AIMC Topic: Proteome
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Integrative Analysis of Proteomics Data to Obtain Clinically Relevant Markers.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2018
The analysis of proteomics data can be significantly challenging. Beyond the technical challenges of accurately identifying and quantifying peptides, identifying the most biologically coherent set of biomarkers can be a particularly daunting step. In...
Machine learning in computational biology to accelerate high-throughput protein expression.
Bioinformatics (Oxford, England)
Aug 15, 2017
MOTIVATION: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughp...
AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.
Bioinformatics (Oxford, England)
Sep 1, 2016
MOTIVATION: Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it ...
[Effects of extractionfrom raspberry on hippocampus proteomics of mice suffered from ovariectomized-induced AD].
Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
Aug 1, 2016
This paper was aimed to investigate the impact of the extraction from raspberry on the Alzheimer disease model protein expression. According to weight, the ovariectomized mice were randomly divided into shame operation group, model group, estrogen po...
Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis.
Cell
Jun 4, 2015
Proteomics has proved invaluable in generating large-scale quantitative data; however, the development of systems approaches for examining the proteome in vivo has lagged behind. To evaluate protein abundance and localization on a proteome scale, we ...
Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 1, 2015
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel...
Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 1, 2015
Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to...
A label distance maximum-based classifier for multi-label learning.
Bio-medical materials and engineering
Jan 1, 2015
Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label ...
An effective fuzzy kernel clustering analysis approach for gene expression data.
Bio-medical materials and engineering
Jan 1, 2015
Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approac...
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