EnDNA-Prot:identification of DNA-binding proteins by applying ensemble learning

DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97-9.52% in ACC and 0.08-0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83-16.63% in terms of ACC and 0.02-0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public. © 2014 Ruifeng Xu et al.

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.

Good practice guide in learning and teaching

Once again this publication is produced to celebrate and promote good teaching and learning support and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. We had changed our editorial approach in drawing together the articles for this 2005/6 edition (our third) of the ABS Good Practice Guide. Firstly we have expanded our contributors beyond ABS academics. This year?s articles have also been written by staff from other areas of the University, a PhD student, a post-doctoral researcher and staff working in learning support. We see this as an acknowledgement that the learning environment involves a range of people in the process of student support. We have also expanded the maximum length of the articles from two to five pages, in order to allow greater reflection on the issues. The themes of the papers cluster around issues relating to diversity (widening participation and internationalisation of the student body), imaginative use of new technology (electronic reading on BlackboardTM ) and reflective practitioners, (reflection on rigour and relevance; on how best to train students in research ethics, relevance in the curriculum and the creativity of the teaching process) Discussion of efforts to train the HE teachers of the future looks forward to the next academic year when the Higher Education Academy?s professional standards will be introduced across the sector. In the last volume we mentioned the launch of the School?s Research Centre in Higher Education Learning and Management (HELM). Since then HELM has stimulated a lot of activity across the School (and University) particularly linking research and teaching. A list of the HELM seminars is listed as an appendix to this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk) who coordinates the HELM seminars. HELM has also won its first independent grant from the EU Leonardo programme to look at the effect of business education on employment. In its annual report to the ABS Research Committee HELM listed for 2004 and 2005, 11 refereed journal articles, 4 book chapters, 3 published conference papers, 18 conference papers, one official reports and £72,500 of grant money produced in this research area across the School. I hope that this shows that reflection on learning is live and well in ABS. May I thank the contributors for taking time out of their busy schedules to write the articles and to Julie Green, the Quality Manager, for putting our diverse approaches into a coherent and publishable form.

The impact of learning outcomes in business education: assessing value, relevance and graduate ability in a multi-country study of employers and business graduates

This paper discusses critical findings from a two-year EU-funded research project involving four European countries: Austria, England, Slovenia and Romania. The project had two primary aims. The first of these was to develop a systematic procedure for assessing the balance between learning outcomes acquired in education and the specific needs of the labour market. The second aim was to develop and test a set of meta-level quality indicators aimed at evaluating the linkages between education and employment. The project was distinctive in that it combined different partners from Higher Education, Vocational Training, Industry and Quality Assurance. One of the key emergent themes identified in exploratory interviews was that employers and recent business graduates in all four countries want a well-rounded education which delivers a broad foundation of key business knowledge across the various disciplines. Both groups also identified the need for personal development in critical skills and competencies. Following the exploratory study, a questionnaire was designed to address five functional business areas, as well as a cluster of 8 business competencies. Within the survey, questions relating to the meta-level quality indicators assessed the impact of these learning outcomes on the workplace, in terms of the following: 1) value, 2) relevance and 3) graduate ability. This paper provides an overview of the study findings from a sample of 900 business graduates and employers. Two theoretical models are proposed as tools for predicting satisfaction with work performance and satisfaction with business education. The implications of the study findings for education, employment and European public policy are discussed.