4.6 Article

Artificial intelligence accelerates multi-modal biomedical process: A Survey

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

A survey of transformer-based multimodal pre-trained modals

Xue Han et al.

Summary: This paper provides a comprehensive account of the opportunities and challenges of Transformer-based multimodal pre-trained models in various domains. It reviews representative tasks of multimodal AI applications and analyzes state-of-the-art Transformer-based multimodal models from different aspects. The paper concludes with key challenges in the field and suggests future research directions.

NEUROCOMPUTING (2023)

Article Computer Science, Information Systems

Predicting Drug-Target Interactions Over Heterogeneous Information Network

Xiaorui Su et al.

Summary: In this paper, a network-based computational method called LG-DTI is proposed to accurately predict drug-target interactions. By learning the local and global representations of drugs and target proteins and incorporating them into a Random Forest classifier, LG-DTI achieves superior performance in DTI prediction. Experimental results and a case study demonstrate the effectiveness and importance of LG-DTI in identifying novel DTIs.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2023)

Article Biochemical Research Methods

Computational prediction and characterization of cell-type-specific and shared binding sites

Qinhu Zhang et al.

Summary: Cell-type-specific gene expression is maintained by selective binding of transcription factors to distinct sites. This article proposes two computational approaches for predicting and characterizing these specific and shared binding sites. Experimental results demonstrate the superior performance of these approaches in three classification tasks.

BIOINFORMATICS (2023)

Article Biochemical Research Methods

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Xin Luo et al.

Summary: This paper proposes a novel protein-protein interaction (PPI) prediction algorithm (PASNVGA) that combines sequence and network information to improve prediction accuracy. The algorithm utilizes principal component analysis to extract protein features and designs a scoring function to measure higher-order connectivity. By training a variational graph autoencoder model to learn integrated protein embeddings, the prediction task is completed using a feedforward neural network.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2023)

Article Computer Science, Information Systems

Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer's Disease

Xingyu Gao et al.

Summary: With the advance of medical imaging technologies, a deep learning framework is proposed for imputation and classification of multimodal brain images. Experimental results show that our method achieves superior performance in image imputation and brain disease diagnosis.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Computer Science, Information Systems

Breast Cancer Detection Using Multimodal Time Series Features From Ultrasound Shear Wave Absolute Vibro-Elastography

Yanan Shao et al.

Summary: In this study, a new processing pipeline for breast cancer classification using S-WAVE data was proposed and evaluated on a dataset of 40 patients. The best results outperformed the state-of-the-art reported S-WAVE breast cancer classification performance, and the sensitivity of the classification results to feature selection and changes in breast lesion contours was studied.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Biochemical Research Methods

Spectral clustering of single-cell multi-omics data on multilayer graphs

Shuyi Zhang et al.

Summary: This study introduces two spectral algorithms on multilayer graphs for clustering cells in multi-omic single-cell sequencing datasets, demonstrating the WLL method as a new spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor algorithm.

BIOINFORMATICS (2022)

Article Engineering, Electrical & Electronic

Fusion of convolutional neural networks based on Dempster-Shafer theory for automatic pneumonia detection from chest X-ray images

Safa Ben Atitallah et al.

Summary: A new approach based on deep learning and evidence fusion theory is proposed for pneumonia diagnosis in chest X-ray images, achieving high accuracy and performance according to experimental results.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2022)

Article Biochemistry & Molecular Biology

Swarm learning for decentralized artificial intelligence in cancer histopathology

Oliver Lester Saldanha et al.

Summary: This study demonstrates the importance of using a decentralized, privacy-preserving machine learning framework for histopathology image analysis. By utilizing swarm learning, AI models can be trained on routine histopathology slides collected in real-world settings to predict molecular alterations in patients. The results show that AI models trained with swarm learning outperform most locally trained models and perform on par with models trained on merged datasets.

NATURE MEDICINE (2022)

Article Multidisciplinary Sciences

Federated learning and differential privacy for medical image analysis

Mohammed Adnan et al.

