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.

[EX Q]: 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.
[EX A]: AMS 3.0: prediction of post-translational modifications

[EX Q]: The response to the novel coronavirus outbreak in China suggests that many of the lessons from the 2003 SARS epidemic have been implemented and the response improved as a consequence. Nevertheless some questions remain and not all lessons have been successful. The national and international response demonstrates the complex link between public health, science and politics when an outbreak threatens to impact on global economies and reputations. The unprecedented measures implemented in China are a bold attempt to control the outbreakwe need to understand their effectiveness to balance costs and benefits for similar events in the future.
[EX A]: Epidemiology and Infection SARS to novel coronavirus -old lessons and new lessons

[EX Q]: One of the daunting challenges facing modern medicine lies in the understanding and treatment of tumor heterogeneity. Most tumors show intra-tumor heterogeneity at both genomic and proteomic levels, with marked impacts on the responses of therapeutic targets. Therapeutic target-related gene expression pathways are affected by hypoxia and cellular stress. However, the finding that targets such as eukaryotic initiation factor (eIF) 4E (and its phosphorylated form, p-eIF4E) are generally homogenously expressed throughout tumors, regardless of the presence of hypoxia or other cellular stress conditions, opens the exciting possibility that malignancies could be treated with therapies that combine targeting of eIF4E phosphorylation with immune checkpoint inhibitors or chemotherapy.
[EX A]:
Beyond molecular tumor heterogeneity: protein synthesis takes control