30,298 research outputs found
A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.
Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 Ă— 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC
Introduction to Protein Structure Prediction
This chapter gives a graceful introduction to problem of protein three-
dimensional structure prediction, and focuses on how to make structural sense
out of a single input sequence with unknown structure, the 'query' or 'target'
sequence. We give an overview of the different classes of modelling techniques,
notably template-based and template free. We also discuss the way in which
structural predictions are validated within the global com- munity, and
elaborate on the extent to which predicted structures may be trusted and used
in practice. Finally we discuss whether the concept of a sin- gle fold
pertaining to a protein structure is sustainable given recent insights. In
short, we conclude that the general protein three-dimensional structure
prediction problem remains unsolved, especially if we desire quantitative
predictions. However, if a homologous structural template is available in the
PDB model or reasonable to high accuracy may be generated
Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease
To understand multifactorial conditions such as Alzheimers disease (AD) we
need brain signatures that predict the impact of multiple pathologies and their
interactions. To help uncover the relationships between brain circuits and
cognitive markers we have used mouse models that represent, at least in part,
the complex interactions altered in AD. In particular, we aimed to understand
the relationship between vulnerable brain circuits and memory deficits measured
in the Morris water maze, and we tested several predictive modeling approaches.
We used in vivo manganese enhanced MRI voxel based analyses to reveal regional
differences in volume (morphometry), signal intensity (activity), and magnetic
susceptibility (iron deposition, demyelination). These regions included the
hippocampus, olfactory areas, entorhinal cortex and cerebellum. The image based
properties of these regions were used to predict spatial memory. We next used
eigenanatomy, which reduces dimensionality to produce sets of regions that
explain the variance in the data. For each imaging marker, eigenanatomy
revealed networks underpinning a range of cognitive functions including memory,
motor function, and associative learning. Finally, the integration of
multivariate markers in a supervised sparse canonical correlation approach
outperformed single predictor models and had significant correlates to spatial
memory. Among a priori selected regions, the fornix also provided good
predictors, raising the possibility of investigating how disease propagation
within brain networks leads to cognitive deterioration. Our results support
that modeling approaches integrating multivariate imaging markers provide
sensitive predictors of AD-like behaviors. Such strategies for mapping brain
circuits responsible for behaviors may help in the future predict disease
progression, or response to interventions.Comment: 23 pages, 3 Tables, 6 Figures; submitted for publicatio
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Toward an integrative understanding of social behavior: new models and new opportunities.
Social interactions among conspecifics are a fundamental and adaptively significant component of the biology of numerous species. Such interactions give rise to group living as well as many of the complex forms of cooperation and conflict that occur within animal groups. Although previous conceptual models have focused on the ecological causes and fitness consequences of variation in social interactions, recent developments in endocrinology, neuroscience, and molecular genetics offer exciting opportunities to develop more integrated research programs that will facilitate new insights into the physiological causes and consequences of social variation. Here, we propose an integrative framework of social behavior that emphasizes relationships between ultimate-level function and proximate-level mechanism, thereby providing a foundation for exploring the full diversity of factors that underlie variation in social interactions, and ultimately sociality. In addition to identifying new model systems for the study of human psychopathologies, this framework provides a mechanistic basis for predicting how social behavior will change in response to environmental variation. We argue that the study of non-model organisms is essential for implementing this integrative model of social behavior because such species can be studied simultaneously in the lab and field, thereby allowing integration of rigorously controlled experimental manipulations with detailed observations of the ecological contexts in which interactions among conspecifics occur
Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction
Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class
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