Summary: The lack of large-scale medical datasets hinders the application of machine learning in healthcare due to confidentiality and privacy concerns. This study demonstrates that differentially private federated learning can achieve similar performance to conventional training while ensuring privacy in the analysis of histopathology images. The findings suggest that it is a viable and reliable framework for collaborative development of machine learning models in medical image analysis.

SCIENTIFIC REPORTS (2022)

Article Computer Science, Information Systems

Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution

Wei Peng et al.

Summary: This study proposes an algorithm, MOFGCN, to predict drug response in cell lines using Multi-Omics Fusion and Graph Convolution Network. The algorithm first calculates cell line similarity and constructs a heterogeneous network. It then learns the latent features for cancer cell lines and drugs using graph convolution operations on the network. The algorithm applies linear correlation coefficient to predict drug sensitivity. Experimental results show that MOFGCN outperforms state-of-the-art algorithms in predicting missing drug responses.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Multidisciplinary Sciences

Multimodal deep learning for Alzheimer's disease dementia assessment

Shangran Qiu et al.

Summary: The authors present a deep learning framework for dementia diagnosis that can accurately identify individuals with normal cognition, mild cognitive impairment, Alzheimer's disease, and other types of dementia. They demonstrate the effectiveness of the framework in comparison to the diagnostic accuracy of neurologists and neuroradiologists. This study also shows that disease-specific patterns detected by the models correspond closely with neuropathological lesions on autopsy.

NATURE COMMUNICATIONS (2022)

Article Computer Science, Information Systems

A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification

Yikai Yang et al.

Summary: Artificial intelligence and health sensory data-fusion have the potential to automate laborious and time-consuming processes in healthcare, such as epileptic seizure annotation. This study proposes a state-of-the-art AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) to improve the accuracy of seizure identification. The results show significant improvement in performance compared to EEG-only or ECG-only techniques.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Computer Science, Information Systems

Adaptive Multimodal Fusion With Attention Guided Deep Supervision Net for Grading Hepatocellular Carcinoma

Shangxuan Li et al.

Summary: In this study, an adaptive multimodal fusion method with an attention-guided deep supervision net was proposed for grading hepatocellular carcinoma (HCC). The proposed method balanced the importance of features among different modalities using the attention mechanism and achieved a significant performance improvement compared to existing fusion methods. Furthermore, the weight coefficient of attention in multimodal fusion was shown to be of great significance in clinical interpretation.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Biology

Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound

Zhou Xu et al.

Summary: The study proposed a multi-region radiomics approach with multimodal ultrasound for AI diagnosis of breast tumors. Results showed that information from multiple regions on breast tumors is useful for diagnosis, and the multi-region multimodal radiomics model achieved the best classification results.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Computer Science, Interdisciplinary Applications

A Deep Generative-Discriminative Learning for Multimodal Representation in Imaging Genetics

Wonjun Ko et al.

Summary: This paper proposes a novel deep learning framework that effectively handles neuroimaging and genetic data simultaneously, achieving state-of-the-art performance in Alzheimer's disease and mild cognitive impairment identification. Unlike existing methods, the framework learns the relationship between imaging phenotypes and genotypes in a nonlinear way without prior neuroscientific knowledge.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

Review Biochemistry & Molecular Biology

Multimodal biomedical AI

Julian N. Acosta et al.

Summary: This Review discusses the potential applications, technical pitfalls, and challenges of multimodal artificial intelligence models in the field of health and medicine. By utilizing data from large biobanks, electronic health records, medical imaging, and genomics, multimodal AI solutions have the ability to capture the complexity of human health and disease. Key areas such as personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, and virtual health assistants offer opportunities, but data, modeling, and privacy challenges need to be addressed for the full potential of multimodal AI in health to be realized.

NATURE MEDICINE (2022)

Article Biochemical Research Methods

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes

Siguo Wang et al.

