Large-scale mapping of human protein–protein interactions by mass spectrometry

Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations

First-Order Theories of Approximate Space (Extended Abstract)

In this paper we present a language for expressing "approximate" spatial relations on points and regions in space. Space is assumed to be two dimensional, orthogonal and discrete. Initially, we present a firstorder theory of one-dimensional approximate space. We axiomatize the theory of space in terms of points and the haze and precedence relations. We then study the models of this theory and we show that in the one dimensional case the theory has a unique model up to isomorphism. This result allows us to affirm that our theory is complete and decidable. Secondly, we propose a conservative two-dimensional extension of the theory which retains its completeness property. In the two-dimensional case, we define a complete set of topological and directional relations that are useful for practical reasoning about space. The derived relations are related to existent formalisms for spatial relations such as Egenhofer's 3-intersection model, for which they provide an alternative semantical ac..

Storage Management for Knowledge Bases

Secondary memory storage plays a major role in making large knowledge bases usable. This paper presents such a storage architecture for knowledge bases. In particular, the Controlled Decomposition Model, a flexible storage model which takes into account the numerous and expressive features of the knowledge representation model is defined. Second, the indexing problem for the knowledge base context is examined and the Temporal Join Index is proposed. An implementation technique for temporal indices, based on a spatial access method, is presented. Finally, an analytical and experimental performance study is conducted to account the performance limits of the proposed methods. 1 Introduction The performance of knowledge base systems slows down significantly as the size of the employed knowledge base increases, if the knowledge base is stored in primary memory. Performance deterioration is even worse when the size of the knowledge base grows much beyond the size of the available main memo..

Representation and Management Issues for Large Spatial Knowledge Bases

this report is organized as follows. Section 2 presents ontologies for space which are used in AI. Section 3 reviews representations of spatial configurations, solids and regions. Section 4 presents various spatial reasoning methods. Section 5 is a thorough review of work done in spatial databases including data modeling, query processing and data organization issues. Section 6 concludes my study with a summary of open research issues in the examined areas. 2 Ontologies of Spac

Efficient Algorithms for Qualitative Reasoning about Imprecise Space

. This paper addresses the problem of qualitative spatial reasoning and presents efficient algorithms to deal it. We assume a representation which views space as a totality of objects surrounded by a haze area and connected in terms of qualitative spatial relations. Statements relating objects in this represesentation are expressed in terms of haze-order constraints. Reasoning about haze-orders involves, first, determining the consistency of a set of haze-order constraints, and, second, deducing new relations from those that are already known. The developed reasoning algorithms make use of a data structure called haze-order graph which trades space for efficiency. Experimental results illustrate the efficiency of the algorithms. 1 Introduction Reasoning about the spatial relations between physical objects or regions of space is of fundamental importance. In many cases this can be done without precise quantitative information about these relations. In fact, some knowledge of the topol..

Qualitative Reasoning about Imprecise Spatial Information

This paper investigates the problem of reasoning about imprecise spatial information. As a representation scheme we assume haze-points that are points surrounded by an area, the haze, denoting the imprecision that is associated with them. Haze-points are related by haze and precedence relations. Using these notions we can build higher dimension objects and relations [Top94a]. This paper focuses on reasoning about spatial imprecision which is formalized as a constraint satisfaction problem over networks of constraints expressed in the language of haze-orders. The main contributions of this paper are (a) a set of preprocessing transformations that decrease the ambiguity which is introduced from unconditional transitivity over the haze relation, and (b) a graph-based data structure which is suitable for efficient inferencing of order relations. 1 Introduction It is generally accepted that neither human perception nor measurement instruments are precise in the information they provide. Th..

Representing Partial Spatial Information in Databases

. In this paper we present a spatial data model which facilitates the representation of and reasoning with various forms of qualitatively and quantitatively incomplete spatial information. The model is founded on a combination of object-oriented and constraint-based data modeling facilities and provides for representations of variable precision and granularity. We identify four basic reasoning tasks required for query processing operations and outline algorithms for each task. Finally, we discuss extensions of the model and outline an implementation based on the Telos knowledge base management system extended with an appropriate constraint reasoning component. 1 Introduction Storing and manipulating spatial information is important for many database applications, including geographic information systems [MP94], vehicle navigation [Ege93], image retrieval [SYH94], protein structure prediction, environmental studies and many others. Existing spatial data models can generally be classif..

Query Processing for Knowledge Bases Using Join Indices

This paper addresses the problem of physical query processing for large object-oriented, temporal knowledge bases. The major tasks being investigated are how to generate the space of all possible execution plans for a given knowledge base query and how to traverse this space in order to choose an efficient execution plan. The results of this work include: (a) the formulation of a set of access level operations which depend on the underlying storage model and the development of a cost model for estimating their cost; (b) the exploration of various optimization heuristics for selecting efficient execution plans for temporal path queries which make use of the join index relations that are provided by the storage model; and (c) a performance study that shows the benefits of join index based query processing techniques for knowledge bases compared to the traditional tuple-oriented (characteristic of the AI-DB coupling systems) and bulk join query processing approaches in database systems. ..