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.
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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: Aptamers are short, single-stranded DNA, RNA, or synthetic XNA molecules that can be developed with high affinity and specificity to interact with any desired targets. They have been widely used in facilitating discoveries in basic research, ensuring food safety and monitoring the environment. Furthermore, aptamers play promising roles as clinical diagnostics and therapeutic agents. This review provides update on the recent advances in this rapidly progressing field of research with particular emphasis on generation of aptamers and their applications in biosensing, biotechnology and medicine. The limitations and future directions of aptamers in target specific delivery and real-time detection are also discussed.

Answer: molecules Recent Advances in Aptamer Discovery and Applications

Question: Since the identification and imprisonment of "Typhoid Mary," a woman who infected at least 47 people with typhoid in the early 1900s, epidemiologists have recognized that 'superspreading' hosts play a key role in disease epidemics. Such variability in transmission also exists among species within a community (amplification hosts) and among habitat patches across a landscape (disease 'hotspots'), underscoring the need for an integrative framework for studying transmission heterogeneity. Here, we synthesize literature on human, plant, and animal diseases to evaluate the relative contributions of host, pathogen, and environmental factors in driving transmission heterogeneity across hosts and space. We show that host and spatial heterogeneity are closely linked and that quantitatively assessing the contribution of infectious individuals, species, or environmental patches to overall transmission can aid management strategies. We conclude by posing hypotheses regarding how pathogen natural history influences transmission heterogeneity and highlight emerging frontiers in the study of transmission heterogeneity.

Answer:
From superspreaders to disease hotspots: linking transmission across hosts and space