Summary: In this study, a deep learning method called DLoopCaller is presented to predict genome-wide chromatin loops. By combining accessible chromatin landscapes and raw Hi-C contact maps, the accuracy of predicting chromatin loops is improved compared to existing methods. The identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment, contributing to the understanding of 3D genome organization and regulation.

PLOS COMPUTATIONAL BIOLOGY (2022)

Article Computer Science, Artificial Intelligence

MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer

Wei Tang et al.

Summary: Multimodal medical image fusion, the merging of information from different modalities, is crucial for comprehensive diagnosis and surgical navigation. Existing deep learning-based methods have improved fusion results but still lack satisfactory performance. In this study, we propose an unsupervised method called MATR that uses a multiscale adaptive Transformer. MATR achieves accurate fusion by adapting the convolutional kernel based on global context and enhancing global semantic extraction. The network architecture is designed to capture useful multimodal information from different scales. The proposed method outperforms other methods in visual quality and quantitative evaluation, and shows good generalization capability.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2022)

Article Computer Science, Information Systems

The Security of Medical Data on Internet Based on Differential Privacy Technology

Zhihan Lv et al.

Summary: The study discusses the security of medical data in the Internet era and proposes an algorithm model combining k-anonymity and differential privacy. Results show that the model based on differential privacy has the best performance in privacy protection and highest operational efficiency.

ACM TRANSACTIONS ON INTERNET TECHNOLOGY (2021)

Article Biochemistry & Molecular Biology

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19

Seyed Aghil Hooshmand et al.

Summary: The study utilized the Multimodal Restricted Boltzmann Machine to combine chemical structures and gene data to discover potential medications for treating COVID-19. The results suggest that this approach may be useful in identifying highly promising remedies with minimal side effects.

MOLECULAR DIVERSITY (2021)

Article Biochemical Research Methods

Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations

Hai-Cheng Yi et al.

Summary: The study introduces a computational framework for predicting molecular associations and constructs a large-scale molecular association network. By training a Random Forest classifier, the framework demonstrates excellent performance in cross-validation.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Article Computer Science, Information Systems

SCNET: A Novel UGI Cancer Screening Framework Based on Semantic-Level Multimodal Data Fusion

Shuai Ding et al.

Summary: UGI cancer screening is critical in improving patient survival rates. The proposed SCNET framework utilizes semantic-level multimodal data fusion for UGI cancer screening, achieving an improvement of 4.01 on average.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Biochemical Research Methods

Integrative survival analysis of breast cancer with gene expression and DNA methylation data

Isabelle Bichindaritz et al.

Summary: The study proposes an adaptive multi-task learning method for survival prediction of breast cancer patients using multi-modal learning, achieving more effective results compared to existing approaches. By combining different gene features, reducing dimensions, and introducing auxiliary loss, an ordinal Cox hazards model is built to predict patients' survival risk.

BIOINFORMATICS (2021)

Article Biochemical Research Methods

Predicting in-vitro Transcription Factor Binding Sites Using DNA Sequence plus Shape

Qinhu Zhang et al.

Summary: The discovery of transcription factor binding sites is crucial for understanding cellular functions, and using a deep learning method that integrates DNA sequences and shape properties has shown significant improvement in predicting TFBSs.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Review Biotechnology & Applied Microbiology

Using machine learning approaches for multi-omics data analysis: A review

Parminder S. Reel et al.

Summary: With the advancement of high-throughput omics technologies, it is crucial for biomedical research to adopt integrative approaches to analyze diverse omics data using machine learning algorithms. This can lead to the discovery of novel biomarkers and improve disease prediction and precision medicine delivery.-Methods in machine learning have enabled researchers to gain a deeper insight into biological systems and provide recommendations for interdisciplinary professionals looking to incorporate machine learning skills in multi-omics studies.

BIOTECHNOLOGY ADVANCES (2021)

Review Computer Science, Artificial Intelligence

Deep learning in ECG diagnosis: A review

Xinwen Liu et al.

