scholarly journals Extracting biological age from biomedical data via deep learning: too much of a good thing?

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Timothy V. Pyrkov ◽  
Konstantin Slipensky ◽  
Mikhail Barg ◽  
Alexey Kondrashin ◽  
Boris Zhurov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Biological Age ◽  
Good Thing ◽  
Biomedical Data

scholarly journals Extracting biological age from biomedical data via deep learning: too much of a good thing?

2017 ◽  
Author(s):  
Tim Pyrkov ◽  
Konstantin Slipensky ◽  
Mikhail Barg ◽  
Alexey Kondrashin ◽  
Boris Zhurov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Proportional Hazards ◽  
Wearable Sensors ◽  
Learning Technologies ◽  
Biological Age ◽  
Chronological Age ◽  
Biomedical Data ◽  
Care Providers ◽  
Proportional Hazards Models ◽  
The Difference

Aging-related physiological changes are systemic and, at least in humans, are linearly associated with age. Therefore, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. Aging acceleration, defined as the difference between the predicted and chronological age was found to be elevated in patients with major diseases and is predictive of mortality. In this work, we compare three increasingly accurate biological age models: metrics derived from unsupervised Principal Components Analysis (PCA), alongside two supervised biological age models; a multivariate linear regression and a state-of-the-art deep convolution neural network (CNN). All predictions were made using one-week long locomotor activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) dataset. We found that application of the supervised approaches improves the accuracy of the chronological age estimation at the expense of a loss of the association between the aging acceleration predicted by the model and all-cause mortality. Instead, we turned to the NHANES death register and introduced a novel way to train parametric proportional hazards models in a form suitable for out-of-the-box implementation with any modern machine learning software. Finally, we characterized a proof-of-concept example, a separate deep CNN trained to predict mortality risks that outperformed any of the biological age or simple linear proportional hazards models. Our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

A New Deep Learning Training Scheme: Application to Biomedical Data

2021 ◽  
pp. 451-459
Author(s):  
Jianhong Cheng ◽  
Qichang Zhao ◽  
Lei Xu ◽  
Jin Liu
Keyword(s):  
Deep Learning ◽  
Biomedical Data ◽  
Training Scheme

Deep Learning in Biomedical Data Science

2018 ◽  
Vol 1 (1) ◽  
pp. 181-205 ◽  
Author(s):  
Pierre Baldi
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Spatial Scale ◽  
Biomedical Imaging ◽  
Data Science ◽  
Black Box ◽  
Biomedical Data ◽  
Learning Methods

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.

scholarly journals Biomedical Image Segmentation and Analysis in Deep Learning

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Tuan Anh Tran ◽  
Tien Dung Cao ◽  
Vu-Khanh Tran ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Patient Treatment ◽  
Biomedical Data ◽  
Biomedical Image Processing ◽  
Human Organ ◽  
Biomedical Image ◽  
Breast Segmentation ◽  
Effective Diagnosis ◽  
Disease Analysis

Biomedical Image Processing, such as human organ segmentation and disease analysis, is a modern field in medicine development and patient treatment. Besides there are many kinds of image formats, the diversity and complexity of biomedical data is still a big issue to all of researchers in their applications. In order to deal with the problem, deep learning give us a successful and effective solutions. Unet and LSTM are two general approaches to the most of case of medical image data. While Unet helps to teach a machine in learning data from each image accompanied with its labelled information, LSTM helps to remember states from many slices of images by times. Unet gives us the segmentation of tumor, abnormal things from biomedical images and then the LSTM gives us the effective diagnosis on a patient disease. In this paper, we show some scenarios of using Unets and LSTM to segment and analysis on many kinds of human organ images and results of brain, retinal, skin, lung and breast segmentation.

scholarly journals CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

2020 ◽  
Author(s):  
Tunca Doğan ◽  
Heval Atas ◽  
Vishal Joshi ◽  
Ahmet Atakan ◽  
Ahmet Sureyya Rifaioglu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Large Scale ◽  
Host Protein ◽  
Knowledge Graph ◽  
Biomedical Data ◽  
Data Resource ◽  
Graph Representations ◽  
Link Type ◽  
Systemic Analysis

AbstractSystemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

scholarly journals Bias invariant RNA-seq metadata annotation

2020 ◽  
Author(s):  
Hannes Wartmann ◽  
Sven Heins ◽  
Karin Kloiber ◽  
Stefan Bonn
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Tissue Sample ◽  
Large Data ◽  
Biomedical Data ◽  
Rna Seq ◽  
Adaptation Algorithm ◽  
Data Repositories ◽  
Technological Advances ◽  
Metadata Annotation

AbstractRecent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Here we investigate RNA-seq metadata prediction based on gene expression values. We present a deep-learning based domain adaptation algorithm for the automatic annotation of RNA-seq metadata. We show how our algorithm outperforms existing approaches as well as traditional deep learning methods for the prediction of tissue, sample source, and patient sex information across several large data repositories. By using a model architecture similar to siamese networks the algorithm is able to learn biases from datasets with few samples. Our domain adaptation approach achieves metadata annotation accuracies up to 12.3% better than a previously published method. Lastly, we provide a list of more than 10,000 novel tissue and sex label annotations for 8,495 unique SRA samples.

scholarly journals Deep learning for biological age estimation

2020 ◽  
Author(s):  
Syed Ashiqur Rahman ◽  
Peter Giacobbi ◽  
Lee Pyles ◽  
Charles Mullett ◽  
Gianfranco Doretto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Age Estimation ◽  
Current Data ◽  
Biological Age ◽  
Machine Learning Techniques ◽  
Biological Aging ◽  
Feature Engineering ◽  
Complex Data ◽  
Aging Research

Abstract Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

scholarly journals Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

2021 ◽  
Vol 11 (4) ◽  
pp. 280
Author(s):  
Andrea Termine ◽  
Carlo Fabrizio ◽  
Claudia Strafella ◽  
Valerio Caputo ◽  
Laura Petrosini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Neurodegenerative Disorders ◽  
Drug Repurposing ◽  
Biomedical Data ◽  
Parkinson’S Diseases ◽  
Made In

In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data.

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