TASK DEFINITION: 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".
PROBLEM: Paragraph: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to outperform several prominent methods in identifying strongly binding peptides. As a method with demonstrated performance in the quantitative modeling of MHCpeptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.
Title: Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

SOLUTION: False

PROBLEM: Paragraph: One of the main tasks in biological sequence analysis is biological sequence comparison. Numerous efficient methods have been developed for sequence comparison. Traditional sequence comparison is based on sequence alignment. In this report, we propose a novel alignment-free method based on the relative Lempel-Ziv complexity to compare biological sequences. The vertebrate transferring genomes and the spike protein sequences are prepared and tested to evaluate the validity of the method. We use this method to build phylogenetic tree of two groups of the sequences. The result demonstrates that our method is powerful and efficient.
Title: A relative Lempel-Ziv complexity: Application to comparing biological sequences

SOLUTION: True

PROBLEM: Paragraph: Production of large quantities of viral vectors is crucial for the success of gene therapy in the clinic. There is a need for higher titers of herpes simplex virus-1 (HSV-1) vectors both for therapeutic use as well as in the manufacturing of clinical grade adenoassociated virus (AAV) vectors. HSV-1 yield increased when primary human fibroblasts were treated with anti-inflammatory drugs like dexamethasone or valproic acid. In our search for compounds that would increase HSV-1 yield, we investigated another anti-inflammatory compound, aurintricarboxylic acid (ATA). Although ATA has been previously shown to have antiviral effects, we find that low (micromolar) concentrations of ATA increased HSV-1 vector production yields. Our results showing the use of ATA to increase HSV-1 titers have important implications for the production of certain HSV-1 vectors as well as recombinant AAV vectors.
Title: Aurintricarboxylic acid increases yield of HSV-1 vectors

SOLUTION:
False