OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied natural language processing (NLP) to analyze indications towards or against LTFU in HIV-positive patients' notes. MATERIALS AND METHODS: Unstructured lemmatized notes were labeled with an LTFU or Retained status using a 183-day threshold. An NLP and supervised machine learning system with a linear model and elastic net regularization was trained to predict this status. Prevalence of characteristics domains in the learned model weights were evaluated. RESULTS: We analyzed 838 LTFU vs 2964 Retained notes and obtained a weighted F1 mean of 0.912 via nested cross-validation; another experiment with notes from the same patients in both classes showed substantially lower metrics. "Comorbidities" were associated with LTFU through, for instance, "HCV" (hepatitis C virus) and likewise "Good adherence" with Retained, represented with "Well on ART" (antiretroviral therapy). DISCUSSION: Mentions of mental health disorders and substance use were associated with disparate retention outcomes, however history vs active use was not investigated. There remains further need to model transitions between LTFU and being retained in care over time. CONCLUSION: We provided an important step for the future development of a model that could eventually help to identify patients who are at risk for falling out of care and to analyze which characteristics could be factors for this. Further research is needed to enhance this method with structured electronic medical record fields.

MOTIVATION: It remains both a fundamental and practical challenge to understand and anticipate motions and conformational changes of proteins during their associations. Conventional normal mode analysis (NMA) based on anisotropic network model (ANM) addresses the challenge by generating normal modes reflecting intrinsic flexibility of proteins, which follows a conformational selection model for protein-protein interactions. But earlier studies have also found cases where conformational selection alone could not adequately explain conformational changes and other models have been proposed. Moreover, there is a pressing demand of constructing a much reduced but still relevant subset of protein conformational space to improve computational efficiency and accuracy in protein docking, especially for the difficult cases with significant conformational changes. METHOD AND RESULTS: With both conformational selection and induced fit models considered, we extend ANM to include concurrent but differentiated intra- and inter-molecular interactions and develop an encounter complex-based NMA (cNMA) framework. Theoretical analysis and empirical results over a large data set of significant conformational changes indicate that cNMA is capable of generating conformational vectors considerably better at approximating conformational changes with contributions from both intrinsic flexibility and inter-molecular interactions than conventional NMA only considering intrinsic flexibility does. The empirical results also indicate that a straightforward application of conventional NMA to an encounter complex often does not improve upon NMA for an individual protein under study and intra- and inter-molecular interactions need to be differentiated properly. Moreover, in addition to induced motions of a protein under study, the induced motions of its binding partner and the coupling between the two sets of protein motions present in a near-native encounter complex lead to the improved performance. A study to isolate and assess the sole contribution of intermolecular interactions toward improvements against conventional NMA further validates the additional benefit from induced-fit effects. Taken together, these results provide new insights into molecular mechanisms underlying protein interactions and new tools for dimensionality reduction for flexible protein docking. AVAILABILITY AND IMPLEMENTATION: Source codes are available upon request.

Linkage in terms of genetic algorithms is a measure of interdependence of groups of genes. When linkage exists, the fitness contribution of one gene depends on the allele setting of another. Our approach, a surrogate-assisted linkage inference genetic algorithm (SALIGA), is able to detect linkage for real-valued alleles. It uses a perturbation-based method with the aid of fitness surrogates and clustering techniques. Experimental results of linkage inference applied to several synthetic fitness functions are provided. The results demonstrate that SALIGA is able to reliably infer linkage and will correctly group the genes of an individual according to their linkage group membership, while using fewer fitness evaluations through a surrogate model. In addition, our results are augmented with a discussion regarding linkage detection through feature selection.

In this paper a music composition system based on genetic algorithms (GAs) will be presented. It can create multi-instrumental, guitar-orientated rock music using objective measures for its fitness functions. The output of this system is a song in the MIDI format. Along with this system, a unique conversion procedure from numerical values to the abc language (and vice versa), which allows the combination of numerical optimization with the rich expressiveness of a music description language, will be shown. The described music composition system will be further compared to other composition systems.