurs. The worldwide incidence of MM amounted to 160,000 cases in 2018 and 106,000 patients have succumbed to the disease. MM is diagnosed relatively well by detecting M monoclonal protein produced from cancerous cells, yet mortality rates remain high because there is a lack of a specific treatment. By identifying upregulated genes found in malignant plasma cells, scientists can develop new and stronger therapies tailored to potential driver genes. This study takes a novel machine learning approach to identify driver genes of MM. A single-cell RNA sequencing dataset obtained from Gene Expression Omnibus containing data from 29,367 plasma cells and 22,088 genes was utilized in this study. This study evaluated the performance of three machine learning models: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with RF achieving the highest accuracy of 95.61%.To name a few genes, the models identified
 == Full document ==
 <pdf>Media:Draft_Kim_152773742-2732-document.pdf</pdf>

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