instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
question:
Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at Additional file 1 Supplementary materials for the publication. Supplementary.xls contains detailed results of training and testing of AMS 3.0 tool in MS Excel file format. Additional file 2 PTM predictor software with training data. PTM_Archive.tar.gz includes the predictor software binaries together with all trained ANNs in tar.gz compressed form.
answer:
AMS 3.0: prediction of post-translational modifications

question:
We determined the interferon- (IFN-) cDNA sequence from three porcine breeds, Duroc, Landrance/Duroc hybrid, and Landrance breeds. Five single nucleotide polymorphisms (SNPs) of porcine IFN- (PoIFN-) were identified, respectively, at positions 269 (A/G), 376 (C/T), 426 (T/C), and 465 (T/C) of the coding sequence in Landrance/Duroc hybrid, and at position 251 (A/G) in Landrance breed. Among them, A269G and A251G polymorphisms resulted in Q67R and K61R replacements in the mature protein. PoIFN- cDNAs of Duroc breed (PoIFN--W) and Landrance/Duroc hybrid (PoIFN--M), which, respectively, encoded Q67 and R67, were introduced into a prokaryotic expression vector pET32 to express recombinant PoIFN--W (rPoIFN--W) and rPoIFN--M protein variants in Escherichia coli. The identity of both protein variants was further confirmed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). We then compared bioactivities of these two recombinant proteins. Although both recombinant protein variants exhibited comparable activities in antiproliferation of PK-15 cells and in nitric oxide (NO) induction of porcine peripheral monocytes, antiviral activity of rPoIFN--W protein was significantly higher (P < 0.001) than that of rPoIFN--M protein in a plaque inhibition assay using pseudorabies virus (PRV). IC50 values of rPoIFN--W and rPoIFN--M protein in anti-PRV assay were determined as 5.3 1.3 and 9.3 4.3 nM, respectively. In conclusion, we have identified five novel SNPs in PoIFN- cDNA, including two missense polymorphisms that result in Q67R and K61R replacements. Our results further demonstrate that Q67R can markedly reduce antiviral activity of the PoIFN- protein. This is the first report that shows the functional SNP in the coding region of IFN-. In the future, it is imperative to determine whether Q67R replacement in IFN- may have disease association.
answer:
A missense polymorphism in porcine interferon- cDNA affects antiviral activity of the protein variant

question:
The purpose of disease mapping is to find spatial clustering and identify risk areas and potential epidemic initiators. Rather than relying on plotting either the case number or incidence rate, this chapter proposes three temporal risk indices: the probability of case occurrence (how often did uneven cases occur), the duration of an epidemic (how long did cases persist), and the intensity of a transmission (were the case of chronological significance). By integrating the three indicators using the local indicator of spatial autocorrelation (LISA) statistic, this chapter intends to develop a novel approach for evaluating spatial-temporal relationships with different risk patterns in the 2002 dengue epidemic, the worst outbreak in the past sixty years. With this approach, not only are hypotheses generated through the mapping processes in furthering investigation, but also procedures provided to identify spatial health risk levels with temporal characteristics.
answer:
A Spatial-Temporal Approach to Differentiate Epidemic Risk Patterns A Spatial-Temporal Approach to Differentiate Epidemic Risk Patterns