Summary: Cardiovascular disease is a leading cause of death globally, and early detection of heart abnormalities can reduce the severity of the consequences. While electrocardiogram is an important tool for diagnosing CVDs, its complex nature requires computer-aided methods to alleviate the burden on humans.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Multi-source propagation aware network clustering*

Tiantian He et al.

Summary: The paper introduces a novel network clustering framework called MSPANC, which utilizes multi-source vertex features to uncover clusters in network data, outperforming most previous methods.

NEUROCOMPUTING (2021)

Article Medicine, Research & Experimental

Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture

Siguo Wang et al.

Summary: The study introduces a novel hybrid convolutional recurrent neural network architecture, CRPTS, to predict transcription factor binding sites (TFBSs) by combining DNA sequence and DNA shape features. The method efficiently extracts features and captures local structural information, outperforming state-of-the-art methods in comprehensive experiments.

MOLECULAR THERAPY-NUCLEIC ACIDS (2021)

Review Computer Science, Information Systems

Differential privacy in health research: A scoping review

Joseph Ficek et al.

Summary: This study evaluated the awareness, development, and usage of differential privacy in health research. The findings showed that differential privacy is mainly applied in genomics, neuroimaging studies, and health surveillance with personal devices, with algorithms primarily used for data release and predictive modeling. Privacy-utility appraisals of differential privacy considered factors such as economic cost-benefit analysis and low-utility situations.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2021)

Article Medical Informatics

Learning from low-rank multimodal representations for predicting disease-drug associations

Pengwei Hu et al.

Summary: The LMFDA method introduces multimodal fusion using low-rank tensors to combine multiple similar networks and utilizes matrix complement technology for predicting potential associations. It demonstrates excellent network integration ability for accurate disease-drug association inference and shows significant improvement over advanced approaches. Experimental results indicate that LMFDA delivers excellent detection performance, suggesting that enhancing similar networks with domain knowledge is a promising direction for drug repositioning.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2021)

Article Multidisciplinary Sciences

Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms

Timothy J. Fawcett et al.

Summary: The ASR is an involuntary muscle reflex in response to a transient loud sound and is used to assess hearing status in animal models. Variability in recording and interpretation of ASRs exists due to lack of standardization. A machine learning model was trained to predict ASR waveform type successfully using highly-predictive features.

METHODSX (2021)

Article Biochemical Research Methods

Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme

Qinhu Zhang et al.

Summary: This study proposes a bag-based classifier combined with a multi-fold learning scheme for motif discovery from ChIP-seq datasets, featuring three key improvements compared to existing methods. Experimental results demonstrate that the proposed method outperforms other DMD tools significantly.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Article Biochemistry & Molecular Biology

DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis

Lianhe Zhao et al.

Summary: DeepOmix is a flexible, scalable, and interpretable deep learning framework for extracting relationships between clinical survival time and multi-omics data. This method outperforms other prediction methods and demonstrates the functional module nodes associated with prognostic results in a case study on Lower Grade Glioma.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

Article Biochemical Research Methods

MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm

Zhen-Hao Guo et al.

Summary: This paper converts MeSH tree structure into a relationship network and applies various graph embedding algorithms to represent terms. Evaluation through node classification and relationship prediction tasks shows that graph embedding algorithms can serve as an independent carrier for representation and enhance the ability of vectors. This approach has the potential to inspire researchers to study term representation in a network perspective.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Computer Science, Artificial Intelligence

Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset

Chihyun Park et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Article Computer Science, Artificial Intelligence

Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images

Wenguang Yuan et al.

MEDICAL IMAGE ANALYSIS (2020)

Article Biochemical Research Methods

Learning Multimodal Networks From Heterogeneous Data for Prediction of lncRNA-miRNA Interactions

Pengwei Hu et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2020)

Article Computer Science, Information Systems

Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest

Xia-an Bi et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Article Biochemical Research Methods

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes

Chen Peng et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2020)

Article Biochemical Research Methods

Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature

Zhen Shen et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2020)

Article Health Care Sciences & Services

The future of digital health with federated learning

Nicola Rieke et al.

