DOCSLIB.ORG

Centre for Bioinformatics Imperial College London Second Report 31

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Centre for Bioinformatics Imperial College London Second Report 31 Centre for Bioinformatics Imperial College London Second Report 31 May 2004 Centre Director: Prof Michael Sternberg www.imperial.ac.uk/bioinformatics [email protected] Support Service Head: Dr Sarah Butcher www.codon.bioinformatics.ic.ac.uk [email protected] Centre for Bioinformatics - Imperial College London - Second Report - May 2004 1 Contents Summary..................................................................................................................... 3 1. Background and Objectives .................................................................................. 4 1.1 Objectives of the Report........................................................................................ 4 1.2 Background........................................................................................................... 4 1.3 Objectives of the Centre for Bioinformatics........................................................... 5 1.4 Objectives of the Bioinformatics Support Service ................................................. 5 2. Management ......................................................................................................... 6 3. Biographies of the Team....................................................................................... 7 4. Bioinformatics Research at Imperial ..................................................................... 8 4.1 Affiliates of the Centre........................................................................................... 8 4.2 Research............................................................................................................... 9 5. Teaching .............................................................................................................. 11 5.1 MSc in Bioinformatics.......................................................................................... 11 5.2 Wellcome Trust 4 year PhD in Bioinformatics..................................................... 11 5.3 Computational Bioinformatics ............................................................................. 11 6. The Bioinformatics Support Service.................................................................... 12 6.1 Introduction ......................................................................................................... 12 6.2 Management ....................................................................................................... 12 6.3 Hardware, Software and Databases ................................................................... 12 6.4 Support & Training .............................................................................................. 16 6.4 Research Projects............................................................................................... 16 6.5 Grants ................................................................................................................. 17 6.6 Courses............................................................................................................... 17 6.7 Financial Arrangements ...................................................................................... 17 7. The London Bioinformatics Forum...................................................................... 19 7.1 Mission of the London Bioinformatics Forum ...................................................... 19 7.2 Objectives ........................................................................................................... 19 7.3 Management ....................................................................................................... 19 7.4 Activities.............................................................................................................. 19 8. Seminar Programme........................................................................................... 20 9. Achievements and Plans..................................................................................... 22 9.1 Achievements...................................................................................................... 22 9.2 Plans ................................................................................................................... 22 Appendix 1 - Selected Publications........................................................................... 23 Centre for Bioinformatics - Imperial College London - Second Report - May 2004 2 Summary The mission of the Centre for Bioinformatics is to promote and co-ordinate world-class research and training in Bioinformatics within Imperial College London and to provide state-of-the-art Bioinformatics support to members of Imperial for their research. Our second report describes the activities of the Centre from 1st February 2003 to 31st May 2004 and documents our publications and grants for the calendar year 2003. Additional information can be found at www.imperial.ac.uk/bioinformatics. The main achievements of the Centre during this period are: • The provision across the College of a Bioinformatics Support Service with 230 registered users as of May 2004. • The successful role of the Bioinformatics Support Service in obtaining a £600K BBSRC grant for the application of E-science to provide support for microarray analysis. • The development of collaborative research projects between the Support Service and several research groups in the College. • The expansion of the Centre with the addition of 11 new Affiliates. • The publication by our Affiliates of more than 50 refereed papers in Bioinformatics during 2003. • The award during 2003 of more than £2 million of grant support for research and training in Bioinformatics. • The co-ordination of postgraduate teaching of Bioinformatics across the College. • The running of a seminar series that attracts an audience from the College and other organisations in the London area. • The establishment with colleagues from other London groups of the London Bioinformatics Forum. Centre for Bioinformatics - Imperial College London - Second Report - May 2004 3 1. Background and Objectives 1.1 Objectives of the Report This is the second report of the Centre for Bioinformatics and covers the period 1st February 2003 to 31st May 2004. The objectives of this report are: • To document the status of the Centre as of May 2004, particularly describing the contribution from our Steering Committee, External Advisors and the Affiliates. • To highlight the main developments over the period of this report. • To describe the activities of the Bioinformatics Support Service in terms of facilities provided and its uptake by users. • To report grants awarded and publications for the calendar year 2003. 