Machine learning, the kidney, and genotype–phenotype analysis

Sealfon, Rachel; Mariani, Laura; Kretzler, Matthias; Troyanskaya, Olga G.

doi:10.1016/j.kint.2020.02.028

Cited by 29 publications

(16 citation statements)

References 81 publications

(85 reference statements)

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“…ML is also starting to be applied to omics data in the kidney [9,88]. The increasing availability of such data, in particular 1 The most common performance metrics for classification problems are the AUROC and the AUPRC.…”

Section: Ai Applications For Nephrologymentioning

confidence: 99%

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

2021

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IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

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Section: Ai Applications For Nephrologymentioning

confidence: 99%

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

2021

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“…There is no benchmarking for this process. If only those models are selected which have the same confusion matrix then the ensemble method will not improve the accuracy [44].…”

Section: Ensembles Of Ann and Lstmmentioning

confidence: 99%

Eye-Color and Type-2 Diabetes Phenotype Prediction From Genotype Data Using Deep Learning Methods

Muneeb

Henschel

2020

Preprint

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Background: Genotype-Phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results: The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96 percent respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97 percent. Conclusion: Genotype-phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

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“…DL techniques that utilize digitized images of biopsies are increasingly considered to facilitate the routine workflow of a pathologist. There has been a surge of publications showcasing DL applications in clinical medicine and biomedical research, with a few of them in nephrology and nephropathology (4)(5)(6)(7)(8)(9). Specifically, DL techniques such as convolutional neural networks have been widely used for the analysis of histopathological images.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning–Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies

Zheng

Cassol

Jung

et al. 2021

The American Journal of Pathology

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Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment by capturing the pathologic features. Using trichrome-stained whole slide images (WSIs) processed from human renal biopsies, we developed a deep learning (DL) framework that captured finer pathological structures at high resolution and overall context at the WSI-level to predict IFTA grade. WSIs (n=67) were obtained from The Ohio State University Wexner Medical Center (OSUWMC).Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: <=10% (None or minimal), 11-25% (Mild), 26-50% (Moderate), and >50% (Severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n=28) obtained from the Kidney Precision Medicine Project (KPMP). There was good agreement on the IFTA grading between the pathologists and the reference estimate (Kappa=0.622±0.071). The accuracy of the DL model was 71.8±5.3% on OSUWMC and 65.0±4.2% on KPMP datasets, respectively. Identification of salient image regions by combining microscopic and WSI-level pathological features yielded visual representations that were consistent with the pathologist-based IFTA grading. Our approach to analyzing microscopic-and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

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Machine learning, the kidney, and genotype–phenotype analysis

Cited by 29 publications

References 81 publications

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

Eye-Color and Type-2 Diabetes Phenotype Prediction From Genotype Data Using Deep Learning Methods

Deep-Learning–Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies

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