Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Uncovering hidden disease patterns by simulating clinical diagnostic processes.

ABSTRACT: Choosing a sequence of observations (often with stochastic outcomes) which maximizes the information gain from a system of interacting variables is essential for a wide range of problems in science and technology, such as clinical diagnostic problems. Here, we use a probabilistic model of diseases and signs/symptoms to simulate the effects of medical decisions on the quality of diagnosis by maximizing an appropriate objective function of the medical observations. The study provides a systematic way of proposing new medical tests, considering the significance of diseases and cost of the suggested observations. The efficacy of methods and role of the objective functions as well as initial signs/symptoms are examined by numerical simulations of the diagnostic process by exhaustive or Monte Carlo sampling algorithms.

SUBMITTER: Ramezanpour A

PROVIDER: S-EPMC5799257 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Uncovering hidden disease patterns by simulating clinical diagnostic processes.

Ramezanpour Abolfazl A Mashaghi Alireza A

Scientific reports 20180205 1

Choosing a sequence of observations (often with stochastic outcomes) which maximizes the information gain from a system of interacting variables is essential for a wide range of problems in science and technology, such as clinical diagnostic problems. Here, we use a probabilistic model of diseases and signs/symptoms to simulate the effects of medical decisions on the quality of diagnosis by maximizing an appropriate objective function of the medical observations. The study provides a systematic ...[more]

PMID: 29402991

Similar Datasets

SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data

Project description: Not available

| S-EPMC10817921 | biostudies-literature

SpotLight Proteomics: uncovering the hidden blood proteome improves diagnostic power of proteomics.

Project description:The human blood proteome is frequently assessed by protein abundance profiling using a combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS). In traditional sequence database search, many good-quality MS/MS data remain unassigned. Here we uncover the hidden part of the blood proteome via novel SpotLight approach. This method combines de novo MS/MS sequencing of enriched antibodies and co-extracted proteins with subsequent label-free quantification of new and known peptides in both enriched and unfractionated samples. In a pilot study on differentiating early stages of Alzheimer's disease (AD) from Dementia with Lewy Bodies (DLB), on peptide level the hidden proteome contributed almost as much information to patient stratification as the apparent proteome. Intriguingly, many of the new peptide sequences are attributable to antibody variable regions, and are potentially indicative of disease etiology. When the hidden and apparent proteomes are combined, the accuracy of differentiating AD (n?=?97) and DLB (n?=?47) increased from ?85% to ?95%. The low added burden of SpotLight proteome analysis makes it attractive for use in clinical settings.

| S-EPMC5294601 | biostudies-literature

Uncovering the relation between clinical reasoning and diagnostic accuracy - An analysis of learner's clinical reasoning processes in virtual patients.

Project description:BACKGROUND:Clinical reasoning is an important topic in healthcare training, assessment, and research. Virtual patients (VPs) are a safe environment to teach, assess and perform research on clinical reasoning and diagnostic accuracy. Our aim was to explore the details of the clinical reasoning process and diagnostic accuracy of undergraduate medical students when working with VPs using a concept mapping tool. METHODS:Over seven months we provided access to 67 German and 30 English VPs combined with a concept mapping tool to visualize and measure the clinical reasoning process of identifying problems, differential diagnoses, recommended tests and treatment options, and composing a summary statement about a VP. A final diagnosis had to be submitted by the learners in order to conclude the VP scenario. Learners were allowed multiple attempts or could request the correct diagnosis from the system. RESULTS:We analyzed 1,393 completed concept maps from 317 learners. We found significant differences between maps with a correct final diagnosis on one or multiple attempts and maps in which learners gave up and requested the solution from the system. These maps had lower scores, fewer summary statements, and fewer problems, differential diagnoses, tests, and treatments. CONCLUSIONS:The different use patterns and scores between learners who had the correct final diagnosis on one or multiple attempts and those who gave up, indicate that diagnostic accuracy in the form of a correct final diagnosis on the first attempt has to be reconsidered as a sole indicator for clinical reasoning competency. For the training, assessment, and research of clinical reasoning we suggest focusing more on the details of the process to reach a correct diagnosis, rather than whether it was made in the first attempt.

| S-EPMC6171878 | biostudies-literature

Uncovering hidden variation in polyploid wheat.

