TASK DEFINITION: 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.
PROBLEM: 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.

SOLUTION: AMS 3.0: prediction of post-translational modifications

PROBLEM: Camels have cultural value in the Arab society and are considered one of the most important animals in the Arabian Peninsula and arid environments, due to the distinct characteristics of their meat and milk. Moreover, there is a great interest in camel racing and beauty shows. Therefore, treatment of elite animals, increasing the number of camels as well as genetic improvement is an essential demand. Because there are unique camels for milk production, meat, or in racing, the need to propagate genetically superior camels is urgent. Recent biotechnological approaches such as stem cells hold great promise for biomedical research, genetic engineering, and as a model for studying early mammalian developmental biology. Establishment of stem cells lines from camels would tremendously facilitate regenerative medicine for genetically superior camels, permit the gene targeting of the camel genome and the generation of genetically modified animal and be a mean for genome conservation for the elite breeds. In this mini-review, we show the current research, future horizons and potential applications for camel stem cells.

SOLUTION: The current perspectives of dromedary camel stem cells research

PROBLEM: Infectious diseases that are caused by pathogenic microorganisms can spread fast and far, from one person to another, directly or indirectly. Prompt quarantining of the infected from the rest, coupled with contact tracing, has been an effective measure to encounter outbreaks. However, urban life and international travel make containment difficult. Furthermore, the length of incubation periods of some contagious diseases like SARS enable infected passengers to elude health screenings before first symptoms appear and thus to carry the disease further. Detecting and visualizing contact-tracing networks, and immediately identifying the routes of infection, are thus important. We apply information visualization and hidden cluster detection for finding cliques of potentially infected people during incubation. Preemptive control and early quarantine are hence possible by our method. Our prototype Infectious Disease Detection and Quarantine Management System (IDDQMS), which can identify and trace clusters of infection by mining patients' history, is introduced in this paper.

SOLUTION:
Hidden Cluster Detection for Infectious Disease Control and Quarantine Management