Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a ...holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.
•An effective end-to-end deep neural network for integration of multi-omics data.•Divergence-based regularization can capture consensus information among modalities.•The performances are varied when ...integrating different modalities of -omics data.•Improved performance in cancer type classification and survival prediction.•Visualized the better agreement among modalities after consensus learning.
Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-scale multi-omics data such as gene expression, DNA methylation, miRNA expression, and copy number variations can provide insights on personalized survival. However, how to effectively integrate multi-omics data remains a challenging task. In this paper, we develop multi-omics integration methods to improve the prediction of overall survival for breast cancer and ovarian cancer patients. Because multi-omics data for the same patient jointly impact the survival of cancer patients, features from different -omics modality are related and can be modeled by either association or causal relationship (e.g., pathways). By extracting these relationships among modalities, we can get rid of the irrelevant information from high-throughput multi-omics data. However, it is infeasible to use the Brute Force method to capture all possible multi-omics interactions. Thus, we use deep neural networks with novel divergence-based consensus regularization to capture multi-omics interactions implicitly by extracting modality-invariant representations. In comparing the concatenation-based integration networks with our new divergence-based consensus networks, the breast cancer overall survival C-index is improved from 0.655±0.062 to 0.671±0.046 when combing DNA methylation and miRNA expression, and from 0.627±0.062 to 0.667±0.073 when combing miRNA expression and copy number variations. In summary, our novel deep consensus neural network has successfully improved the prediction of overall survival for breast cancer and ovarian cancer patients by implicitly learning the multi-omics interactions.
Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic ...biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient's condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)).
Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction.
For breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641 ± 0.031 with ConcatAE, and 0.63 ± 0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583 ± 0.058) or miRNA expression (0.616 ± 0.057).
In conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.
Abstract
Motivation
To characterize long non-coding RNAs (lncRNAs), both identifying and functionally annotating them are essential to be addressed. Moreover, a comprehensive construction for lncRNA ...annotation is desired to facilitate the research in the field.
Results
We present LncADeep, a novel lncRNA identification and functional annotation tool. For lncRNA identification, LncADeep integrates intrinsic and homology features into a deep belief network and constructs models targeting both full- and partial-length transcripts. For functional annotation, LncADeep predicts a lncRNA's interacting proteins based on deep neural networks, using both sequence and structure information. Furthermore, LncADeep integrates KEGG and Reactome pathway enrichment analysis and functional module detection with the predicted interacting proteins, and provides the enriched pathways and functional modules as functional annotations for lncRNAs. Test results show that LncADeep outperforms state-of-the-art tools, both for lncRNA identification and lncRNA-protein interaction prediction, and then presents a functional interpretation. We expect that LncADeep can contribute to identifying and annotating novel lncRNAs.
Availability and implementation
LncADeep is freely available for academic use at http://cqb.pku.edu.cn/ZhuLab/lncadeep/ and https://github.com/cyang235/LncADeep/.
Supplementary information
Supplementary data are available at Bioinformatics online.
With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research ...opportunities.
This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods.
First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.
Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pandemic. However, clinicians are often reluctant to ...trust deep models due to their black-box characteristics. To automatically differentiate COVID-19 and community-acquired pneumonia from healthy lungs in radiographic imaging, we propose an explainable attention-transfer classification model based on the knowledge distillation network structure. The attention transfer direction always goes from the teacher network to the student network. Firstly, the teacher network extracts global features and concentrates on the infection regions to generate attention maps. It uses a deformable attention module to strengthen the response of infection regions and to suppress noise in irrelevant regions with an expanded reception field. Secondly, an image fusion module combines attention knowledge transferred from teacher network to student network with the essential information in original input. While the teacher network focuses on global features, the student branch focuses on irregularly shaped lesion regions to learn discriminative features. Lastly, we conduct extensive experiments on public chest X-ray and CT datasets to demonstrate the explainability of the proposed architecture in diagnosing COVID-19.
Objective: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data ...contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. Methods: In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. Results: To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Conclusion: Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine. Significance: Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.
Summary
Introduction
: There has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale ...labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols. Domain adaptation (DA) has been developed to transfer the knowledge from a labeled data domain to a related but unlabeled domain in either image space or feature space. DA is a type of transfer learning (TL) that can improve the performance of models when applied to multiple different datasets.
Objective
: In this survey, we review the state-of-the-art DL-based DA methods for medical imaging. We aim to summarize recent advances, highlighting the motivation, challenges, and opportunities, and to discuss promising directions for future work in DA for medical imaging.
Methods
: We surveyed peer-reviewed publications from leading biomedical journals and conferences between 2017-2020, that reported the use of DA in medical imaging applications, grouping them by methodology, image modality, and learning scenarios.
Results
: We mainly focused on pathology and radiology as application areas. Among various DA approaches, we discussed domain transformation (DT) and latent feature-space transformation (LFST). We highlighted the role of unsupervised DA in image segmentation and described opportunities for future development.
Conclusion
: DA has emerged as a promising solution to deal with the lack of annotated training data. Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks. Opportunities include domain transferability, multi-modal DA, and applications that benefit from synthetic data.
Semiconductor quantum dots (QDs) are light-emitting particles on the nanometer scale that have emerged as a new class of fluorescent labels for chemical analysis, molecular imaging, and biomedical ...diagnostics. Compared with traditional fluorescent probes, QDs have unique optical and electronic properties such as size-tunable light emission, narrow and symmetric emission spectra, and broad absorption spectra that enable the simultaneous excitation of multiple fluorescence colors. QDs are also considerably brighter and more resistant to photobleaching than are organic dyes and fluorescent proteins. These properties are well suited for dynamic imaging at the single-molecule level and for multiplexed biomedical diagnostics at ultrahigh sensitivity. Here, we discuss the fundamental properties of QDs; the development of next-generation QDs; and their applications in bioanalytical chemistry, dynamic cellular imaging, and medical diagnostics. For in vivo and clinical imaging, the potential toxicity of QDs remains a major concern. However, the toxic nature of cadmium-containing QDs is no longer a factor for in vitro diagnostics, so the use of multicolor QDs for molecular diagnostics and pathology is probably the most important and clinically relevant application for semiconductor QDs in the immediate future.
The early detection and diagnosis of malignant colorectal tumors enables the initiation of early-stage therapy and can significantly increase the survival rate and post-treatment quality of life ...among cancer patients. Hyperspectral imaging (HSI) is recognized as a powerful tool for noninvasive cancer detection. In the gastrointestinal field, most of the studies on HSI have involved ex vivo biopsies or resected tissues. In the present study, we aimed to assess the difference in the in vivo spectral reflectance of malignant colorectal tumors and normal mucosa. A total of 21 colorectal tumors or adenomatous polyps from 12 patients at Shanghai Zhongshan Hospital were examined using a flexible hyperspectral (HS) colonoscopy system that can obtain in vivo HS images of the colorectal mucosa. We determined the optimal wavelengths for differentiating tumors from normal tissue based on these recorded images. The application of the determined wavelengths in spectral imaging in clinical trials indicated that such a clinical support system comprising a flexible HS colonoscopy unit and band selection unit is useful for outlining the tumor region and enhancing the display of the mucosa microvascular pattern in vivo.
PDF
