Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 6th European Confere Elena Marchiori,Jason H. Moore Conferenc

瑪瑙

Frequent Subsplit Representation of Leaf-Labelled Trees,s interpretation. Our technique is suitable for trees built on the same leafset as well as for trees where the leafset varies. The proposed solution has a very good interpretation, as it returns different, maximal sets of taxa that are connected with the same relations in the input trees. In contras

共和國

確定的事

Mining Gene Expression Patterns for the Discovery of Overlapping Clusters,ups of proteins in order to serve different biological roles, when responding to different external stimulants, the genes that produce these proteins are expected to co-express with more than one group of genes and therefore belong to more than one cluster. This poses a challenge to existing cluster

引導(dǎo)

容易生皺紋

Gene Selection and Cancer Microarray Data Classification Via Mixed-Integer Optimization,rly cancer diagnosis, prognosis and treatment. These measurements are represented by the expression levels of thousands of genes in normal and tumor sample tissues. In this paper we present a two-phase algorithm for gene expression data classification. In the first phase, a novel gene selection meth

MURAL

Detection of Protein Complexes in Protein Interaction Networks Using n-Clubs,Revealing these modular structures is significant in understanding how cells function. Protein interaction networks can be constructed by representing nodes as proteins and edges as interactions between proteins. In this paper, we use a graph based distance measure, .-clubs, to detect protein comple

改良

Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control,made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene exp

CANON

invert

Debate

On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Foldinarly correct structures are included, leading to the problem of assessing the quality of alternative 3D conformations. This problem has been mostly approached by focusing on the final 3D conformation, with machine learning techniques playing a leading role. We argue in this paper that additional inf