Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and th...
BACKGROUND: Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to eval...
OBJECTIVE: The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation.
Database : the journal of biological databases and curation
Dec 26, 2016
We previously reported a paradigm for large-scale phylogenomic analysis of gene families that takes advantage of the large corpus of experimentally supported Gene Ontology (GO) annotations. This 'GO Phylogenetic Annotation' approach integrates GO ann...
BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only o...
Journal of integrative bioinformatics
Dec 22, 2016
Microbes are highly diverse and widely distributed organisms. They account for ~60% of Earth’s biomass and new predictions point for the existence of 1011 to 1012 species, which are constantly sharing genes through several different mechanisms....
The control system designing of Unmanned Wave Glider (UWG) is challenging since the control system is weak maneuvering, large time-lag and large disturbance, which is difficult to establish accurate mathematical model. Meanwhile, to complete marine e...
Gene expression profiling followed by gene ontology (GO) term enrichment analysis can generate long lists of significant GO terms. To interpret these results and get biological insight in the data, filtering and rearranging these long lists of GO ter...
BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by s...
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality a...
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