Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

https://www.zora.uzh.ch/id/eprint/165500/1/30476243.pdf

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

/pdf/string-v10-protein-protein-interaction-networks-integrated-2gg77rxec0.pdf

STRING v10: protein–protein interaction networks, integrated over the tree of life

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

/pdf/the-string-database-in-2017-quality-controlled-protein-1u6rx6raii.pdf

The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.

We discuss modelling issues related to the design of a somatic facial motor space. The variants proposed are conceived to be part of a larger system for dealing with simulation-based face emotion analysis along dual interactions.

A Note on Modelling a Somatic Motor Space for Affective Facial Expressions

We present a non-Gaussian local limit theorem for the number of occurrences of a given symbol in a word of length n generated at random. The stochastic model for the random generation is defined by a rational formal series with non-negative real coefficients. The result yields a local limit towards a uniform density function and holds under the assumption that the formal series defining the model is recognized by a weighted finite state automaton with two primitive components having equal dominant eigenvalue.

/pdf/a-local-limit-property-for-pattern-statistics-in-bicomponent-51bg830dmu.pdf

A Local Limit Property for Pattern Statistics in Bicomponent Stochastic Models

In parallel computation domain, graph coloring is widely studied in its own and represents a reference problem for scheduling of parallel tasks. Unfortunately, common graph coloring strategies usually focus on minimizing the number of colors without any concern for the sizes of each color class, thus producing highly skewed color class distributions. However, to guarantee efficiency in parallel computations, but also in other application contexts, it is important to keep the color classes highly balanced in their sizes. In this paper we address this challenging issue for large scale graphs, proposing a fast parallel MCMC heuristic for sparse graphs that randomly generates good balanced colorings provided that a sufficient number of colors are made available. We show its effectiveness through some numerical simulations on random graphs.

/pdf/a-parallel-mcmc-algorithm-for-the-balanced-graph-coloring-41z2pjvco9.pdf

A parallel MCMC algorithm for the Balanced Graph Coloring problem

We study the computational complexity of the problem of computing an optimal clustering \(\{A_1,A_2,...,A_k\}\) of a set of points assuming that every cluster size \(|A_i|\) belongs to a given set M of positive integers. We present a polynomial time algorithm for solving the problem in dimension 1, i.e. when the points are simply rational values, for an arbitrary set M of size constraints, which extends to the \(\ell _1\)-norm an analogous procedure known for the \(\ell _2\)-norm. Moreover, we prove that in the Euclidean plane, i.e. assuming dimension 2 and \(\ell _2\)-norm, the problem is NP-hard even with size constraints set reduced to \(M=\{2,3\}\).

/pdf/on-the-complexity-of-clustering-with-relaxed-size-xatosyjbcn.pdf

On the Complexity of Clustering with Relaxed Size Constraints

In a recent work we have determined the local limit distribution of pattern statistics representing the number of occurrences of a symbol in words of length n in a regular language generated at random according to a suitable stochastic model. Such a model is defined by a finite automaton with weights in R+, consisting of two primitive components, having some transition from the first to the second component. In the present work we extend those results to the case when there is no communication among the components, and hence the associated formal series is the sum of two rational series recognized by finite state automata with primitive transition matrix. We obtain local limit laws of Gaussian type when there is a dominant component or when, in equipotent case, the main terms of mean value and variance are equal. On the contrary, if these terms are not the same then the local limit distribution is a convex combination of Gaussian laws. All convergence rates of our limits are of the order O(n−1/2). This completes the analysis of local limit laws of symbol statistics under a bicomponent stochastic model.

/pdf/saddle-point-method-in-the-analysis-of-pattern-statistics-42s1w2roww.pdf

Jianyi Lin

Papers

A Note on Modelling a Somatic Motor Space for Affective Facial Expressions

A Local Limit Property for Pattern Statistics in Bicomponent Stochastic Models

A parallel MCMC algorithm for the Balanced Graph Coloring problem

On the Complexity of Clustering with Relaxed Size Constraints

Saddle point method in the analysis of pattern statistics for regular languages