Enyi Li’s research while affiliated with Nankai University and other places

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Publications (3)


A graphical illustration of the proposed DeepQA compared to existing methods. (a) Existing methods train an AI model for each cohort with homogeneous data as aging clocks to evaluate biological ages. The models, typically random forest, LDA with inefficient gene selection, are usually trained only on healthy/control subjects and tested on both healthy and unhealthy subjects. (b) We propose a unified transcriptome‐based aging clock based on deep neural networks, named DeepQA. Unlike existing methods, DeepQA is trained on data of both healthy and unhealthy subjects on multiple cohorts and is able to infer biological age much more accurate than existing methods. (c) Architecture of the proposed DeepQA as well as specially designed loss functions for the training on both healthy and unhealthy subjects. Our DeepQA has 26.3 M trainable parameters in total.
Prediction performance of DeepQA compared with existing methods on healthy subjects of different groups, namely all, brain, retina, heart, lung, and others from top to bottom. Columns correspond to DeepQA, ELDA‐GEF, RF‐Variable, and EN‐All, respectively. In each plot, the x‐axis is the true age and the y‐axis is the predicted age. DeepQA outperformed existing methods on all groups.
(a) Age prediction accuracy of DeepQA and existing methods on both healthy and unhealthy subjects. This plot is a graphical illustration of Table 2. For healthy subjects, MAE was used to measure the goodness of prediction. For unhealthy subjects, statistical significance, −log10(p‐value), was plotted to show the plausibility of prediction. (b) Comparison of DeepQA and existing methods on different groups of healthy samples. DeepQA outperformed existing methods in all the groups. (c) Robustness of DeepQA and other methods against simulated Gaussian noises in gene expression data at different levels. (d) Robustness of DeepQA and other methods against simulated PCA noises in gene expression data at different levels. (e) Robustness in MAE of DeepQA and other methods against Gaussian or Dropout noises at a typical magnitude of 1.5. (f) Robustness in R² of DeepQA and other methods against Gaussian or Dropout noises at a typical magnitude of 1.5. (g) Impact of noises in ages (labels) on prediction accuracy of DeepQA on healthy subjects. (h) Training time of DeepQA w.r.t the size of the training data. The diameters of the blue solid dots correspond to the prediction error in MAE.
Age prediction accuracy of DeepQA with different hyperparameter. (a) ∆$$ \Delta $$, the margin for the Hinge‐MAE loss. (b) γ$$ \gamma $$, weight to balance the loss terms. (c) nN$$ \frac{n}{N} $$, the percentage of genes used in the AHK loss. (d) The number of experts. The red circle corresponds to ‐log10(0.05) which is the threshold for the significance test. For AD, AMD, and Schizophrenia, the data points should be located outside the red circle which indicates aging acceleration has been detected. For the other two conditions, data points should be inside the red circle. (e) MAE on the healthy subjects with different hyperparameters, among which the number of experts was the most sensitive one affecting the prediction accuracy on the healthy subjects. The optimal values of these hyperparameters were shown in the main text.
(a) KEGG pathways enriched on the top‐1000 genes identified to be important for age prediction on the group of healthy subjects and five unhealthy conditions by DeepQA. (b–d) KEGG analysis of genes identified by DeepQA from young (b), adult (c), and old healthy groups (d), respectively. (e–f) DeepQA genes overlapped with SASP genes in AD (e) or AMD (f). (g–h) GO analysis of overlapped genes from AD (g) or AMD (h). In all graphs, top 1000 genes with DeepQA were selected. In all the bubble charts, color gradient indicates the level of statistical significance (−log10 (p‐value)) and the dot size indicates the number of genes. The gene enrichment analysis was performed using the online DAVID tools (Sherman et al. 2022).

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DeepQA: A Unified Transcriptome‐Based Aging Clock Using Deep Neural Networks
  • Article
  • Full-text available

January 2025

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14 Reads

Hongqian Qi

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Hongchen Zhao

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Enyi Li

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[...]

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Jianda Han

Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging of a subject, and to identify molecular and cellular biomarkers of aging. A typical approach for estimating biological age, adopted by almost all existing aging clocks, is to train machine learning models only on healthy subjects, but to infer on both healthy and unhealthy subjects. However, the inherent bias in this approach results in inaccurate biological age as shown in this study. Moreover, almost all existing transcriptome‐based aging clocks were built around an inefficient procedure of gene selection followed by conventional machine learning models such as elastic nets, linear discriminant analysis etc. To address these limitations, we proposed DeepQA, a unified aging clock based on mixture of experts. Unlike existing methods, DeepQA is equipped with a specially designed Hinge‐Mean‐Absolute‐Error (Hinge‐MAE) loss so that it can train on both healthy and unhealthy subjects of multiple cohorts to reduce the bias of inferring biological age of unhealthy subjects. Our experiments showed that DeepQA significantly outperformed existing methods for biological age estimation on both healthy and unhealthy subjects. In addition, our method avoids the inefficient exhaustive search of genes, and provides a novel means to identify genes activated in aging prediction, alternative to such as differential gene expression analysis.

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Mind the Gap: Learning Modality-Agnostic Representations With a Cross-Modality UNet

January 2024

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41 Reads

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2 Citations

IEEE Transactions on Image Processing

Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap include reducing the distributional differences of representations of different modalities, learning indistinguishable representations or explicit modality transfer. The first two approaches suffer from the loss of discriminant information while removing the modality-specific variations. The third one heavily relies on the successful modality transfer, could face catastrophic performance drop when explicit modality transfers are not possible or difficult. To tackle this problem, we proposed a compact encoder-decoder neural module (cmUNet) to learn modality-agnostic representations while retaining identity-related information. This is achieved through cross-modality transformation and in-modality reconstruction, enhanced by an adversarial/perceptual loss which encourages indistinguishability of representations in the original sample space. For cross-modality matching, we propose MarrNet where cmUNet is connected to a standard feature extraction network which takes as inputs the modality-agnostic representations and outputs similarity scores for matching. We validated our method on five challenging tasks, namely Raman-infrared spectrum matching, cross-modality person re-identification and heterogeneous (photo-sketch, visible-near infrared and visible-thermal) face recognition, where MarrNet showed superior performance compared to state-of-the-art methods. Furthermore, it is observed that a cross-modality matching method could be biased to extract discriminant information from partial or even wrong regions, due to incompetence of dealing with modality gaps, which subsequently leads to poor generalization. We show that robustness to occlusions can be an indicator of whether a method can well bridge the modality gap. This, to our knowledge, has been largely neglected in the previous works. Our experiments demonstrated that MarrNet exhibited excellent robustness against disguises and occlusions, and outperformed existing methods with a large margin (> 10%). The proposed cmUNet is a meta-approach and can be used as a building block for various applications.