NPJ DIGITAL MEDICINE (2020)

Review Computer Science, Artificial Intelligence

Secure, privacy-preserving and federated machine learning in medical imaging

Georgios A. Kaissis et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Comments Mining With TF-IDF: The Inherent Bias and Its Removal

Inbal Yahav et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2019)

Article Computer Science, Artificial Intelligence

Multimodal Machine Learning: A Survey and Taxonomy

Tadas Baltrusaitis et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)

Article Computer Science, Interdisciplinary Applications

Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images

Jue Jiang et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Computer Science, Interdisciplinary Applications

Learning Cross-Modality Representations From Multi-Modal Images

Gijs van Tulder et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Biochemical Research Methods

Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer

Lin Yuan et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2019)

Article Biochemical Research Methods

Enhancing the prediction of disease-gene associations with multimodal deep learning

Ping Luo et al.

BIOINFORMATICS (2019)

Article Genetics & Heredity

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer

Zhi Huang et al.

FRONTIERS IN GENETICS (2019)

Article Computer Science, Interdisciplinary Applications

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Yutong Xie et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Computer Science, Interdisciplinary Applications

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation

Jose Dolz et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Biochemical Research Methods

MOLI: multi-omics late integration with deep neural networks for drug response prediction

Hossein Sharifi-Noghabi et al.

BIOINFORMATICS (2019)

Article Computer Science, Interdisciplinary Applications

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference

Jana Lipkova et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Computer Science, Artificial Intelligence

Deep learning based early stage diabetic retinopathy detection using optical coherence tomography

Xuechen Li et al.

NEUROCOMPUTING (2019)

Article Computer Science, Artificial Intelligence

Integration of weighted LS-SVM and manifold learning for fuzzy modeling

GaoFeng Qin et al.

NEUROCOMPUTING (2018)

Review Neurosciences

A review on automatic fetal and neonatal brain MRI segmentation

Antonios Makropoulos et al.

NEUROIMAGE (2018)

Article Engineering, Electrical & Electronic

Deep Multimodal Learning A survey on recent advances and trends

Dhanesh Ramachandram et al.

IEEE SIGNAL PROCESSING MAGAZINE (2017)

Article Computer Science, Interdisciplinary Applications

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Yitian Zhao et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2017)

Article Computer Science, Artificial Intelligence

Bag-of-words feature representation for blind image quality assessment with local quantized pattern

Xuemei Xie et al.

NEUROCOMPUTING (2017)

Article Computer Science, Artificial Intelligence

Bag-of-concepts: Comprehending document representation through clustering words in distributed representation

Han Kyul Kim et al.

NEUROCOMPUTING (2017)

Article Multidisciplinary Sciences

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Stephen F. Weng et al.

PLOS ONE (2017)

Review Computer Science, Artificial Intelligence

A review on brain structures segmentation in magnetic resonance imaging

Sandra Gonzalez-Villa et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2016)

Article Computer Science, Artificial Intelligence

Improving the BoVW via discriminative visual n-grams and MKL strategies

A. Pastor Lopez-Monroya et al.

NEUROCOMPUTING (2016)

Article Computer Science, Artificial Intelligence

Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix

Peng Yang et al.

NEUROCOMPUTING (2016)

Editorial Material Medicine, General & Internal

Predicting the Future - Big Data, Machine Learning, and Clinical Medicine

Ziad Obermeyer et al.

NEW ENGLAND JOURNAL OF MEDICINE (2016)

Article Biochemical Research Methods

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

Muxuan Liang et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2015)

Article Computer Science, Information Systems

Multimodal information fusion application to human emotion recognition from face and speech

Muharram Mansoorizadeh et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2010)

Article Biochemistry & Molecular Biology

Improved performance in protein secondary structure prediction by combining multiple predictions

De-Shuang Huang et al.

PROTEIN AND PEPTIDE LETTERS (2006)