Network Inference
... Correlation between two time points of different x’es Correlation between two time points of x and f This defines a prior on the observables Then observe and a posterior distribution is defined ...
... Correlation between two time points of different x’es Correlation between two time points of x and f This defines a prior on the observables Then observe and a posterior distribution is defined ...
Computational Prediction of Beta Structure from Amino Acid
... Sequence in a Class of Pathologically Relevant Proteins Abstract Objectives/Goals Because structure dictates the function of proteins - physiological or pathological - protein structure discovery is of great interest to biological science. Though experimental approaches have yielded good results, th ...
... Sequence in a Class of Pathologically Relevant Proteins Abstract Objectives/Goals Because structure dictates the function of proteins - physiological or pathological - protein structure discovery is of great interest to biological science. Though experimental approaches have yielded good results, th ...
Document
... MEME is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letterprobability matrices which describe the probability of each po ...
... MEME is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letterprobability matrices which describe the probability of each po ...
Dynamic Network Inference
... Definition: A Stochastic Process X(t) is a GP if all finite sets of time points, t1,t2,..,tk, defines stochastic variable that follows a multivariate Normal distribution, N(μ,Σ), where μ is the k-dimensional mean and Σ is the k*k dimensional covariance matrix. Examples: Brownian Motion: All incremen ...
... Definition: A Stochastic Process X(t) is a GP if all finite sets of time points, t1,t2,..,tk, defines stochastic variable that follows a multivariate Normal distribution, N(μ,Σ), where μ is the k-dimensional mean and Σ is the k*k dimensional covariance matrix. Examples: Brownian Motion: All incremen ...
PPT
... Albert et al. (2007) A Novel Method for Signal Trnasduction Network Inference from Indirect Experimental Evidence JCompuBiol. 14.7.927Li et l. (2006) Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling. PLOS Biol. 4.10.1732- ...
... Albert et al. (2007) A Novel Method for Signal Trnasduction Network Inference from Indirect Experimental Evidence JCompuBiol. 14.7.927Li et l. (2006) Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling. PLOS Biol. 4.10.1732- ...
BONFACE ISIAHO ASILIGWA P58/75814/2012 ASSIGNMENT III
... Comparing bottom-up and top-down string alignment algorithms , both do almost the same work. The Top-down (memorized) version pays a penalty in recursion overhead, but can potentially be faster than the bottom-up version in situations where some of the sub problems never get examined at all. These d ...
... Comparing bottom-up and top-down string alignment algorithms , both do almost the same work. The Top-down (memorized) version pays a penalty in recursion overhead, but can potentially be faster than the bottom-up version in situations where some of the sub problems never get examined at all. These d ...
Gene network inference - Institute for Mathematics and its
... • The procedure finds the interaction coefficients iteratively for each gene xi. • A partial F test is constructed to compare the total square error of the predicted gene trajectory with a specific subset of coefficients being added or removed. • If the p-value obtained from the test exceeds a certa ...
... • The procedure finds the interaction coefficients iteratively for each gene xi. • A partial F test is constructed to compare the total square error of the predicted gene trajectory with a specific subset of coefficients being added or removed. • If the p-value obtained from the test exceeds a certa ...
(Biological) networks
... This lecture introduces basic concepts of network description and analysis, especially as applied to molecular biological networks, their analysis and reconstruction. (Biological) networks A network is collection of “things” (represented as nodes) and their relationships/interactions (represented as ...
... This lecture introduces basic concepts of network description and analysis, especially as applied to molecular biological networks, their analysis and reconstruction. (Biological) networks A network is collection of “things” (represented as nodes) and their relationships/interactions (represented as ...
341- INTRODUCTION TO BIOINFORMATICS Overview of the …
... • What is gene expression? • What are microarrays? • What are the steps of microarray experiments? • Statistical significance tests that are used for evaluating the results of microarray experiments; e.g., t -test. • Data clustering / classification – Distance Measures – Hierarchical clustering – De ...
... • What is gene expression? • What are microarrays? • What are the steps of microarray experiments? • Statistical significance tests that are used for evaluating the results of microarray experiments; e.g., t -test. • Data clustering / classification – Distance Measures – Hierarchical clustering – De ...
Network motif
All networks, including biological networks, social networks, technological networks (e.g., computer networks and electrical circuits) and more, can be represented as graphs, which include a wide variety of subgraphs. One important local property of networks are so-called network motifs, which are defined as recurrent and statistically significant sub-graphs or patterns.Network motifs are sub-graphs that repeat themselves in a specific network or even among various networks. Each of these sub-graphs, defined by a particular pattern of interactions between vertices, may reflect a framework in which particular functions are achieved efficiently. Indeed, motifs are of notable importance largely because they may reflect functional properties. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Although network motifs may provide a deep insight into the network’s functional abilities, their detection is computationally challenging.