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Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks [article]

Zhifei Zhang, Wanling Gao, Fan Zhang, Yunyou Huang, Shaopeng Dai, Fanda Fan, Jianfeng Zhan, Mengjia Du, Silin Yin, Longxin Xiong, Juan Du, Yumei Cheng, Xiexuan Zhou, Rui Ren (+2 others)
2019 arXiv   pre-print
Fifth, what are the performance gaps of state-of-the-practice and state-of-the-art systems handling big medical data currently or in future?  ...  Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data?  ...  In the research, the cluster-of-cluster assignments (COCA) algorithm, which is a kind of agglomerative hierarchical clustering method using Pearson correlation as the distance measurement, is adopted to  ... 
arXiv:1901.00642v1 fatcat:fak46q7bgzesll6y4h7i6mcysi

Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

Mario Zanfardino, Monica Franzese, Katia Pane, Carlo Cavaliere, Serena Monti, Giuseppina Esposito, Marco Salvatore, Marco Aiello
2019 Journal of Translational Medicine  
To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool  ...  To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations  ...  This approach includes sparse generalized canonical correlation analysis (sGCCA) [74] , multi-omics factor analysis (MOFA) [75] , and Joint and Individual Variation Explained (JIVE) [76] .  ... 
doi:10.1186/s12967-019-2073-2 pmid:31590671 pmcid:PMC6778975 fatcat:nwlos7y25jgjfh65t7suykhgze

Machine learning in resting-state fMRI analysis [article]

Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
2018 arXiv   pre-print
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data.  ...  Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions.  ...  Acknowledgements This work was supported by NIH R01 grants (R01LM012719 and R01AG053949), the NSF NeuroNex grant 1707312, and NSF CAREER grant (1748377).  ... 
arXiv:1812.11477v1 fatcat:nd6j5jbspzh2rmxyyufdyesxom

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Li Shen, Paul M. Thompson
2019 Proceedings of the IEEE  
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed  ...  Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical  ...  [46] computed a novel disease progression score (DPS) from multimodal neuroimaging data and performed GWAS on it.  ... 
doi:10.1109/jproc.2019.2947272 pmid:31902950 pmcid:PMC6941751 fatcat:rx5b44yv55d2xicdiznnwjdac4

Biomarkers in the primary progressive aphasias

Murray Grossman
2014 Aphasiology  
I review genetic, neuroimaging and biofluid studies that can help determine the pathologic basis for PPA.  ...  serve as the basis for clinical trials in this spectrum of disease.  ...  David Irwin and Corey McMillan for their collaborative support, to the patients with PPA and their families who contribute enthusiastically to our work, and also to Lyndsey Nickels and Karen Croot for  ... 
doi:10.1080/02687038.2014.929631 pmid:25580048 pmcid:PMC4287262 fatcat:rpv2ns23gvfchhdwys2vwxk2ji

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Protima Khan, Md. Fazlul Kader, S. M. Riazul Islam, Aisha B. Rahman, Md. Shahriar Kamal, Masbah Uddin Toha, Kyung-Sup Kwak
2021 IEEE Access  
In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease  ...  In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology.  ...  The proposed mechanism was tested through simulation and real data. The results were compared with sparse canonical correlation analysis.  ... 
doi:10.1109/access.2021.3062484 fatcat:lmhp34ad3zdexb5y4bt5ksntia

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Vincent Grollemund, Pierre-François Pradat, Giorgia Querin, François Delbot, Gaétan Le Chat, Jean-François Pradat-Peyre, Peter Bede
2019 Frontiers in Neuroscience  
Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials.  ...  Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers.  ...  After running a clustering algorithm, we obtain clusters and cluster memberships for each patient.Further analysis of shared traits within the same cluster can help identify novel disease phenotypes.  ... 
doi:10.3389/fnins.2019.00135 pmid:30872992 pmcid:PMC6403867 fatcat:5o6rjl5yjrbhrfxdzj2lq72hzq

The neural circuitry of restricted repetitive behavior: Magnetic resonance imaging in neurodevelopmental disorders and animal models

B.J. Wilkes, M.H. Lewis
2018 Neuroscience and Biobehavioral Reviews  
inform us about the specific neural circuitry of RRB, and (3) suggest future directions for neuroimaging investigations of RRB that have been effectively employed in other areas within neuroscience.  ...  Thus, the remainder of this section will only briefly review common imaging modalities and how they are used to study neural circuitry.  ...  We would also like to thank Lisa Curry-Pochy for generating artwork used for figures in this work. This work was supported by the National Institutes of Health [R01MH080055, R21MH110911].  ... 
doi:10.1016/j.neubiorev.2018.05.022 pmid:29802854 pmcid:PMC6169529 fatcat:mtsoqbihsvbkpa6xrsrroouelu

