Results 91 to 100 of about 1,012,468 (372)

Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator [PDF]

open access: yes, 2012
When an unbiased estimator of the likelihood is used within a Metropolis–Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this estimator against the asymptotic variances of the averages computed under this ...
A. Doucet   +3 more
semanticscholar   +1 more source

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

open access: yesFEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan   +2 more
wiley   +1 more source

ERBIN limits epithelial cell plasticity via suppression of TGF‐β signaling

open access: yesFEBS Letters, EarlyView.
In breast and lung cancer patients, low ERBIN expression correlates with poor clinical outcomes. Here, we show that ERBIN inhibits TGF‐β‐induced epithelial‐to‐mesenchymal transition in NMuMG breast and A549 lung adenocarcinoma cell lines. ERBIN suppresses TGF‐β/SMAD signaling and reduces TGF‐β‐induced ERK phosphorylation.
Chao Li   +3 more
wiley   +1 more source

On Bayes estimates

open access: yesJournal of Multivariate Analysis, 1973
AbstractIn this paper the author tries to give general conditions for the existence of Bayes estimates and for the consistency of sequences of Bayes estimates.In Section 3 we prove existence theorems for Bayes estimates, which contain those of DeGroot and Rao [3], as a special case.
openaire   +3 more sources

Aggregated estimating equation estimation [PDF]

open access: yesStatistics and Its Interface, 2011
Motivated by the recent active research on online analytical processing (OLAP), we develop a computation and storage efficient algorithm for estimating equation (EE) estimation in massive data sets using a “divide-and-conquer” strategy. In each partition of the data set, we compress the raw data into some low dimensional statistics and then discard the
Ruibin Xi, Nan Lin
openaire   +2 more sources

Thermostable neutral metalloprotease from Geobacillus sp. EA1 does not share thermolysin's preference for substrates with leucine at the P1′ position

open access: yesFEBS Letters, EarlyView.
Knowing how proteases recognise preferred substrates facilitates matching proteases to applications. The S1′ pocket of protease EA1 directs cleavage to the N‐terminal side of hydrophobic residues, particularly leucine. The S1′ pocket of thermolysin differs from EA's at only one position (leucine in place of phenylalanine), which decreases cleavage ...
Grant R. Broomfield   +3 more
wiley   +1 more source

M ESTIMATION, S ESTIMATION, AND MM ESTIMATION IN ROBUST REGRESSION [PDF]

open access: yesInternational Journal of Pure and Apllied Mathematics, 2014
In regression analysis the use of least squares method would not be appropriate in solving problem containing outlier or extreme observations. So we need a parameter estimation method which is robust where the value of the estimation is not much affected by small changes in the data. In this paper we present M estimation, S estimation and MM estimation
Y. Susanti   +3 more
openaire   +1 more source

Exploring lipid diversity and minimalism to define membrane requirements for synthetic cells

open access: yesFEBS Letters, EarlyView.
Designing the lipid membrane of synthetic cells is a complex task, in which its various roles (among them solute transport, membrane protein support, and self‐replication) should all be integrated. In this review, we report the latest top‐down and bottom‐up advances and discuss compatibility and complexity issues of current engineering approaches ...
Sergiy Gan   +2 more
wiley   +1 more source

On the estimation of the derivatives of a function with the derivatives of an estimate

open access: yesJournal of Multivariate Analysis, 1989
Let \({\tilde \theta}{}_ n(x)\) be an estimator of a smooth function \(\theta\) (x). It is proved that \(\theta\) (x) can be estimated easier than its derivative \(\theta^{(s)}(x)\), providing for \(\| {\hat \theta}_ n^{(s)}-\theta^{(s)}\|_ q\) an upper bound that depends on \(\| {\hat \theta}_ n-\theta \|_ q\).
openaire   +4 more sources

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