The TREC 2005 Genomics Track featured two tasks, an ad hoc retrieval task and four subtasks in text categorization. The ad hoc retrieval task utilized a 10-year, 4.5-million document subset of the MEDLINE bibliographic database, with 50 topics conforming to five generic topic types. The categorization task used a full-text document collection with training and test sets consisting of about 6,000 biomedical journal articles each. Participants aimed to triage the documents into categories representing data resources in the Mouse Genome Informatics database, with performance assessed via a utility measure.

/pdf/trec-2005-genomics-track-overview-4a2ycgutnk.pdf

TREC 2005 Genomics Track Overview.

The TREC 2004 Genomics Track consisted of two tasks. The first task was a standard ad hoc retrieval task using topics obtained from real biomedical research scientists and documents from a large subset of the MEDLINE bibliographic database. The second task focused on categorization of full-text documents, simulating the task of curators of the Mouse Genome Informatics (MGI) system and consisting of three subtasks. One subtask focused on the triage of articles likely to have experimental evidence warranting the assignment of GO terms, while the other two subtasks focused on the assignment of the three top-level GO categories. The track had 33 participating groups.

/pdf/trec-2004-genomics-track-overview-3b54ymqnze.pdf

TREC 2004 Genomics Track Overview

A GIS (Geographic Information System) is a computer system designed to collect, store, retrieve, manipulate, and display spatial data. This technology combines aspects of hypertext/hypermedia and database management software in a unique useful form. An overview of GIS technology, its history, the software market, and general issues is proposed.

Maps for the rest of us: affordable and fun

Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD'02 text mining contest—where figure-captions proved to be an invaluable feature for identifying documents of interest—images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them—both alone and in combination with text—to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods. Contact: shatkay@cs.queensu.ca

/pdf/integrating-image-data-into-biomedical-text-categorization-tgwwm1qsjt.pdf

Integrating image data into biomedical text categorization

The annotation of web sites in social bookmarking systems has become a popular way to manage and find information on the web. The community structure of such systems attracts spammers: recent post pages, popular pages or specific tag pages can be manipulated easily. As a result, searching or tracking recent posts does not deliver quality results annotated in the community, but rather unsolicited, often commercial, web sites. To retain the benefits of sharing one's web content, spam-fighting mechanisms that can face the flexible strategies of spammers need to be developed.A classical approach in machine learning is to determine relevant features that describe the system's users, train different classifiers with the selected features and choose the one with the most promising evaluation results. In this paper we will transfer this approach to a social bookmarking setting to identify spammers. We will present features considering the topological, semantic and profile-based information which people make public when using the system. The dataset used is a snapshot of the social bookmarking system BibSonomy and was built over the course of several months when cleaning the system from spam. Based on our features, we will learn a large set of different classification models and compare their performance. Our results represent the groundwork for a first application in BibSonomy and for the building of more elaborate spam detection mechanisms.

The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems.

A shortest-path algorithm finds a path containing the minimal cost between two vertices in a graph. A plethora of shortest-path algorithms is studied in the literature that span across multiple disciplines. This paper presents a survey of shortest-path algorithms based on a taxonomy that is introduced in the paper. One dimension of this taxonomy is the various flavors of the shortest-path problem. There is no one general algorithm that is capable of solving all variants of the shortest-path problem due to the space and time complexities associated with each algorithm. Other important dimensions of the taxonomy include whether the shortest-path algorithm operates over a static or a dynamic graph, whether the shortest-path algorithm produces exact or approximate answers, and whether the objective of the shortest-path algorithm is to achieve time-dependence or is to only be goal directed. This survey studies and classifies shortest-path algorithms according to the proposed taxonomy. The survey also presents the challenges and proposed solutions associated with each category in the taxonomy.

A Survey of Shortest-Path Algorithms.

The widespread use of location-aware devices together with the increased popularity of micro-blogging applications (e.g., Twitter) led to the creation of large streams of spatio-textual data. In order to serve real-time applications, the processing of these large-scale spatio-textual streams needs to be distributed. However, existing distributed stream processing systems (e.g., Spark and Storm) are not optimized for spatial/textual content. In this demonstration, we introduce Tornado, a distributed in-memory spatio-textual stream processing server that extends Storm. To efficiently process spatio-textual streams, Tornado introduces a spatio-textual indexing layer to the architecture of Storm. The indexing layer is adaptive, i.e., dynamically re-distributes the processing across the system according to changes in the data distribution and/or query workload. In addition to keywords, higher-level textual concepts are identified and are semantically matched against spatio-textual queries. Tornado provides data deduplication and fusion to eliminate redundant textual data. We demonstrate a prototype of Tornado running against real Twitter streams, where the users can register continuous or snapshot spatio-textual queries using a map-assisted query-interface.

/pdf/tornado-a-distributed-spatio-textual-stream-processing-4rteipe9gf.pdf

Tornado: a distributed spatio-textual stream processing system

We propose a Named Entities transliteration mining system using Finite State Automata (FSA). We compare the proposed approach with a baseline system that utilizes the Editex technique to measure the length-normalized phonetic based edit distance between the two words. We submitted three standard runs in NEWS2010 shared task and ranked first for English to Arabic (WM-EnAr) and obtained an F-measure of 0.915, 0.903, and 0.874 respectively.

Language Independent Transliteration Mining System Using Finite State Automata Framework

The vast number of published medical documents is considered a vital source for relationship discovery. This paper presents a statistical unsupervised system, called BioNoculars, for extracting protein-protein interactions from biomedical text. BioNoculars uses graph-based mutual reinforcement to make use of redundancy in data to construct extraction patterns in a domain independent fashion. The system was tested using MEDLINE abstract for which the protein-protein interactions that they contain are listed in the database of interacting proteins and protein-protein interactions (DIPPPI). The system reports an F-Measure of 0.55 on test MEDLINE abstracts.

https://www.aclweb.org/anthology/W07-1012.pdf

BioNoculars: Extracting Protein-Protein Interactions from Biomedical Text

Collaborative software is gaining pace as a vital means of information sharing between users. This paper discusses one of the key challenges that affect such systems which is identifying spammers. We discuss potential features that describe the system’s users and illustrate how we can use those features in order to determine potential spamming users through various machine learning models.

Amgad Madkour

Papers

A Survey of Shortest-Path Algorithms.

Tornado: a distributed spatio-textual stream processing system

Language Independent Transliteration Mining System Using Finite State Automata Framework

BioNoculars: Extracting Protein-Protein Interactions from Biomedical Text

Using Semantic Features to Detect Spamming in Social Bookmarking Systems