1.2 Background • Bioinformatics can be defined as the use of computational, mathematical and statistical methods to organise, analyse and interpret biological information, particularly at the molecular, genetic and genomic levels. • Bioinformatics is central to the interpretation and exploitation of the wealth of biological data being generated in the post-genome era with the consequential major clinical and commercial benefits. • It is vital that Imperial has world-class research in Bioinformatics together with state-of-the-art facilities for all users. Since Bioinformatics research is located in all four Faculties, a clearly identifiable focus is required, in particular to co- ordinate multi-disciplinary research. In parallel, it is essential to provide biologists and clinical reseachers with state-of-the-art Bioinformatics to empower them to deliver world-class research. • To address these issues, in 2001 the Deputy Rector together with the Faculties of Life Sciences and of Medicine established the Centre for Bioinformatics and the associated Bioinformatics Support Service. • The Bioinformatics Support Service is located on the newly-refurbished third floor of the Biochemistry Building on the South Kensington Campus. This acts as the focus for the Centre with its links to the different Departments and Campuses of the College. Centre for Bioinformatics - Imperial College London - Second Report - May 2004 4 1.3 Objectives of the Centre for Bioinformatics The objectives of the Centre for Bioinformatics are to: • Co-ordinate the strategic development of Bioinformatics at Imperial across the four Faculties and at the different Campuses. • Develop new collaborative projects within and outside Imperial, particularly those that are multi-disciplinary. • Contribute to a broad College-wide view of Bioinformatics including the development of links with areas such as statistical genetics, chemometrics and image processing. • Have a strategic role in the provision of teaching and training in Bioinformatics. • Organise seminar programs and symposia on Bioinformatics. • Disseminate relevant software, databases and information to the UK and world scientific communities, both academic and industrial. • Facilitate the provision of state-of-the-art Bioinformatics support to members of Imperial by directing the Bioinformatics Support Service. 1.4 Objectives of the Bioinformatics Support Service The objectives of the Bioinformatics Support Service are to provide the following services to all Imperial Campuses: • In-house facilities for major Bioinformatics tasks, such as sequence database searching and microarray processing. • Access to appropriate commercial software and data. • Curated links to public domain sites providing additional services. • Expertise and training courses on the use of the above facilities. • Collaborative
Load more

Recommended publications
  • Predicting and Characterising Protein-Protein Complexes
    Predicting and Characterising Protein-Protein Complexes Iain Hervé Moal June 2011 Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute and Department of Biochemistry and Molecular Biology, University College London A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Biochemistry at the University College London. 2 I, Iain Hervé Moal, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Abstract Macromolecular interactions play a key role in all life processes. The con- struction and annotation of protein interaction networks is pivotal for the understanding of these processes, and how their perturbation leads to dis- ease. However the extent of the human interactome and the limitations of the experimental techniques which can be brought to bear upon it necessit- ate theoretical approaches. Presented here are computational investigations into the interactions between biological macromolecules, focusing on the structural prediction of interactions, docking, and their kinetic and thermo- dynamic characterisation via empirical functions. Firstly, the use of normal modes in docking is investigated. Vibrational analysis of proteins are shown to indicate the motions which proteins are intrinsically disposed to under- take, and the use of this information to model flexible deformations upon protein-protein binding is evaluated. Subsequently SwarmDock, a docking algorithm which models flexibility as a linear combination of normal modes, is presented and benchmarked on a wide variety of test cases. This algorithm utilises state of the art energy functions and metaheuristics to navigate the free energy landscape.
    [Show full text]
  • Functional Effects Detailed Research Plan
    GeCIP Detailed Research Plan Form Background The Genomics England Clinical Interpretation Partnership (GeCIP) brings together researchers, clinicians and trainees from both academia and the NHS to analyse, refine and make new discoveries from the data from the 100,000 Genomes Project. The aims of the partnerships are: 1. To optimise: • clinical data and sample collection • clinical reporting • data validation and interpretation. 2. To improve understanding of the implications of genomic findings and improve the accuracy and reliability of information fed back to patients. To add to knowledge of the genetic basis of disease. 3. To provide a sustainable thriving training environment. The initial wave of GeCIP domains was announced in June 2015 following a first round of applications in January 2015. On the 18th June 2015 we invited the inaugurated GeCIP domains to develop more detailed research plans working closely with Genomics England. These will be used to ensure that the plans are complimentary and add real value across the GeCIP portfolio and address the aims and objectives of the 100,000 Genomes Project. They will be shared with the MRC, Wellcome Trust, NIHR and Cancer Research UK as existing members of the GeCIP Board to give advance warning and manage funding requests to maximise the funds available to each domain. However, formal applications will then be required to be submitted to individual funders. They will allow Genomics England to plan shared core analyses and the required research and computing infrastructure to support the proposed research. They will also form the basis of assessment by the Project’s Access Review Committee, to permit access to data.
