Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. Here, a spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi-cliques and quasi-bipartites) of complicated protein-protein interaction networks. Our analyses suggest that these hidden topological structures consist of biologically relevant functional groups. This result motivates a new method to predict the function of uncharacterized proteins based on the classification of known proteins within topological structures. Using this spectral analysis method, 48 quasi-cliques and six quasi-bipartites were isolated from a network involving 11,855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were assigned functions.

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein-protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein-Protein Interactions prediction), which employs deep neural networks to learn effectively the representations of proteins from common protein descriptors. The experimental results indicate that DeepPPI achieves superior performance on the test data set with an Accuracy of 92.50%, Precision of 94.38%, Recall of 90.56%, Specificity of 94.49%, Matthews Correlation Coefficient of 85.08% and Area Under the Curve of 97.43%, respectively. Extensive experiments show that DeepPPI can learn useful features of proteins pairs by a layer-wise abstraction, and thus achieves better prediction performance than existing methods. The source code of our approach can be available via http://ailab.ahu.edu.cn:8087/DeepPPI/index.html .

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Recent evidence points to considerable transcription occurring in non-protein-coding regions of eukaryote genomes. However, their lack of conservation and demonstrated function have created controversy over whether these transcripts are functional. Applying a novel cloning strategy, we have cloned 100 novel and 61 known or predicted Caenorhabditis elegans full-length ncRNAs. Studying the genomic environment and transcriptional characteristics have shown that two-thirds of all ncRNAs, including many intronic snoRNAs, are independently transcribed under the control of ncRNA-specific upstream promoter elements. Furthermore, the transcription levels of at least 60% of the ncRNAs vary with developmental stages. We identified two new classes of ncRNAs, stem-bulge RNAs (sbRNAs) and snRNA-like RNAs (snlRNAs), both featuring distinct internal motifs, secondary structures, upstream elements, and high and developmentally variable expression. Most of the novel ncRNAs are conserved in Caenorhabditis briggsae, but only one homolog was found outside the nematodes. Preliminary estimates indicate that the C. elegans transcriptome contains ∼2700 small non-coding RNAs, potentially acting as regulatory elements in nematode development.

This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information about the FITS headers of spectra are also introduced. The released data set includes 319 000 spectra and a catalog of these objects.