An application of Hoffman graphs for spectral characterizations of graphs

In this paper, we present the first application of Hoffman graphs for spectral characterizations of graphs. In particular, we show that the $2$-clique extension of the $(t+1)\times(t+1)$-grid is determined by its spectrum when $t$ is large enough. This result will help to show that the Grassmann graph $J_2(2D,D)$ is determined by its intersection numbers as a distance regular graph, if $D$ is large enough

Predicting protein disorder by analyzing amino acid sequence

<p>Abstract</p> <p>Background</p> <p>Many protein regions and some entire proteins have no definite tertiary structure, presenting instead as dynamic, disorder ensembles under different physiochemical circumstances. These proteins and regions are known as Intrinsically Unstructured Proteins (IUP). IUP have been associated with a wide range of protein functions, along with roles in diseases characterized by protein misfolding and aggregation.</p> <p>Results</p> <p>Identifying IUP is important task in structural and functional genomics. We exact useful features from sequences and develop machine learning algorithms for the above task. We compare our IUP predictor with PONDRs (mainly neural-network-based predictors), disEMBL (also based on neural networks) and Globplot (based on disorder propensity).</p> <p>Conclusion</p> <p>We find that augmenting features derived from physiochemical properties of amino acids (such as hydrophobicity, complexity etc.) and using ensemble method proved beneficial. The IUP predictor is a viable alternative software tool for identifying IUP protein regions and proteins.</p