IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024
10 – 13 November 2024 Houston, TX USA
Discovering consensus regions for interpretable identification of RNA N6-methyladenosine modification sites via graph contrastive clustering
G. Li, B. Zhao, X. Su, Y. Yang, P. Hu, X. Zhou, L. Hu
As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.
Sensor Informatics
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ECG Feature Importance Rankings: Cardiologists vs. Algorithms
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Generative Listener EEG for Speech Emotion Recognition Using Generative Adversarial Networks with Compressed Sensing
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Spatiotemporal Network based on GCN and BiGRU for seizure detection
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Imaging Informatics
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A Unified Multi-Modality Fusion Framework for Deep Spatio-Temporal-Spectral Feature Learning in Resting-State fMRI Denoising
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A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence Tomography
El-Baz, Ayman; Elsharkawy, M.; Sharafeldeen, Ahmed; Khalifa, Fahmi; Soliman, A.; Elnakib, Ahmed; Ghazal, Mohammed; Sewelam, A.; Thanos, A.; Sandhu, H. S.
Continuous Refinement-based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems
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CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI
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Hybrid Masked Image Modeling for 3D Medical Image Segmentation
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A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
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Generalizable Polyp Segmentation via Randomized Global Illumination Augmentation
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TSGET: Two-Stage Global Enhanced Transformer for Automatic Radiology Report Generation
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Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation
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TNCB: Tri-net with Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification
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CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation
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Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates
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Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
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Medical Informatics
Attention Mechanisms in Clinical Text Classification: A Comparative Evaluation
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PVR-Vocoder: A Pathological Voice Repair Vocoder for Voice Disorders
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DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
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The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health
McGinnis, Ryan; Loftness, Bryn C.; Halvorson-Phelan, Julia; OLeary, Aisling; Bradshaw, Carter; Prytherch, Shania; Berman, Isabel; Torous, John; Copeland, William; Cheney, Nick; McGinnis, Ellen
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Fu, Qiming; Zhou, Xueyang; Xia, Youbing; Wang, Yunzhe; Lu, You; Chen, Yanming; Chen, Jianping
Bioinformatics
Discovering consensus regions for interpretable identification of RNA N6-methyladenosine modification sites via graph contrastive clustering
Li, Guodong; Zhao, Bowei; Su, Xiaorui; Yang, Yue; HU, Pengwei; Zhou, Xi; Hu, Lun
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TEPI: T_ axonomy-aware E_ mbedding and P_ seudo- I_maging for Scarcely-labeled Zero-shot Genome Classification
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TBCA: Prediction of transcription factor binding sites using a deep neural network with lightweight attention mechanism
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Predicting Protein Functions based on Heterogeneous Graph Attention Technique
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Hierarchical and dynamic graph attention network for drug-disease association prediction
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MUMA: a multi-omics meta-learning algorithm for data interpretation and classification
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Efficient Motif Discovery in Protein Sequences Using a Branch and Bound Algorithm
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PLANNER: a multi-scale deep language model for the origins of replication site prediction
Li, Fuyi; Wang, Cong; He, Zhijie; Jia, Runchang; Pan, Shirui ; JM, Lachlan Coin; Song, Jiangning
Modeling and AI Informatics
A Novel Dual Layer Cascade Reliability Framework for an Informed and Intuitive Clinician-AI Interaction In Diagnosis of Colorectal Cancer Polyps
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RClaNet: An explainable Alzheimer’s disease diagnosis framework by joint deformable registration and classification
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Data augmentation for Human Activity Recognition with Generative Adversarial Networks
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Insights of Machine Learning into Medical Decision Making Systems: From Research to Practice
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Identification of congenital valvular murmurs in young patients using deep learning-based attention transformers and phonocardiograms
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Convolutional Attention Based Multimodal Network for Depression Detection on Social Media and Its Impact During Pandemic
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Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization
Xie, Qianqian; Tiwari, Prayag; Ananiadou, Sophia
Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria
Bender, Theresa; Beinecke, Jacqueline M.; Krefting, Dagmar; Müller, Carolin; Dathe, Henning; Seidler, Tim; Spicher, Nicolai; Hauschild, Anne-Christin
Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study in South Africa
Kolozali, Sefki; L, Sara White; Norris, Shane; Fasli, Maria; Heerden, Alastair van
Few-shot class-incremental learning for Medical time series classification
SUN, Le; Zhang, Mingyang; Wang, Benyou; Tiwari, Prayag
Advanced Machine Learning and Artificial Intelligence Tools for Computational Biology
Exploiting Hierarchical Interactions for Protein Surface Learning
Lin, Yiqun; Pan, Liang; Li, Yi; Liu, Ziwei; Li, Xiaomeng
EnDL-HemoLyt: Ensemble deep learning-based tool for identifying therapeutic peptides with low hemolytic activity
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Employing Feature Selection Algorithms to Determine the Immune State of Mice Model of Rheumatoid Arthritis
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Hybrid Bayesian Optimization-based Graphical Discovery for Methylation Sites Prediction
Gu, Ling-Yan; Chen, Tingbo; Li, Jianqiang; Huang, Yu-An; Du, Zhihua; Leung, Victor; Chen, Jie
K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering
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Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks
Shen, Cong; Mao, Dongdong; Tang, Jijun; Liao, Zhijun; Chen, S.Y.
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Sharma, Ritesh; Shrivastava, Sameer; Singh, Sanjay Kumar; Kumar, Abhinav; Singh, Amit Kumar; Saxena, Sonal
Compound Scaling Encoder-Decoder (CoSED) Network for Diabetic Retinopathy Related Bio-marker Detection
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