Project description:Comprehensive reverse genetic resources, which have been key to understanding gene function in diploid model organisms, are missing in many polyploid crops. Young polyploid species such as wheat, which was domesticated less than 10,000 y ago, have high levels of sequence identity among subgenomes that mask the effects of recessive alleles. Such redundancy reduces the probability of selection of favorable mutations during natural or human selection, but also allows wheat to tolerate high densities of induced mutations. Here we exploited this property to sequence and catalog more than 10 million mutations in the protein-coding regions of 2,735 mutant lines of tetraploid and hexaploid wheat. We detected, on average, 2,705 and 5,351 mutations per tetraploid and hexaploid line, respectively, which resulted in 35-40 mutations per kb in each population. With these mutation densities, we identified an average of 23-24 missense and truncation alleles per gene, with at least one truncation or deleterious missense mutation in more than 90% of the captured wheat genes per population. This public collection of mutant seed stocks and sequence data enables rapid identification of mutations in the different copies of the wheat genes, which can be combined to uncover previously hidden variation. Polyploidy is a central phenomenon in plant evolution, and many crop species have undergone recent genome duplication events. Therefore, the general strategy and methods developed herein can benefit other polyploid crops.

| S-EPMC5307431 | biostudies-literature

Uncovering hidden duplicated content in public transcriptomics data.

Project description:As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data.

| S-EPMC3595988 | biostudies-literature

Uncovering ecological state dynamics with hidden Markov models.

Project description:Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

| S-EPMC7702077 | biostudies-literature

Meta graphical lasso: uncovering hidden interactions among latent mechanisms.

Project description:In complex systems, it's crucial to uncover latent mechanisms and their context-dependent relationships. This is especially true in medical research, where identifying unknown cancer mechanisms and their impact on phenomena like drug resistance is vital. Directly observing these mechanisms is challenging due to measurement complexities, leading to an approach that infers latent mechanisms from observed variable distributions. Despite machine learning advancements enabling sophisticated generative models, their black-box nature complicates the interpretation of complex latent mechanisms. A promising method for understanding these mechanisms involves estimating latent factors through linear projection, though there's no assurance that inferences made under specific conditions will remain valid across contexts. We propose a novel solution, suggesting data, even from systems appearing complex, can often be explained by sparse dependencies among a few common latent factors, regardless of the situation. This simplification allows for modeling that yields significant insights across diverse fields. We demonstrate this with datasets from finance, where we capture societal trends from stock price movements, and medicine, where we uncover new insights into cancer drug resistance through gene expression analysis.

| S-EPMC11300637 | biostudies-literature

Uncovering the hidden ground state of green fluorescent protein.

Project description:The fluorescence properties of GFP are strongly influenced by the protonation states of its chromophore and nearby amino acid side chains. In the ground state, the GFP chromophore is neutral and absorbs in the near UV. Upon excitation, the chromophore is deprotonated, and the resulting anionic chromophore emits its green fluorescence. So far, only excited-state intermediates have been observed in the GFP photocycle. We have used ultrafast multipulse control spectroscopy to prepare and directly observe GFP's hidden anionic ground-state intermediates as an integral part of the photocycle. Combined with dispersed multichannel detection and advanced global analysis techniques, the existence of two distinct anionic ground-state intermediates, I(1) and I(2), has been unveiled. I(1) and I(2) absorb at 500 and 497 nm, respectively, and interconvert on a picosecond timescale. The I(2) intermediate has a lifetime of 400 ps, corresponding to a proton back-transfer process that regenerates the neutral ground state. Hydrogen/deuterium exchange of the protein leads to a significant increase of the I(1) and I(2) lifetimes, indicating that proton motion underlies their dynamics. We thus have assessed the complete chain of reaction intermediates and associated timescales that constitute the photocycle of GFP. Many elementary processes in biology rely on proton transfers that are limited by slow diffusional events, which seriously precludes their characterization. We have resolved the true reaction rate of a proton transfer in the molecular ground state of GFP, and our results may thus aid in the development of a generic understanding of proton transfer in biology.

| S-EPMC539731 | biostudies-literature

Gene-expression patterns reveal underlying biological processes in Kawasaki disease.

Project description:This SuperSeries is composed of the SubSeries listed below.

2007-12-13 | GSE9873 | GEO

Simulating Finite-Time Isothermal Processes with Superconducting Quantum Circuits.

Project description:Finite-time isothermal processes are ubiquitous in quantum-heat-engine cycles, yet complicated due to the coexistence of the changing Hamiltonian and the interaction with the thermal bath. Such complexity prevents classical thermodynamic measurements of a performed work. In this paper, the isothermal process is decomposed into piecewise adiabatic and isochoric processes to measure the performed work as the internal energy change in adiabatic processes. The piecewise control scheme allows the direct simulation of the whole process on a universal quantum computer, which provides a new experimental platform to study quantum thermodynamics. We implement the simulation on ibmqx2 to show the 1/τ scaling of the extra work in finite-time isothermal processes.

| S-EPMC8002232 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data