Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Does industry matter? Examining the role of industry structure and organizational learning in innovation and brand performance

A manager's perception of industry structure (dynamism) has the potential to impact various organizational strategies and behaviors. This may be particularly so with regard to perceptions driving organizational learning orientations and innovation based marketing strategy. The position taken here suggests that firms operating within a competitive industry tend to pursue innovative ways of performing value-creating activities, which requires the development of learning capabilities. The results of a study of SMEs suggest that market focused learning, relative to other learning capabilities plays a key role in the relationships between industry structure, innovation and brand performance. The findings also show that market focused learning and internally focused learning influence innovation and that innovation influences a brand's performance. (c) 2005 Elsevier Inc. All rights reserved.

Supervised learning of graph structure

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition. © 2011 Springer-Verlag.

Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.

Integrated Marketing Communication (IMC): New Discipline with an Old Learning Approach: A Syllabi Analysis

Marketing communications as a discipline has changed significantly in both theory and practice over the past decade. But has our teaching of IMC kept pace with the discipline changes? The purpose of this paper is to explore how far the evolving concepts of IMC are reaching university learners. By doing this, the paper offers an approach to assessing how well marketing curricula are fulfilling their purpose. The course outlines (syllabi) for all IMC courses in 30 universities in Australia and five universities in New Zealand were analyzed. The findings suggest that most of what is taught in the units is not IMC. It is not directed by the key constructs of IMC, nor by the research informing the discipline. Rather, it appears to have evolved little from traditional promotion management units and is close in content and structure to many introductory advertising courses. This paper suggests several possible explanations for this, including: (1) a tacit rejection of IMC as a valid concept; (2) a lack of information about what IMC is and what it is not; and (3) a scarcity of teaching and learning materials that are clearly focused on key constructs and research issues of IMC.

A reflective model for colour studies learning

In a university context how should colour be taught in order to engage students? Entwistle states, ‘What we learn depends on how we learn, and why we have to learn it.’ Therefore, there is a need to address the accumulating evidence that highlights the effects of learning environments on the quality of student learning when considering colour education. It is necessary to embrace the contextual demands while ensuring that the student knowledge of colour and the joy of discovering its characteristics in practice are enhanced. Institutional policy is forcing educators to re-evaluate traditional studio’s effectiveness and the intensive 'hands-on' interactive approach that is embedded in such an approach. As curriculum development involves not only theory and project work, the classroom culture and physical environment also need to be addressed. The increase in student numbers impacting the number of academic staff/student ratio, availability of teaching support as well as increasing variety of student age, work commitments, learning styles and attitudes have called for positive changes to how we teach. The Queensland University of Technology’s restructure in 2005 was a great opportunity to re-evaluate and redesign the approach to teaching within the design units of Interior Design undergraduate program –including colour. The resultant approach “encapsulates a mode of delivery, studio structure, as well as the learning context in which students and staff interact to facilitate learning”1 with a potential “to be integrated into a range of Interior Design units as it provides an adaptive educational framework rather than a prescriptive set of rules”.

Cooperative learning in Thailand : professional development to enhance primary education

The overall purpose of this study was to develop a model to inform the design of professional development programs and the implementation of cooperative learning within Thai primary school mathematics classrooms. Action research design, with interviews, surveys and observations, was used for this study. Survey questionnaires and classroom observations investigated the factors that influence the implementation of cooperative learning strategies and academic achievement in Thai primary school mathematics classrooms. The teachers’ interviews and classroom observation also examined the factors that need to be addressed in teacher professional development programs in order to facilitate cooperative learning in Thai mathematics classrooms. The outcome of this study was a model consisting of two sets of criteria to inform the successful implementation of cooperative learning in Thai primary schools. The first set of criteria was for proposers and developers of professional development programs. This set consists of macro- and micro-level criteria. The macro-level criteria focus on the overall structure of professional development programs and how and when the professional development programs should be implemented. The micro-level criteria focused on the specific topics that need to be included in professional development programs. The second set of criteria was for Thai principals and teachers to facilitate the introduction of cooperative learning in their classrooms. The research outcome also indicated that the attainment of these cooperative learning strategies and skills had a positive impact on the students’ learning of mathematics.

Mainstreamed students with learning difficulties : failing and underachieving in the secondary school

Despite the rhetoric that students with learning difficulties are adequately supported within schools, the evidence suggests that they continue to experience school failure with devastating consequences. Students with learning difficulties are disproportionately represented as juvenile delinquents, as the unemployed and in mental health statistics. However, the defining of this group remains confused and imprecise and has not been a national priority. This has repercussions for both secondary schools and for the students themselves. This paper highlights research related to teaching practices, policies and school structure and their effects on the academic outcomes and emotional well being of students with learning difficulties. Finally, it makes a number of recommendations to change the status quo for these students.