    [Show full text]
  • Development of Novel Strategies for Template-Based Protein Structure Prediction
    Imperial College London Department of Life Sciences PhD Thesis Development of novel strategies for template-based protein structure prediction Stefans Mezulis supervised by Prof. Michael Sternberg Prof. William Knottenbelt September 2016 Abstract The most successful methods for predicting the structure of a protein from its sequence rely on identifying homologous sequences with a known struc- ture and building a model from these structures. A key component of these homology modelling pipelines is a model combination method, responsible for combining homologous structures into a coherent whole. Presented in this thesis is poing2, a model combination method using physics-, knowledge- and template-based constraints to assemble proteins us- ing information from known structures. By combining intrinsic bond length, angle and torsional constraints with long- and short-range information ex- tracted from template structures, poing2 assembles simplified protein models using molecular dynamics algorithms. Compared to the widely-used model combination tool MODELLER, poing2 is able to assemble models of ap- proximately equal quality. When supplied only with poor quality templates or templates that do not cover the majority of the query sequence, poing2 significantly outperforms MODELLER. Additionally presented in this work is PhyreStorm, a tool for quickly and accurately aligning the three-dimensional structure of a query protein with the Protein Data Bank (PDB). The PhyreStorm web server provides comprehensive, current and rapid structural comparisons to the protein data bank, providing researchers with another tool from which a range of biological insights may be drawn. By partitioning the PDB into clusters of similar structures and performing an initial alignment to the representatives of each cluster, PhyreStorm is able to quickly determine which structures should be excluded from the alignment.
    [Show full text]
  • Investigation of Topic Trends in Computer and Information Science
    Investigation of Topic Trends in Computer and Information Science by Text Mining Techniques: From the Perspective of Conferences in DBLP* 텍스트 마이닝 기법을 이용한 컴퓨터공학 및 정보학 분야 연구동향 조사: DBLP의 학술회의 데이터를 중심으로 Su Yeon Kim (김수연)** Sung Jeon Song (송성전)*** Min Song (송민)**** ABSTRACT The goal of this paper is to explore the field of Computer and Information Science with the aid of text mining techniques by mining Computer and Information Science related conference data available in DBLP (Digital Bibliography & Library Project). Although studies based on bibliometric analysis are most prevalent in investigating dynamics of a research field, we attempt to understand dynamics of the field by utilizing Latent Dirichlet Allocation (LDA)-based multinomial topic modeling. For this study, we collect 236,170 documents from 353 conferences related to Computer and Information Science in DBLP. We aim to include conferences in the field of Computer and Information Science as broad as possible. We analyze topic modeling results along with datasets collected over the period of 2000 to 2011 including top authors per topic and top conferences per topic. We identify the following four different patterns in topic trends in the field of computer and information science during this period: growing (network related topics), shrinking (AI and data mining related topics), continuing (web, text mining information retrieval and database related topics), and fluctuating pattern (HCI, information system and multimedia system related topics). 초 록 이 논문의 연구목적은 컴퓨터공학 및 정보학 관련 연구동향을 분석하는 것이다. 이를 위해 텍스트마이닝 기법을 이용하여 DBLP(Digital Bibliography & Library Project)의 학술회의 데이터를 분석하였다. 대부분의 연구동향 분석 연구가 계량서지학적 연구방법을 사용한 것과 달리 이 논문에서는 LDA(Latent Dirichlet Allocation) 기반 다항분포 토픽 모델링 기법을 이용하였다.
    [Show full text]
  • Can Predicate Invention Compensate for Incomplete Background Knowledge?
    Can predicate invention compensate for incomplete background knowledge? Andrew CROPPER Stephen MUGGLTEON Imperial College London, United Kingdom Abstract. In machine learning we are often faced with the problem of incomplete data, which can lead to lower predictive accuracies in both feature-based and re- lational machine learning. It is therefore important to develop techniques to com- pensate for incomplete data. In inductive logic programming (ILP) incomplete data can be in the form of missing values or missing predicates. In this paper, we inves- tigate whether an ILP learner can compensate for missing background predicates through predicate invention. We conduct experiments on two datasets in which we progressively remove predicates from the background knowledge whilst measuring the predictive accuracy of three ILP learners with differing levels of predicate inven- tion. The experimental results show that as the number of background predicates decreases, an ILP learner which performs predicate invention has higher predictive accuracies than the learners which do not perform predicate invention, suggesting that predicate invention can compensate for incomplete background knowledge. Keywords. inductive logic programming, predicate invention, relational machine learning, meta-interpretive learning 1. Introduction In an ideal world machine learning datasets would be complete. In reality, however, we are often faced with incomplete data, which can lead to lower predictive accuracies in both feature-based [6,10] and relational [22,17] machine learning. It is therefore important to develop techniques to compensate for incomplete data. In inductive logic programming (ILP) [11], a form of relational machine learning, incomplete background knowledge can be in the form of missing values or missing predicates [9].
