Visualization of disease relationships by multiple maps t-SNE regularization based on Nesterov accelerated gradient

Shen, Xin; Zhu, Xianchao; Jiang, Xingpeng; He, Tingting; Hu, Xiaohua

doi:10.1109/bibm.2017.8217720

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Neighborhood Preservation Ratio (NPR) has been used as a measure of correctness in embeddings and to quantitatively assess the performance of different dimensionality reduction algorithms [35][36][37]. There are different ways to calculate NPR, for instance Shen et al [38] evaluated an NPR score that gives a general quality score for the whole embedding. In contrast, Maaten et al [35] calculated an NPR score per sample in the embedding which uses the ratio of preserved neighbors of each sample in both highand low-dimensional spaces.…”

Section: Neighborhood Preservation Ratiomentioning

confidence: 99%

Confidence estimation for t-SNE embeddings using random forest

Yigin

Saygılı

2022

Int. J. Mach. Learn. & Cyber.

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Dimensionality reduction algorithms are commonly used for reducing the dimension of multi-dimensional data to visualize them on a standard display. Although many dimensionality reduction algorithms such as the t-distributed Stochastic Neighborhood Embedding aim to preserve close neighborhoods in low-dimensional space, they might not accomplish that for every sample of the data and eventually produce erroneous representations. In this study, we developed a supervised confidence estimation algorithm for detecting erroneous samples in embeddings. Our algorithm generates a confidence score for each sample in an embedding based on a distance-oriented score and a random forest regressor. We evaluate its performance on both intra- and inter-domain data and compare it with the neighborhood preservation ratio as our baseline. Our results showed that the resulting confidence score provides distinctive information about the correctness of any sample in an embedding compared to the baseline. The source code is available at https://github.com/gsaygili/dimred.

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Section: Neighborhood Preservation Ratiomentioning

confidence: 99%

Confidence estimation for t-SNE embeddings using random forest

Yigin

Saygılı

2022

Int. J. Mach. Learn. & Cyber.

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“…They included t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE), isometric mapping (ISOMAP), kernel principal component analysis (KPCA), and multidimensional scaling (MDS) [19]- [28]. At the same time, we included other four peer methods: principal component analysis (PCA), sparse PCA, nonnegative matrix factorization (NMF), and random projection in experiments for comparisons [21], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Manifold Learning Analysis for Allele-Skewed DNA Modification SNPs for Psychiatric Disorders

Liu

Han

2020

IEEE Access

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Bipolar disorder (BPD) and schizophrenia (SCZ) are two severe worldwide psychiatric disorders. Identifying genetic components contributing to both disorders will provide meaningful insights into their pathogenesis and widely-existed misdiagnosis. In this study, we employ allele-skewed DNA modification (ASM-SNP) data to investigate the two psychiatric disorders via state-of-the-art manifold learning, data-driven feature selection, and novel pathway analysis. We propose a novel manifold learning analysis for ASM-SNP data of bipolar disorder and schizophrenia based on a data-driven feature selection algorithm: nonnegative singular value approximation (NSVA). Our results indicate that t-distributed stochastic neighbor embedding (t-SNE) outperforms its peers in distinguishing psychiatric disorder samples from normal ones in both visualization and phenotype classification. It achieves the best phenotype diagnosis results with the average AUC 0.95 by using only about 20% top-ranked SNPs. Furthermore, our results from manifold learning along with support vector machine analysis suggest that the possible non-separability of SCZ and BPD in genetics. We also validate that SCZ and BPD both share the same or similar genetic variations from pathway analysis. This study indicates the inevitable misdiagnosis issue between BPD and SCZ from a machine learning and systems biology approach. The result sheds light on the existing psychiatry research to reexamine the existing behavior-based classification for BPD and SCZ. To the best of our knowledge, this study is the first comprehensive investigation of BPD and SCZ in bioinformatics.

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“…For dimensionality reduction purposes, the machine learning algorithm t‐SNE 38 transformed our 512‐dimentional feature vectors into 2‐dimentional data points. The images were subsequently embedded at their determined location in the GM, visually describing the similarity relationship between the images.…”

Section: Methodsmentioning

confidence: 99%

Content‐based gastric image retrieval using convolutional neural networks

Zheng

et al. 2020

Int J Imaging Syst Tech

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The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content-based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet-18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet-18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state-of-art conventional methods. Furthermore, we constructed a Gastric-Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.

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Visualization of disease relationships by multiple maps t-SNE regularization based on Nesterov accelerated gradient

Cited by 11 publications

References 6 publications

Confidence estimation for t-SNE embeddings using random forest

Confidence estimation for t-SNE embeddings using random forest

Manifold Learning Analysis for Allele-Skewed DNA Modification SNPs for Psychiatric Disorders

Content‐based gastric image retrieval using convolutional neural networks

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