Deterministic Modeling of Copolymer Microstructure: Composition Drift and Sequence Patterns

2014 ◽  
Vol 9 (3) ◽  
pp. 285-306 ◽  
Author(s):  
Ivan Kryven ◽  
Piet D. Iedema
Keyword(s):  
Deterministic Modeling ◽  
Copolymer Microstructure ◽  
Sequence Patterns ◽  
Composition Drift

scholarly journals The effect of composition drift and copolymer microstructure on mechanical bulk properties of styrene-methyl acrylate emulsion copolymers

1995 ◽  
Vol 92 (1) ◽  
pp. 133-156 ◽  
Author(s):  
Harold A. S. Schoonbrood ◽  
Harry M. G. Brouns ◽  
Henk A. Thijssen ◽  
Alex M. van Herk ◽  
Anton L. German
Keyword(s):  
Methyl Acrylate ◽  
Bulk Properties ◽  
Copolymer Microstructure ◽  
Acrylate Emulsion ◽  
Composition Drift

Flexible k-mers with variable-length indels for identifying binding sequences of protein dimers

2019 ◽  
Vol 21 (5) ◽  
pp. 1787-1797
Author(s):  
Chenyang Hong ◽  
Kevin Y Yip
Keyword(s):  
Binding Proteins ◽  
Affinity Purification ◽  
Classification Performance ◽  
Variable Regions ◽  
Binding Motifs ◽  
New Class ◽  
Protein Dimers ◽  
Sequence Patterns ◽  
Standard Chip ◽  
Exponential Enrichment

Abstract Many DNA-binding proteins interact with partner proteins. Recently, based on the high-throughput consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) method, many such protein pairs have been found to bind DNA with flexible spacing between their individual binding motifs. Most existing motif representations were not designed to capture such flexibly spaced regions. In order to computationally discover more co-binding events without prior knowledge about the identities of the co-binding proteins, a new representation is needed. We propose a new class of sequence patterns that flexibly model such variable regions and corresponding algorithms that identify co-bound sequences using these patterns. Based on both simulated and CAP-SELEX data, features derived from our sequence patterns lead to better classification performance than patterns that do not explicitly model the variable regions. We also show that even for standard ChIP-seq data, this new class of sequence patterns can help discover co-bound events in a subset of sequences in an unsupervised manner. The open-source software is available at https://github.com/kevingroup/glk-SVM.

scholarly journals Use of State Sequence Analysis in Pharmacoepidemiology: A Tutorial

2021 ◽  
Vol 18 (24) ◽  
pp. 13398
Author(s):  
Jacopo Vanoli ◽  
Consuelo Rubina Nava ◽  
Chiara Airoldi ◽  
Andrealuna Ucciero ◽  
Virginio Salvi ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Opioid Therapy ◽  
Prescription Data ◽  
Clustering Methods ◽  
Real World Data ◽  
Complexity Measures ◽  
Opioid Prescription ◽  
Inappropriate Use ◽  
State Sequence ◽  
Sequence Patterns

While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.

scholarly journals Intra-protein binding peptide fragments have specific and intrinsic sequence patterns

2017 ◽  
Author(s):  
Yuhong Wang ◽  
Junzhou Huang ◽  
Wei Li ◽  
Sheng Wang ◽  
Chuanfan Ding
Keyword(s):  
Protein Binding ◽  
Learning Algorithm ◽  
Double Helix ◽  
Peptide Fragments ◽  
Feed Forward Neural Network ◽  
Binding Peptide ◽  
Protein Protein Interaction ◽  
Deep Learning Algorithm ◽  
Sequence Patterns ◽  
Dna Double Helix

AbstractThe key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that similar rules and intrinsic sequence patterns between intra-protein binding peptide fragments do exist, and they can be extracted using a deep learning algorithm. Multi-millions of binding and non-binding peptide fragments from currently available protein X-ray structures are classified with an accuracy of up to 93%. This discovery has the potential in helping solve protein folding and protein-protein interaction problems, two open and fundamental problems in molecular biology.One Sentence SummaryClassification of binding and non-binding intra-protein peptide fragments using feed-forward neural network

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