In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

[EX Q]: Paragraph: Annotation The process of adding descriptive information to a data set and the individual data elements that comprise that data set. May include both computational analyses and manual curation. Curation The process of manually adding descriptive information or explanatory material to a data set usually based on a review of the existing literature. Database A collection of information, usually stored electronically on a computer. Database, hierarchical (XML) Extensible markup language. A formally defined hierarchical organization of data. A database software package designed to operate on a particular computer platform and operating system. Database schema A map of the database structure, including all of the fields, tables, and relationships that comprise the framework for storage of information within that database. Database, relational A database structure that provides the ability to define formal relationships between all data elements stored within the database. Ontology A controlled vocabulary used to formally describe (for our purposes) a biological process or entity. SQL Structured Query Language. A computer language used to query relational databases for information. 
 Title: Virus Databases
[EX A]: False

[EX Q]: Paragraph: We recently observed six cases of generalized dermatitis associated with Malassezia overgrowth in cats presented to the Veterinary College of Alfort, France. Elevated numbers of yeasts were observed in lesional skin by cytology and culture. Skin lesions occurred on the face, ventral neck, abdomen and ear canals and were characterized by some degree of alopecia, erythema and crusting. In most cases, pruritus was intense. The species M. pachydermatis was systematically isolated. 
 Title: Generalized dermatitis associated with Malassezia overgrowth in cats: A report of six cases in France
[EX A]: True

[EX Q]: Paragraph: Background 3 In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics 4 algorithms. Sequenced datasets include background organisms, creating ambiguity in the true 5 source organism for each read. Bacterial and viral datasets were created with even and staggered 6 coverage to evaluate organism identification, read mapping, and gene identification capabilities of 7 available algorithms. These truth datasets are provided as a resource for the development and 8 refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform 9 decision makers on strengths and weaknesses of available algorithms and how the results may be best 10 leveraged for bacterial and viral organism identification and characterization. 11 12 Source organisms were selected to mirror communities described in the Human Microbiome Project as 13 well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The 14 six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: 15 MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. 16 Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species 19 levels, false positive organisms identified to genus and species level, read mapping, relative abundance 20 estimation, and gene calling. No algorithm out performed the others in all categories, and the 21 algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria 22 and LMAT for viruses. The algorithms were ranked by overall performance using a normalized 23 weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken 24 and LMAT. 25 Conclusions 27 Simulated FASTQ datasets with well--characterized truth data about microbial community composition 28 reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms 29 evaluated. The simulated datasets are available to download from the Sequence Read Archive 30 (SRP062063). 31 32 
 Title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim
[EX A]:
False