Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

Iddamalgoda, Lahiru; Das, P.; Aponso, Achala; Sundararajan, Vijayaraghava Seshadri; Suravajhala, Prashanth; Valadi, Jayaraman

doi:10.3389/fgene.2016.00136

Cited by 8 publications

(6 citation statements)

References 46 publications

(53 reference statements)

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“…Differentiating statistical from machine learning methods is somewhat arbitrary [ 18 , 51 ]. Typical examples of machine learning algorithms are artificial neural networks (ANNs) [ 10 ], SVMs, and random forests [ 52 ]. Okazaki and Ott [ 53 ] provided a good review on machine learning approaches to digenic inherence.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Overview of frequent pattern mining

Ott

Park

2022

Genomics Inform

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Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

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Section: Machine Learning Approachesmentioning

confidence: 99%

Overview of frequent pattern mining

Ott

Park

2022

Genomics Inform

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“…Notably, the SVM provided the overall best performance for our dataset because it is a machine learning approach for binary classification and can handle the smaller datasets well ( 54 ). RT appeared to give the poorest performance, likely due to underfitting caused by smaller dataset ( 55 ). To further assess the performance of the top-ranking features, an independent urine sample was applied to validate the models.…”

Section: Resultsmentioning

confidence: 99%

Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

et al. 2022

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Ovarian cancer (OC) is the third most common malignant tumor of women accompanied by alteration of systemic metabolism, yet the underlying interactions between the local OC tissue and other system biofluids remain unclear. In this study, we recruited 17 OC patients, 16 benign ovarian tumor (BOT) patients, and 14 control patients to collect biological samples including ovary plasma, urine, and hair from the same patient. The metabolic features of samples were characterized using a global and targeted metabolic profiling strategy based on Gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) revealed that the metabolites display obvious differences in ovary tissue, plasma, and urine between OC and non-malignant groups but not in hair samples. The metabolic alterations in OC tissue included elevated glycolysis (lactic acid) and TCA cycle intermediates (malic acid, fumaric acid) were related to energy metabolism. Furthermore, the increased levels of glutathione and polyunsaturated fatty acids (linoleic acid) together with decreased levels of saturated fatty acid (palmitic acid) were observed, which might be associated with the anti-oxidative stress capability of cancer. Furthermore, how metabolite profile changes across differential biospecimens were compared in OC patients. Plasma and urine showed a lower concentration of amino acids (alanine, aspartic acid, glutamic acid, proline, leucine, and cysteine) than the malignant ovary. Plasma exhibited the highest concentrations of fatty acids (stearic acid, EPA, and arachidonic acid), while TCA cycle intermediates (succinic acid, citric acid, and malic acid) were most concentrated in the urine. In addition, five plasma metabolites and three urine metabolites showed the best specificity and sensitivity in differentiating the OC group from the control or BOT groups (AUC > 0.90) using machine learning modeling. Overall, this study provided further insight into different specimen metabolic characteristics between OC and non-malignant disease and identified the metabolic fluctuation across ovary and biofluids.

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“…Such precise positions can help prevent the pitfalls of annotation errors, making our analysis more robust. We speculate that such analysis can also aid in predicting the early onset of cancers [ 21 , 22 ]. Taken together, our analyses could provide early roads for prognosis where these genes could aid as key candidates.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

Singh¹,

Malik

Someshwar³

et al. 2022

Genes

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Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.

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Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

Cited by 8 publications

References 46 publications

Overview of frequent pattern mining

Overview of frequent pattern mining

Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

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