Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications
Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-08-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00136/full |
| _version_ | 1818616846122745856 |
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| author | Lahiru Iddamalgoda Partha Sarathi Das Partha Sarathi Das Achala Aponso Vijayaraghava Seshadri Sundararajan Prashanth Suravajhala Prashanth Suravajhala Prashanth Suravajhala Jayaraman K Valadi |
| author_facet | Lahiru Iddamalgoda Partha Sarathi Das Partha Sarathi Das Achala Aponso Vijayaraghava Seshadri Sundararajan Prashanth Suravajhala Prashanth Suravajhala Prashanth Suravajhala Jayaraman K Valadi |
| author_sort | Lahiru Iddamalgoda |
| collection | DOAJ |
| description | Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification and scoring based prioritization methods for determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors’ could be used in accurately categorizing the genetic factors in disease causation |
| first_indexed | 2024-12-16T16:56:17Z |
| format | Article |
| id | doaj.art-82a181df666745c2941d77f7e26007ab |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-16T16:56:17Z |
| publishDate | 2016-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-82a181df666745c2941d77f7e26007ab2022-12-21T22:23:52ZengFrontiers Media S.A.Frontiers in Genetics1664-80212016-08-01710.3389/fgene.2016.00136191543Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and ImplicationsLahiru Iddamalgoda0Partha Sarathi Das1Partha Sarathi Das2Achala Aponso3Vijayaraghava Seshadri Sundararajan4Prashanth Suravajhala5Prashanth Suravajhala6Prashanth Suravajhala7Jayaraman K Valadi8University of WestminsterBioclues.orgVidyasagar UniversityUniversity of WestminsterBioclues.orgBioclues.orgAarhus UniversityBioinformatics OrganizationBioclues.orgData mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification and scoring based prioritization methods for determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors’ could be used in accurately categorizing the genetic factors in disease causationhttp://journal.frontiersin.org/Journal/10.3389/fgene.2016.00136/fullData Miningmachine learningSingle nucleotide polymorphismprotein-protein interactioninherited diseases |
| spellingShingle | Lahiru Iddamalgoda Partha Sarathi Das Partha Sarathi Das Achala Aponso Vijayaraghava Seshadri Sundararajan Prashanth Suravajhala Prashanth Suravajhala Prashanth Suravajhala Jayaraman K Valadi Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications Frontiers in Genetics Data Mining machine learning Single nucleotide polymorphism protein-protein interaction inherited diseases |
| title | Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications |
| title_full | Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications |
| title_fullStr | Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications |
| title_full_unstemmed | Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications |
| title_short | Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications |
| title_sort | data mining and pattern recognizing models for identifying inherited diseases challenges and implications |
| topic | Data Mining machine learning Single nucleotide polymorphism protein-protein interaction inherited diseases |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00136/full |
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