Abstracts

2012 Dementia and Geriatric Cognitive Disorders  
disease, dementia with Lewy bodies, and Parkinson's disease).  ...  Task performance was correlated with changes in grey matter signal intensity using voxel-based morphometry.  ...  The case is presented for its rarity of relatively late onset of FTD with possible temporal association with a parasagittal meningioma, and the challenges faced in management.  ... 
doi:10.1159/000342903 pmid:23007027 fatcat:io2rfqz7hndlddkrquqhkjhm7m

Introduction to JINS Special Issue on Human Brain Connectivity in the Modern Era: Relevance to Understanding Health and Disease

Deanna M. Barch, Mieke Verfaellie, Stephen M. Rao
2016 Journal of the International Neuropsychological Society  
This work also involved the use of instrumentation supported by the NIH Shared Instrumentation Grant Program and/or High-End Instrumentation Grant Program; specifically, grant number(s) S10RR022976 and  ...  and Bioengineering (NIBIB), National Institutes of Health.  ...  using Analysis of Functional NeuroImages (AFNI).  ... 
doi:10.1017/s1355617716000047 fatcat:f2preenihbes5ftkrxbo7tgt64

Abstracts

2010 Dementia and Geriatric Cognitive Disorders  
Aggregation of tau into paired helical filaments and neurofibrillary tangles. (3) Clustering of tau on microtubules and inhibition of axonal transport, (4) Cleavage of tau into toxic fragments. (5) Toxic  ...  Efforts to develop treatments for tau pathology in Alzheimer's disease may thus prove to be useful in the treatment of the same pathology in other tauopathies.  ...  Generalized Estimating Equations analysis (adjusted for age, sex, and presence or absence of symptoms) was used account for kindred clustering.  ... 
doi:10.1159/000320292 fatcat:3toxl2yk3ffc7kaw4gnbb362wy

ePresentation Sessions

2018 European Journal of Neurology  
to investigate the presence of brain dysfunctional subtypes within our group, iii) seed-based interregional correlation analysis to assess the resting-state networks.  ...  We used descriptive statistics; Wilcoxon-Mann-Whitney parametric hypothesis test, principle component analysis, Mardia multivariate analysis of normality a DBscan cluster analysis to define typical phenotypes  ...  Data on the frequency of both syndromes in Parkinson's disease (PD) is very limited.  ... 
doi:10.1111/ene.13700 fatcat:xk5co6xixzbs3n7wnhc5xjkpv4

CSF neurofilament light levels predict hippocampal atrophy in cognitively healthy older adults

Ane-Victoria Idland, Roser Sala-Llonch, Tom Borza, Leiv Otto Watne, Torgeir Bruun Wyller, Anne Brækhus, Henrik Zetterberg, Kaj Blennow, Kristine Beate Walhovd, Anders Martin Fjell
2017 Neurobiology of Aging  
Acknowledgements The research presented in this thesis was carried out at the Department of Geriatric Medicine,  ...  We performed two cluster analyses: Cluster analysis 1: We used cluster analysis to establish clusters of CSF biomarkers with shared behavior across participants.  ...  We ran two different cluster analyses. Cluster analysis 1: We used cluster analysis to establish clusters of CSF biomarkers with shared behavior across participants.  ... 
doi:10.1016/j.neurobiolaging.2016.09.012 pmid:27794264 fatcat:6sgqujw7jjclhg7mxtvoqptvjy

ACNP 57th Annual Meeting: Poster Session III

2018 Neuropsychopharmacology  
SASP biomarkers were derived from proteomic panel measured by multiplex immunoassay, and the SASP index was derived the regression analysis from a previously published study.  ...  Greater SASP index was negatively correlated with information processing speed (r = -0.31, p < 0.001), executive function (r = -0.27, p < 0.001) and global cognition (r = -0.25, p = 0.01).  ...  K-means clustering revealed poor group identification, with a Rand index of 0.07.  ... 
doi:10.1038/s41386-018-0268-5 fatcat:wiooxnisvfhovadauljiso4ie4

Abstracts from the 35th Annual Meeting of AChemS

2013 Chemical Senses  
To understand the role of endogenously released GLP-1 in the NAc core, we have been performing detailed behavioral analysis of the effects of blockade of GLP-1R at this site.  ...  We continue to use other behavioral approaches to discern effects on palatability and motivation for food.  ...  #P25 POSTER SESSION I: MULTIMODAL RECEPTION; CHEMOSENSATION AND DISEASE; OLFACTION PERIPHERY Differences in Odor Identification between Alzheimer's and Parkinson's Patients #P27 POSTER SESSION #P28  ... 
doi:10.1093/chemse/bjt036 fatcat:y46sw4p335epfkuraityepgpnm
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