Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional neural networks

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MRI Kidney Tumor Image Classification with SMOTE Preprocessing and SIFT-tSNE Features using CNN

Kidney tumor detection is a challenging task due to the complexity of tumor characteristics and variability in imaging modalities. In this paper, we propose a deep learning-based approach for detecting kidney tumors with 98.5% accuracy. Our method addresses the issue of an imbalanced dataset by applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the distribution of images. SMOTE generates synthetic samples of the minority class to increase the number of samples, thus providing a balanced dataset. We utilize a convolutional neural network (CNN) architecture that is trained on this balanced dataset of kidney tumor images. The CNN can learn and extract relevant features from the images, resulting in precise tumor classification. We evaluated our approach on a separate dataset and compared it with state-of-the-art methods. The results demonstrate that our method not only outperforms other methods but also shows robustness in detecting kidney tumors with a high degree of accuracy. By enabling early detection and diagnosis of kidney tumors, our proposed method can potentially improve patient outcomes. Additionally, addressing the imbalance in the dataset using SMOTE demonstrates the usefulness of this technique in improving the performance of deep learning-based image classification systems