Watanabe2018_Simple markov model for a disease model
Ontology highlight
ABSTRACT:
SUBMITTER: 
Leandro Watanabe
PROVIDER: MODEL1803120002 | BioModels | 2019-01-30
REPOSITORIES: BioModels
Similar Datasets
Project description:State transition diagram of a three-state Markov model.
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal
The yearly transition probabilities are:
Healthy to Dead: 0.01; Healthy to Sick: 0.2; Sick to Healthy: 0.1;Sick to Dead: 0.3.
Initial conditions: Healthy = 100, Sick = 0 and Dead = 0.
Output: Number of people in each state for years 1-10.
2019-01-30 | MODEL1803120003 | BioModels
Project description:Simple Markov model.
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal.
The yearly transition probabilities are:
Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3.
The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male.
Initial conditions: Healthy = (50 Male, 50 Female), Sick = (0,0) and Dead = (0,0).
Output: Number of men and women in each disease state for years 1-10.
2019-01-30 | MODEL1803120004 | BioModels
Project description:Model dependent on changing parameters.
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal.
The yearly transition probabilities are:
Healthy to Dead: Age/1000; Healthy to Sick: according to function F1 depending on Age and Male parameters; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male parameters. Pre-Transition Rules: Age increased by 1 each cycle.
Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,…,50 for each individual), Sick = (0,0) and Dead = (0,0).
Output: Number of men and women in each disease state for years 1-10 and their ages in each state.
2019-01-30 | MODEL1803120005 | BioModels
Project description:Model with functions depending on Age, Male, BP (Blood Pressure).
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: Age/1000; Healthy to Sick: According to function F1 depending on Age and Male and BP; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male.
Pre-Transition Rules: Age increased by 1 and BP by Age/10 each simulation cycle. Post-Transition Rules: Treatment = BP>140 , becomes 1 when BP crosses 140 threshold; BP =BP-Treatment*10 , meaning a drop of 10 once treatment is applied; CostThisYear = Age + \Treatment*10 , cost depends on age and if treatment was taken; Cost= Cost + CostThisYear , it accumulates cost over time.
Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,...,50 for each individual), BP =120, Sick = (0,0) and Dead = (0,0).
Output: Number of men and women in each disease state for years 1-10 and their ages and costs in each state. A stratified report by male and female and young – up to age 30 and old above age 30 is produced.
2019-01-30 | MODEL1803120006 | BioModels
Project description:The interaction between RNA polymerase II (RNAPII) and RNA processing and packaging factors is strongly influenced by the C-terminal domain (CTD), which consists of multiple heptad repeats that can be differentially phosphorylated at five positions. Here we report strand-specific, high-resolution profiling of the five types of RNAPII CTD phosphorylation in yeast using crosslinking and analysis of modified polymerase (CLAMP). The 5’ regions of protein coding genes showed enrichment of Ser5P, and depletion of Tyr1P, Ser2P, Thr4P and Ser7P. CTD phosphorylation pattern boundaries were associated with known sites of RNAPII pausing, splicing, and nucleosome positioning. To integrate the distribution of the RNAPII modifications across all transcription units, we developed an eight-state, strand-specific Hidden Markov Model. This identified distinct modification states associated with initiating, early elongating and later elongating RNAPII. The initiation state was enriched near the Transcription Start Site (TSS) of mRNAs and ncRNAs, and persisted throughout the 1st exon of intron-containing genes. Notably, unstable ncRNAs failed to transition into the elongation states seen on protein coding genes, and their early termination and rapid degradation probably reflect this failure.
2016-06-08 | GSE69676 | GEO
Project description:This track displays a chromatin state segmentation for each of nine human cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF). A common set of states across the cell types were learned by computationally integrating ChIP-seq data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) using a Hidden Markov Model (HMM). In total, fifteen states were used to segment the genome, and these states were then grouped and colored to highlight predicted functional elements. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq data from the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track was used to generate this track. Data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) and nine cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF) was binarized separately at a 200 base pair resolution based on a Poisson background model. The chromatin states were learned from this binarized data using a multivariate Hidden Markov Model (HMM) that explicitly models the combinatorial patterns of observed modifications (Ernst and Kellis, 2010). To learn a common set of states across the nine cell types, first the genomes were concatenated across the cell types. For each of the nine cell types, each 200 base pair interval was then assigned to its most likely state under the model. Detailed information about the model parameters and state enrichments can be found in (Ernst et al, accepted). This is release 1 (Jun 2011) of this track, and it is based on the NCBI36/hg18 release of the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track. This track has also been lifted over to GRCh37/hg19 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHmm). It is anticipated that the HMM methods will be run on the newer GRCh37/hg19 Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHistone) data and will replace the lifted version.
2012-05-23 | E-GEOD-38163 | biostudies-arrayexpress
Project description:The pancreas is a vital organ with digestive and endocrine roles, and diseases of the pancreas affect millions of people yearly. A better understanding of the pancreas proteome and its dynamic post-translational modifications (PTMs) is necessary to engineer higher fidelity tissue analogues for use in transplantation. The extracellular matrix (ECM) has major roles in binding and signaling essential to the viability of insulin-producing islets of Langerhans. To analyze post-translational modifications (PTMs) in the pancreas, native and decellularized tissues from four donors were analyzed.
2021-08-06 | PXD025048 | Pride
Project description:An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.
2020-11-03 | BIOMD0000000974 | BioModels
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (mailto:jernst@mit.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track displays a chromatin state segmentation for each of nine human cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF). A common set of states across the cell types were learned by computationally integrating ChIP-seq data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) using a Hidden Markov Model (HMM). In total, fifteen states were used to segment the genome, and these states were then grouped and colored to highlight predicted functional elements. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-05-24 | GSE38163 | GEO