The Relationships Among Multidimensional Perfectionsim, Shame and Trichotillomania Symptom Severity

The purpose of this study was to explore the relationship between multidimensional perfectionism, shame and Trichotillomania (TTM) symptom severity in a sample of college students and a clinical sample of individuals with TTM. A total of 286 college students were recruited from a large, Southeastern public University and 114 individuals with TTM were recruited across at a conference for individuals with TTM and TTM-focused social media communities. The study sought to explore whether shame (characterological, behavioral or bodily) mediated the relationship between wither adaptive or maladaptive perfectionism and TTM symptom severity. Correlations and tests of means were conducted and the Preacher and Hayes macro with bootstrapping was utilized to test mediation and moderation with the following measures: the Almost Perfect Scale-Revised (APS-R; Slaney et al., 2001), the Massachusetts General Hairpulling Scale (MGH-HPS; Keuthen et al., 1995, and the Experience of Shame Scale (ESS; Andrews, Qian, & Valentine, 2002). Results suggested that the clinical sample reported significantly higher levels of all three types of shame, as well as significantly higher scores for TTM severity than the student sample. No mediation or moderation was found among the variables for the student sample. In the clinical sample, no significant moderation was found, but behavioral shame was significantly mediated the relationship between maladaptive perfectionism and TTM severity. A discussion of limitations, implications for practitioners, and directions for future research were provided

Combining classifiers for improved classification of proteins from sequence or structure

<p>Abstract</p> <p>Background</p> <p>Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage.</p> <p>Results</p> <p>In this study, we develop a hybrid machine learning approach for classifying proteins, and we apply the method to the problem of assigning proteins to structural categories based on their sequences or their 3D structures. The method combines a full-coverage but lower accuracy nearest neighbor method with higher accuracy but reduced coverage multiclass SVMs to produce a full coverage classifier with overall improved accuracy. The hybrid approach is based on the simple idea of "punting" from one method to another using a learned threshold.</p> <p>Conclusion</p> <p>In cross-validated experiments on the SCOP hierarchy, the hybrid methods consistently outperform the individual component methods at all levels of coverage.</p> <p>Code and data sets are available at <url>http://noble.gs.washington.edu/proj/sabretooth</url></p

Rankprop: a web server for protein remote homology detection

Summary: We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein similarity network of 1.1 million proteins to construct the graph for the Rankprop algorithm, whereas previously, results were only reported for a database of 108 000 proteins. We also describe two algorithmic improvements to the original algorithm, including propagation from multiple homologs of the query and better normalization of ranking scores, that lead to higher accuracy and to scores with a probabilistic interpretation

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Background: Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results: We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion: By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition

NIPS workshop on New Problems and Methods in Computational Biology

The field of computational biology has seen dramatic growth over the past few years, both in terms of available data, scientific questions and challenges for learning and inference. These new types of scientific and clinical problems require the development of novel supervised and unsupervised learning approaches. In particular, the field is characterized by a diversity of heterogeneous data. The human genome sequence is accompanied by real-valued gene expression data, functional annotation of genes, genotyping information, a graph of interacting proteins, a set of equations describing the dynamics of a system, localization of proteins in a cell, a phylogenetic tree relating species, natural language text in the form of papers describing experiments, partial models that provide priors, and numerous other data sources. This supplementary issue consists of seven peer-reviewed papers based on the NIPS Workshop on New Problems and Methods in Computational Biology held at Whistler, British Columbia, Canada on December 8, 2006. The Neural Information Processing Systems Conference is the premier scientific meeting on neural computation, with session topics spanning artificial intelligence, learning theory, neuroscience, etc. The goal of this workshop was to present emerging problems and machine learning techniques in computational biology, with a particular emphasis on methods for computational learning from heterogeneous data. We received 37 extended abstract submissions, from which 13 were selected for oral presentation. The current supplement contains seven papers based on a subset of the 13 extended abstracts. Submitted manuscripts were rigorously reviewed by at least two referees. The quality of each paper was evaluated with respect to its contribution to biology as well as the novelty of the machine learning methods employed

Protein Ranking by Semi-Supervised Network Propagation

Background: Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, focus on detecting statistically significant pairwise sequence alignments and often miss more subtle sequence similarity. Recent work in the machine learning community has shown that exploiting the global structure of the network defined by these pairwise similarities can help detect more remote relationships than a purely local measure. Methods: We review RankProp, a ranking algorithm that exploits the global network structure of similarity relationships among proteins in a database by performing a diffusion operation on a protein similarity network with weighted edges. The original RankProp algorithm is unsupervised. Here, we describe a semi-supervised version of the algorithm that uses labeled examples. Three possible ways of incorporating label information are considered: (i) as a validation set for model selection, (ii) to learn a new network, by choosing which transfer function to use for a given query, and (iii) to estimate edge weights, which measure the probability of inferring structural similarity. Results: Benchmarked on a human-curated database of protein structures, the original RankProp algorithm provides significant improvement over local network search algorithms such as PSI-BLAST. Furthermore, we show here that labeled data can be used to learn a network without any need for estimating parameters of the transfer function, and that diffusion on this learned network produces better results than the original RankProp algorithm with a fixed network. Conclusion: In order to gain maximal information from a network, labeled and unlabeled data should be used to extract both local and global structure

Navigating Digital Ethics for Rural Research : Guidelines and recommendations for researchers and administrators of social media groups

This document was produced as a deliverable of the research project “Navigating Digital Ethics for Rural Research: Guidelines and recommendations for researchers and administrators of social media groups” (DigiEthics). Digiethics is a transdisciplinary project seeking to advance digital ethics by co-designing guidelines for engaging Facebook groups. This project was funded by the by the British Academy Early Career Research Network Scotland Hub Seed Fund 2023. This document is available online with background information at: https://www.hutton.ac.uk/research/ projects/digiethics-navigating-digital-ethics-rural-research If you have read/used this document and you have any comments or feedback you would like to share with us, we would love to hear from you. Please contact [email protected] PD