Enhancing access to the Bibliome: the TREC 2004 Genomics Track

BACKGROUND: The goal of the TREC Genomics Track is to improve information retrieval in the area of genomics by creating test collections that will allow researchers to improve and better understand failures of their systems. The 2004 track included an ad hoc retrieval task, simulating use of a search engine to obtain documents about biomedical topics. This paper describes the Genomics Track of the Text Retrieval Conference (TREC) 2004, a forum for evaluation of IR research systems, where retrieval in the genomics domain has recently begun to be assessed. RESULTS: A total of 27 research groups submitted 47 different runs. The most effective runs, as measured by the primary evaluation measure of mean average precision (MAP), used a combination of domain-specific and general techniques. The best MAP obtained by any run was 0.4075. Techniques that expanded queries with gene name lists as well as words from related articles had the best efficacy. However, many runs performed more poorly than a simple baseline run, indicating that careful selection of system features is essential. CONCLUSION: Various approaches to ad hoc retrieval provide a diversity of efficacy. The TREC Genomics Track and its test collection resources provide tools that allow improvement in information retrieval systems

The MERG Suite: Tools for discovering competencies and associated learning resources

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TREC Genomics Track Overview

The first year of TREC Genomics Track featured two tasks: ad hoc retrieval and information extraction. Both tasks centered around the Gene Reference into Function (GeneRIF) resource of the National Library of Medicine, which was used as both pseudorelevance judgments for ad hoc document retrieval as well as target text for information extraction. The track attracted 29 groups who participated in one or both tasks. The growing amount of scientific discovery in genomics and related biomedical disciplines has led to a corresponding growth in the amount of on-line data and information. A growing challenge for biomedical researchers is how to access and manage this ever-increasing quantity of information. This situation presents opportunities and challenges for the information retrieval (IR) field. IR has historically focused on document retrieval, but the field has expanded in recent years with the growth of new information needs (e.g., question-answering, cross-lingual), data types (e.g., video) and platforms (e.g., the Web). This paper describes the events leading up to the first year of TREC Genomics Track, the first year’s results, and future directions for subsequent years. Genomics and Information Resources The field of genomics is concerned with the genome, which is usually defined as the genetic material of living organisms. Its research focuses on the central dogma of biology: deoxyribonucleic acid (DNA) is transcribed into ribonucleic acid (RNA), which serves to translate the nucleotide sequences of DNA into proteins. The latter are responsible for functions in living organisms and the collection of all proteins in is increasingly called the proteome. With the advent of new technologies for sequencing the genome and proteome, along with other tools for identifying the expression o

Phrases

boosting, and query expansion using external knowledge resources for genomic information retrieval In our TREC Genomics Track work, we focused on domain-specific techniques in attempting to improve retrieval performance beyond a word searching baseline. One set of experiments looked at using phrases based on gene name synonyms with boosting of the canonical name of the gene. Another set assessed query expansion using external knowledge resources. Query expansion has been a staple of the TREC ad hoc task dating back almost to the inception of TREC, showing consistent benefit when added to a wide variety of baseline techniques, e.g., [1, 2]. In the biomedical domain, however, results have been mixed. While Srinivasan obtained improved retrieval using retrieval feedback (automatic relevance feedback) in a small test collection [3], Hersh et. al. did not find improved retrieval when queries were expanded using thesaurus relationships in the Unified Medical Language System (UMLS) Metathesaurus [4]. Query expansion may be feasible in the genomics domain due to the considerable effort being devoted to creating useful cross-linkages across data sources. The most prominent example is the collection of databases maintained by the National Center fo

Abstract Enhancing Access to the Bibliome: The TREC Genomics Track

The growing amount of scientific discovery in genomics and related biomedical disciplines has led to a corresponding increase in the amount of on-line data and information. A new challenge for biomedical researchers has been how to access and manage this ever-increasing quantity of information. The Text Retrieval Conference (TREC) has implemented a Genomics Track to create an experimental environment for research in the use of information retrieval systems in the genomics domain. In the first year of the track, an ad hoc document retrieval task and an information extraction task were carried out by 29 research groups. Future work will focus on more complex data sources, searching tasks, and types of experiments. Keywords: Information retrieval, Text Retrieval, genomics, bioinformatics

Examining the COVID-19 case growth rate due to visitor vs. local mobility in the United States using machine learning

Abstract Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread in the United States between March 1, 2020, and December 31, 2020. Two metrics, namely local and visitor transmission risk, were extracted from mobility data to capture the transmission potential of COVID-19 through mobility. A combination of the three factors: the current number of cases, local transmission risk, and the visitor transmission risk, are used to model the future number of cases using various machine learning models. The factors that contribute to better forecast performance are the ones that impact the number of cases. The statistical significance of the forecasts is also evaluated using the Diebold–Mariano test. Finally, the performance of models is compared for three waves across all 50 states. The results show that visitor mobility significantly impacts the case growth by improving the prediction accuracy by 33.78%. We also observe that the impact of visitor mobility is more pronounced during the first peak, i.e., March–June 2020

TREC 2005 genomics track overview

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. 1