    [Show full text]
  • Knowledge Mining Biological Network Models Stephen Muggleton
    Knowledge Mining Biological Network Models Stephen Muggleton To cite this version: Stephen Muggleton. Knowledge Mining Biological Network Models. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. pp.2- 2, 10.1007/978-3-642-16327-2_2. hal-01055072 HAL Id: hal-01055072 https://hal.inria.fr/hal-01055072 Submitted on 11 Aug 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Knowledge Mining Biological Network Models S.H. Muggleton Department of Computing Imperial College London Abstract: In this talk we survey work being conducted at the Cen- tre for Integrative Systems Biology at Imperial College on the use of machine learning to build models of biochemical pathways. Within the area of Systems Biology these models provide graph-based de- scriptions of bio-molecular interactions which describe cellular ac- tivities such as gene regulation, metabolism and transcription. One of the key advantages of the approach taken, Inductive Logic Pro- gramming, is the availability of background knowledge on existing known biochemical networks from publicly available resources such as KEGG and Biocyc.
    [Show full text]
  • Centre for Bioinformatics Imperial College London
    Centre for Bioinformatics Imperial College London Inaugural Report January 2003 Centre Director: Prof Michael Sternberg www.imperial.ac.uk/bioinformatics [email protected] Support Service Head: Dr Sarah Butcher www.codon.bioinformatics.ic.ac.uk [email protected] Centre for Bioinformatics - Imperial College London - Inaugural Report - January 2003 1 Contents Summary..................................................................................................................... 3 1. Background and Objectives .................................................................................... 4 1.1 Background ....................................................................................................... 4 1.2 Objectives of the Centre for Bioinformatics ....................................................... 4 1.3 Objectives of the Bioinformatics Support Service.............................................. 5 1.4 Historical Notes ................................................................................................. 5 2. Management ........................................................................................................... 6 3. Biographies of the Team......................................................................................... 7 4. Bioinformatics Research at Imperial ....................................................................... 8 4.1 Affiliates of the Centre ....................................................................................... 8 4.2 Research Grants
    [Show full text]
  • Towards Chemical Universal Turing Machines
    Towards Chemical Universal Turing Machines Stephen Muggleton Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ. [email protected] Abstract aid of computers. This is not only because of the scale of the data involved but also because scientists are unable Present developments in the natural sciences are pro- to conceptualise the breadth and depth of the relationships viding enormous and challenging opportunities for var- ious AI technologies to have an unprecedented impact between relevant databases without computational support. in the broader scientific world. If taken up, such ap- The potential benefits to science of such computerization are plications would not only stretch present AI technology high knowledge derived from large-scale scientific data has to the limit, but if successful could also have a radical the potential to pave the way to new technologies ranging impact on the way natural science is conducted. We re- from personalised medicines to methods for dealing with view our experience with the Robot Scientist and other and avoiding climate change (Muggleton 2006). Machine Learning applications as examples of such AI- In the 1990s it took the international human genome inspired developments. We also consider potential fu- project a decade to determine the sequence of a single hu- ture extensions of such work based on the use of Uncer- man genome, but projected increases in the speed of gene tainty Logics. As a generalisation of the robot scientist sequencing imply that before 2050 it will be feasible to de- we introduce the notion of a Chemical Universal Turing machine.
    [Show full text]
  • Efficiently Learning Efficient Programs
    Imperial College London Department of Computing Efficiently learning efficient programs Andrew Cropper Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Computing of Imperial College London and the Diploma of Imperial College London, June 2017 Abstract Discovering efficient algorithms is central to computer science. In this thesis, we aim to discover efficient programs (algorithms) using machine learning. Specifically, we claim we can efficiently learn programs (Claim 1), and learn efficient programs (Claim 2). In contrast to universal induction methods, which learn programs using only examples, we introduce program induction techniques which addition- ally use background knowledge to improve learning efficiency. We focus on inductive logic programming (ILP), a form of program induction which uses logic programming to represent examples, background knowledge, and learned programs. In the first part of this thesis, we support Claim 1 by using appropriate back- ground knowledge to efficiently learn programs. Specifically, we use logical minimisation techniques to reduce the inductive bias of an ILP learner. In addi- tion, we use higher-order background knowledge to extend ILP from learning first-order programs to learning higher-order programs, including the support for higher-order predicate invention. Both contributions reduce learning times and improve predictive accuracies. In the second part of this thesis, we support Claim 2 by introducing tech- niques to learn minimal cost logic programs. Specifically, we introduce Metaopt, an ILP system which, given sufficient training examples, is guaranteed to find minimal cost programs. We show that Metaopt can learn minimal cost robot strategies, such as quicksort, and minimal time complexity logic programs, including non-deterministic programs.
