On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection

International audienceWavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Computational Analysis Of Behavior In Employment Interviews And Video Resumes

Used in nearly every organization, employment interviews are a ubiquitous process where job applicants are evaluated by an employer for an open position. Consisting of an interpersonal interaction between at least one interviewer and a job applicant, they are used to assess interviewee knowledge, skills, abilities, and behavior in order to select the most suitable person for the job at hand. Because they require face-to-face interaction between at least two protagonists, they are inherently social, and all that recruiters have as a basis to forge their opinion is the applicant's behavior during the interview (in addition to his resume); in such settings, first impressions are known to play an important role. First impressions can be defined as snap judgments of others made based on a low amount of information. Interestingly, social psychology research has shown that humans are quite accurate at making inferences about others, even if the information is minimal. Social psychologists long studied job interviews, with the aim of understanding the relationships between behavior, interview outcomes, and job performance. Until recently, psychology studies relied on the use of time-intensive manual annotations by human observers. However, the advent of inexpensive audio and video sensors in the last decade, in conjunction with improved perceptual processing methods, has enabled the automatic and accurate extraction of behavioral cues, facilitating the conduct of social psychology studies. The use of automatically extracted nonverbal cues in combination with machine learning inference techniques has led to promising computational methods for the automatic prediction of individual and group social variables such as personality, emergent leadership, or dominance. In this thesis, we addressed the problem of automatically predicting hirability impressions from interview recordings by investigating three main aspects. First, we explored the use of state-of-the-art computational methods for the automatic extraction of nonverbal cues. As a rationale for selecting the behavioral features to be extracted, we reviewed the psychology literature for nonverbal cues which were shown to play a role in job interviews. While the main focus of this thesis is nonverbal behavior, we also investigated the use of verbal content and standard questionnaire outputs. Also, we did not limit ourselves to the use of existing techniques: we developed a multimodal nodding detection method based on previous findings in psychology stating that head gestures are conditioned on the speaking status of the person under analysis, and results showed that considering the speaking status improved the accuracy. Second, we investigated the use of supervised machine learning techniques for the prediction of hirability impressions in a regression task, and up to 36% of the variance could be explained, demonstrating that the automatic inference of hirability is a promising task. Finally, we analyzed the predictive validity of thin slices, short segments of interaction, and showed that short excerpts of job interviews could be predictive of the outcome, with up to 34% of the variance explained by nonverbal behavior extracted from thin slices. As another trend, online social media is changing the landscape of personnel recruitment. Until now, resumes were among the most widely used tools for the screening of job applicants. [...

Support Vector Machine-based Fuzzy Systems for Quantitative Prediction of Peptide Binding Affinity

Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines