213 research outputs found
content based music access an approach and its applications
At current time, the availability of largemusic repositories poses challenging research problems. Among all, content-based identification is gaining an increasing interest because it can provide new tools for easy access and retrieval. In this paper we describe an ongoing methodology for the content-based identification of unknown music recordings through a collection of music documents. Moreover, as future prospective scenario, identification is viewed in a more general similarity context, where also the perception of the users is considered
High throughput interactome determination via sulfur anomalous scattering
We propose a novel approach to detect the binding between proteins making use
of the anomalous diffraction of natively present heavy elements inside the
molecule 3D structure. In particular, we suggest considering sulfur atoms
contained in protein structures at lower percentages than the other atomic
species. Here, we run an extensive preliminary investigation to probe both the
feasibility and the range of usage of the proposed protocol. In particular, we
(i) analytically and numerically show that the diffraction patterns produced by
the anomalous scattering of the sulfur atoms in a given direction depend
additively on the relative distances between all couples of sulfur atoms. Thus
the differences in the patterns produced by bound proteins with respect to
their non-bonded states can be exploited to rapidly assess protein complex
formation. Next, we (ii) carried out analyses on the abundances of sulfurs in
the different proteomes and molecular dynamics simulations on a representative
set of protein structures to probe the typical motion of sulfur atoms. Finally,
we (iii) suggest a possible experimental procedure to detect protein-protein
binding. Overall, the completely label-free and rapid method we propose may be
readily extended to probe interactions on a large scale even between other
biological molecules, thus paving the way to the development of a novel field
of research based on a synchrotron light source.Comment: 9 pages, 4 figure
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Deriving disease subtypes from electronic health records (EHRs) can guide
next-generation personalized medicine. However, challenges in summarizing and
representing patient data prevent widespread practice of scalable EHR-based
stratification analysis. Here we present an unsupervised framework based on
deep learning to process heterogeneous EHRs and derive patient representations
that can efficiently and effectively enable patient stratification at scale. We
considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising
of a total of 57,464 clinical concepts. We introduce a representation learning
model based on word embeddings, convolutional neural networks, and autoencoders
(i.e., ConvAE) to transform patient trajectories into low-dimensional latent
vectors. We evaluated these representations as broadly enabling patient
stratification by applying hierarchical clustering to different multi-disease
and disease-specific patient cohorts. ConvAE significantly outperformed several
baselines in a clustering task to identify patients with different complex
conditions, with 2.61 entropy and 0.31 purity average scores. When applied to
stratify patients within a certain condition, ConvAE led to various clinically
relevant subtypes for different disorders, including type 2 diabetes,
Parkinson's disease and Alzheimer's disease, largely related to comorbidities,
disease progression, and symptom severity. With these results, we demonstrate
that ConvAE can generate patient representations that lead to clinically
meaningful insights. This scalable framework can help better understand varying
etiologies in heterogeneous sub-populations and unlock patterns for EHR-based
research in the realm of personalized medicine.Comment: C.F. and R.M. share senior authorshi
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Replicating Cardiovascular Condition-Birth Month Associations
Independent replication is vital for study findings drawn from Electronic Health Records (EHR). This replication study evaluates the relationship between seasonal effects at birth and lifetime cardiovascular condition risk. We performed a Season-wide Association Study on 1,169,599 patients from Mount Sinai Hospital (MSH) to compute phenome-wide associations between birth month and CVD. We then evaluated if seasonal patterns found at MSH matched those reported at Columbia University Medical Center. Coronary arteriosclerosis, essential hypertension, angina, and pre-infarction syndrome passed phenome-wide significance and their seasonal patterns matched those previously reported. Atrial fibrillation, cardiomyopathy, and chronic myocardial ischemia had consistent patterns but were not phenome-wide significant. We confirm that CVD risk peaks for those born in the late winter/early spring among the evaluated patient populations. The replication findings bolster evidence for a seasonal birth month effect in CVD. Further study is required to identify the environmental and developmental mechanisms
PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.
MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online
Measurement of the top quark-pair production cross section with ATLAS in pp collisions at \sqrt{s}=7\TeV
A measurement of the production cross-section for top quark pairs(\ttbar)
in collisions at \sqrt{s}=7 \TeV is presented using data recorded with
the ATLAS detector at the Large Hadron Collider. Events are selected in two
different topologies: single lepton (electron or muon ) with large
missing transverse energy and at least four jets, and dilepton (,
or ) with large missing transverse energy and at least two jets. In a
data sample of 2.9 pb-1, 37 candidate events are observed in the single-lepton
topology and 9 events in the dilepton topology. The corresponding expected
backgrounds from non-\ttbar Standard Model processes are estimated using
data-driven methods and determined to be events and events, respectively. The kinematic properties of the selected events are
consistent with SM \ttbar production. The inclusive top quark pair production
cross-section is measured to be \sigmattbar=145 \pm 31 ^{+42}_{-27} pb where
the first uncertainty is statistical and the second systematic. The measurement
agrees with perturbative QCD calculations.Comment: 30 pages plus author list (50 pages total), 9 figures, 11 tables,
CERN-PH number and final journal adde
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