    [Show full text]
  • Alan Turing and the Development of Artificial Intelligence
    Alan Turing and the development of Artificial Intelligence Stephen Muggleton Department of Computing, Imperial College London December 19, 2012 Abstract During the centennial year of his birth Alan Turing (1912-1954) has been widely celebrated as having laid the foundations for Computer Sci- ence, Automated Decryption, Systems Biology and the Turing Test. In this paper we investigate Turing’s motivations and expectations for the development of Machine Intelligence, as expressed in his 1950 article in Mind. We show that many of the trends and developments within AI over the last 50 years were foreseen in this foundational paper. In particular, Turing not only describes the use of Computational Logic but also the necessity for the development of Machine Learning in order to achieve human-level AI within a 50 year time-frame. His description of the Child Machine (a machine which learns like an infant) dominates the closing section of the paper, in which he provides suggestions for how AI might be achieved. Turing discusses three alternative suggestions which can be characterised as: 1) AI by programming, 2) AI by ab initio machine learning and 3) AI using logic, probabilities, learning and background knowledge. He argues that there are inevitable limitations in the first two approaches and recommends the third as the most promising. We com- pare Turing’s three alternatives to developments within AI, and conclude with a discussion of some of the unresolved challenges he posed within the paper. 1 Introduction In this section we will first review relevant parts of the early work of Alan Turing which pre-dated his paper in Mind [42].
    [Show full text]
  • Knowledge Mining Biological Network Models
    Knowledge Mining Biological Network Models S.H. Muggleton Department of Computing Imperial College London Abstract: In this talk we survey work being conducted at the Cen- tre for Integrative Systems Biology at Imperial College on the use of machine learning to build models of biochemical pathways. Within the area of Systems Biology these models provide graph-based de- scriptions of bio-molecular interactions which describe cellular ac- tivities such as gene regulation, metabolism and transcription. One of the key advantages of the approach taken, Inductive Logic Pro- gramming, is the availability of background knowledge on existing known biochemical networks from publicly available resources such as KEGG and Biocyc. The topic has clear societal impact owing to its application in Biology and Medicine. Moreover, object descrip- tions in this domain have an inherently relational structure in the form of spatial and temporal interactions of the molecules involved. The relationships include biochemical reactions in which one set of metabolites is transformed to another mediated by the involvement of an enzyme. Existing genomic information is very incomplete concerning the functions and even the existence of genes and me- tabolites, leading to the necessity of techniques such as logical ab- duction to introduce novel functions and invent new objects. Moreo- ver, the development of active learning algorithms has allowed automatic suggestion of new experiments to test novel hypotheses. The approach thus provides support for the overall scientific cycle of hypothesis generation and experimental testing. Bio-Sketch: Professor Stephen Muggleton FREng holds a Royal Academy of Engineering and Microsoft Research Chair (2007-) and is Director of the Imperial College Computational Bioinformatics Centre (2001-) (www.doc.ic.ac.uk/bioinformatics) and Director of Modelling at the BBSRC Centre for Integrative Modelling at Impe- rial College.
    [Show full text]
  • Logic-Based Machine Learning
    Chapter LOGICBASED MACHINE LEARNING Stephen Muggleton and Flaviu Marginean Department of Computer Science University of York Heslington York YO DD United Kingdom Abstract The last three decades has seen the development of Computational Logic techniques within Articial Intelligence This has led to the development of the sub ject of Logic Programming LP which can b e viewed as a key part of LogicBased Articial Intelligence The subtopic of LP concerned with Machine Learning is known as Inductive Logic Programming ILP which again can b e broadened to Logic Based Machine Learning by dropping Horn clause restrictions ILP has its ro ots in the groundbreaking research of Gordon Plotkin and Ehud Shapiro This pap er provides a brief survey of the state of ILP application s theory and techniques Keywords Inductive logic programming machine learning scientic discovery protein prediction learning of natural language INTRODUCTION As envisaged by John McCarthy in McCarthy Logic has turned out to b e one of the key knowledge representations for Articial Intelligence research Logic in the form of the FirstOrder Predicate Calculus provides a representation for knowledge which has a clear semantics together with wellstudied sound rules of inference Interestingly Turing Turing and McCarthy McCarthy viewed a combination of Logic and Learning as b eing central to the development of Articial Intelligence research This article is concerned with the topic of LogicBased Machine Learning LBML The mo dern study of LBML has largely b een involved with the study
    [Show full text]