AIMC Topic: Molecular Sequence Data

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Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC.

Computer methods and programs in biomedicine Jul 22, 2015
Heat Shock Proteins (HSPs) are the substantial ingredients for cell growth and viability, which are found in all living organisms. HSPs manage the process of folding and unfolding of proteins, the quality of newly synthesized proteins and protecting ...
Reproducibility of Results Sensitivity and Specificity Amino Acid Sequence Machine Learning Algorithms Molecular Sequence Data HSP70 Heat-Shock Proteins Sequence Alignment Sequence Analysis, Protein Heat-Shock Proteins Dipeptides Pattern Recognition, Automated
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PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

BMC bioinformatics Jul 5, 2015
BACKGROUND: Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides in...
Disulfides Molecular Sequence Data Cystine Algorithms Models, Statistical Animals Peptide Fragments Support Vector Machine Amino Acid Sequence
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TRAL: tandem repeat annotation library.

Bioinformatics (Oxford, England) May 18, 2015
MOTIVATION: Currently, more than 40 sequence tandem repeat detectors are published, providing heterogeneous, partly complementary, partly conflicting results.
Tandem Repeat Sequences Databases, Protein Amino Acid Sequence Knowledge Bases Gene Library Molecular Sequence Data Documentation Molecular Sequence Annotation Humans Software Cluster Analysis
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Machine learning assisted design of highly active peptides for drug discovery.

PLoS computational biology Apr 7, 2015
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...
Antimicrobial Cationic Peptides Structure-Activity Relationship Amino Acid Sequence Protein Interaction Mapping Molecular Sequence Data Pattern Recognition, Automated Bacterial Physiological Phenomena Machine Learning Peptides Drug Discovery Sequence Analysis, Protein
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Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

Journal of theoretical biology Apr 2, 2015
As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimen...
Binding Sites Succinates Molecular Sequence Data Protein Processing, Post-Translational Amino Acid Sequence Internet Artificial Intelligence Computer Simulation Proteins Computational Biology Supervised Machine Learning Lysine Algorithms Amino Acids Reproducibility of Results Support Vector Machine
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Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

BMC systems biology Feb 6, 2015
BACKGROUND: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome...
Computational Biology Protein Conformation Sequence Analysis, Protein Support Vector Machine Nucleic Acid Conformation Models, Molecular Internet Molecular Sequence Data DNA-Binding Proteins Amino Acid Sequence
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An improved poly(A) motifs recognition method based on decision level fusion.

Computational biology and chemistry Dec 30, 2014
Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene...
Poly A Polyadenylation RNA, Messenger Computational Biology Support Vector Machine Principal Component Analysis Nucleotide Motifs Molecular Sequence Data
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Using support vector machines to identify protein phosphorylation sites in viruses.

Journal of molecular graphics & modelling Dec 24, 2014
Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of normal host-cell functions. Given its importance in biology, a unique opportunity has arisen to identify viral protein phosphorylation sites. However, ...
Support Vector Machine Amino Acid Sequence Tyrosine Molecular Sequence Data Viral Proteins Databases, Protein Protein Structure, Tertiary Serine Phosphorylation Threonine Phosphoproteins
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Genetics. Predicting the impact of genomic variation.

Nature methods Mar 1, 2016
Models, Genetic Humans Models, Statistical Molecular Sequence Data Base Sequence Computer Simulation Unsupervised Machine Learning Sequence Analysis, DNA Chromosome Mapping Genome, Human Genetic Variation
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CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences.

The Journal of chemical physics Dec 28, 2015
We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-g...
Amino Acid Sequence Molecular Sequence Data Huntingtin Protein Computer Simulation Nerve Tissue Proteins Humans Machine Learning
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