VoiceLens: A multi-view multi-class disease classification model through daily-life speech data

DNA copy number variations (CNVs) have been shown to be associated with cancer development and progression. The detection of these CNVs has the potential to impact the basic knowledge and treatment of many types of cancers, and can play a role in the discovery and development of molecular-based personalized cancer therapies. One of the most common types of high-resolution chromosomal microarrays is array-based comparative genomic hybridization (aCGH) methods that assay DNA CNVs across the whole genomic landscape in a single experiment. In this article we propose methods to use aCGH profiles to predict disease states. We employ a Bayesian classification model and treat disease states as outcome, and aCGH profiles as covariates in order to identify significant regions of the genome associated with disease subclasses. We propose a principled two-stage method where we first make inferences on the underlying copy number states associated with the aCGH emissions based on hidden Markov model (HMM) formulations to account for serial dependencies in neighboring probes. Subsequently, we infer associations with disease outcomes, conditional on the copy number states, using Bayesian linear variable selection procedures. The selected probes and their effects are parameters that are useful for predicting the disease categories of any additional individuals on the basis of their aCGH profiles. Using simulated datasets, we investigate the method's accuracy in detecting disease category. Our methodology is motivated by and applied to a breast cancer dataset consisting of aCGH profiles assayed on patients from multiple disease subtypes.

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Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

BMC Bioinformatics ◽

10.1186/s12859-020-03848-0 ◽

2020 ◽

Vol 21 (S21) ◽

Author(s):

Taeho Jo ◽

◽

Kwangsik Nho ◽

Shannon L. Risacher ◽

Andrew J. Saykin

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Deep Learning ◽

Early Detection ◽

Medial Temporal Lobe ◽

Memory Loss ◽

Disease Classification ◽

Classification Model ◽

Tau Pet

Abstract Background Alzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. Results The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). Conclusion A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

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Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.712251 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yasunori Yamada ◽

Kaoru Shinkawa ◽

Miyuki Nemoto ◽

Tetsuaki Arai

Keyword(s):

Older Adults ◽

Early Detection ◽

Daily Life ◽

Binary Classification ◽

Emotional States ◽

Automatic Assessment ◽

Adverse Health Effects ◽

Wide Range ◽

Speech Data ◽

Speech Features

Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored. In this study, we developed a tablet-based application and collected speech responses of 57 older adults to daily life questions regarding, for example, one's feelings and future travel plans. From audio data of these speech responses, we automatically extracted speech features characterizing acoustic, prosodic, and linguistic aspects, and investigated their associations with self-rated scores of the UCLA Loneliness Scale. Consequently, we found that with increasing loneliness scores, speech responses tended to have less inflections, longer pauses, reduced second formant frequencies, reduced variances of the speech spectrum, more filler words, and fewer positive words. The cross-validation results showed that regression and binary-classification models using speech features could estimate loneliness scores with an R2 of 0.57 and detect individuals with high loneliness scores with 95.6% accuracy, respectively. Our study provides the first empirical results suggesting the possibility of using speech data that can be collected in everyday life for the automatic assessments of loneliness in older adults, which could help develop monitoring technologies for early detection and intervention for mitigating loneliness.

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Image Processing based Plant Disease Detection using CNN

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35040 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 656-660

Author(s):

Prof. Rachana Sable

Keyword(s):

Technological Development ◽

Real Life ◽

Input Image ◽

Disease Classification ◽

Classification Model ◽

Scientific Development ◽

Disease Detection ◽

Agricultural Field ◽

Detection Techniques ◽

Life Problems

In the era of Scientific Development, many technologies and new ways of solving real-life problems are being invented every day. The basic need of the food is increasing parallelly, due to an increase in population. According to FAO of the UN, annually 20 to 40 percent of crops were lost due to diseases. That’s why technological development in the agricultural field is important to improve the productivity of crops. This major issue can be overcome by implementing disease detection techniques to identify the disease from an input image. This process involves steps like dataset collection, image pre-processing and training a classification model. The dataset consists of plants like cotton, grapes and tomatoes. CNN classification model is used for disease classification. The proposed model gives an accuracy of 96%. After disease classification it also provides information like causes, symptoms, management and diagnostic solutions.

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LAMP: disease classification derived from layered assessment on modules and pathways in the human gene network

10.21203/rs.3.rs-41665/v1 ◽

2020 ◽

Author(s):

Zhilong Mi ◽

Binghui Guo ◽

Xiaobo Yang ◽

Ziqiao Yin ◽

Zhiming Zheng

Keyword(s):

Targeted Therapy ◽

Gene Networks ◽

Gene Network ◽

Human Gene ◽

Disease Classification ◽

Cellular Heterogeneity ◽

Classification Model ◽

Disease Etiology ◽

Disease Associations ◽

Classification Of Diseases

Abstract Background: Classification of diseases based on genetic information is of great significance as the basis for precision medicine, increasing the understanding of disease etiology and revolutionizing personalized medicine. Much effort has been directed at understanding disease associations by constructing disease networks, and classifying patient samples according to gene expression data. Integrating human gene networks overcomes limited coverage of genes. Incorporating pathway information into disease classification procedure addresses the challenge of cellular heterogeneity across patients. Results: In this work, we propose a disease classification model LAMP, which concentrates on the layered assessment on modules and pathways. Directed human gene interactions are the foundation of constructing the human gene network, where the significant roles of disease and pathway genes are recognized. The fast unfolding algorithm identifies 11 modules in the largest connected component. Then layered networks are introduced to distinguish positions of genes in propagating information from sources to targets. After gene screening, hierarchical clustering and refined process, 1726 diseases from KEGG are classified into 18 categories. Also, it is expounded that diseases with overlapping genes may not belong to the same category in LAMP. Within each category, entropy is applied to measure the compositional complexity, and to evaluate the prospects for combination diagnosis and gene-targeted therapy for diseases.Conclusions: In this work, by collecting data from BioGRID and KEGG, we develop a disease classification model LAMP, to support people to view diseases from the perspective of commonalities in etiology and pathology. Comprehensive research on existing diseases can help meet the challenges of unknown diseases. The results provide suggestions for combination diagnosis and gene-targeted therapy, which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies.

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