ISMB/ECCB 2007: the Premier Conference on Computational Biology Thomas Lengauer, B
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright. -
AI and Bioinformatics
AI Magazine Volume 25 Number 1 (2004) (© AAAI) Articles Editorial Introduction AI and Bioinformatics Janice Glasgow, Igor Jurisica, and Burkhard Rost ■ This article is an editorial introduction to the re- modern-day biology is far more complex than search discipline of bioinformatics and to the articles suggested by the simplified sketch presented in this special issue. In particular, we address the issue here. In fact, researchers in life sciences live off of how techniques from AI can be applied to many of the introduction of new concepts; the discov- the open and complex problems of modern-day mol- ecular biology. ery of exceptions; and the addition of details that usually complicate, rather than simplify, his special issue of AI Magazine focuses the overall understanding of the field. on some areas of research in bioinfor- Possibly the most rapidly growing area of re- Tmatics that have benefited from applying cent activity in bioinformatics is the analysis AI techniques. Undoubtedly, bioinformatics is of microarray data. The article by Michael Mol- a truly interdisciplinary field: Although some la, Michael Waddell, David Page, and Jude researchers continuously affect wet labs in life Shavlik (“Using Machine Learning to Design science through collaborations or provision of and Interpret Gene-Expression Microarrays”) tools, others are rooted in the theory depart- introduces some background information and ments of exact sciences (physics, chemistry, or provides a comprehensive description of how engineering) or computer sciences. This wide techniques from machine learning can be used variety creates many different perspectives and to help understand this high-dimensional and terminologies. One result of this Babel of lan- prolific gene-expression data. -
Computational Biology and Bioinformatics
Vol. 30 ISMB 2014, pages i1–i2 BIOINFORMATICS EDITORIAL doi:10.1093/bioinformatics/btu304 Editorial This special issue of Bioinformatics serves as the proceedings of The conference used a two-tier review system, a continuation the 22nd annual meeting of Intelligent Systems for Molecular and refinement of a process begun with ISMB 2013 in an effort Biology (ISMB), which took place in Boston, MA, July 11–15, to better ensure thorough and fair reviewing. Under the revised 2014 (http://www.iscb.org/ismbeccb2014). The official confer- process, each of the 191 submissions was first reviewed by at least ence of the International Society for Computational Biology three expert referees, with a subset receiving between four and (http://www.iscb.org/), ISMB, was accompanied by 12 Special eight reviews, as needed. These formal reviews were frequently Interest Group meetings of one or two days each, two satellite supplemented by online discussion among reviewers and Area meetings, a High School Teachers Workshop and two half-day Chairs to resolve points of dispute and reach a consensus on tutorials. Since its inception, ISMB has grown to be the largest each paper. Among the 191 submissions, 29 were conditionally international conference in computational biology and bioinfor- accepted for publication directly from the first round review Downloaded from matics. It is expected to be the premiere forum in the field for based on an assessment of the reviewers that the paper was presenting new research results, disseminating methods and tech- clearly above par for the conference. A subset of 16 papers niques and facilitating discussions among leading researchers, were viewed as potentially in the top tier but raised significant practitioners and students in the field. -
Are You an Invited Speaker? a Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics
Are You an Invited Speaker? A Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics Senator Jeong, Sungin Lee, and Hong-Gee Kim Biomedical Knowledge Engineering Laboratory, Seoul National University, 28–22 YeonGeon Dong, Jongno Gu, Seoul 110–749, Korea. E-mail: {senator, sunginlee, hgkim}@snu.ac.kr Participating in scholarly events (e.g., conferences, work- evaluation, but it would be hard to claim that they have pro- shops, etc.) as an elite-group member such as an orga- vided comprehensive lists of evaluation measurements. This nizing committee chair or member, program committee article aims not to provide such lists but to add to the current chair or member, session chair, invited speaker, or award winner is beneficial to a researcher’s career develop- practices an alternative metric that complements existing per- ment.The objective of this study is to investigate whether formance measures to give a more comprehensive picture of elite-group membership for scholarly events is represen- scholars’ performance. tative of scholars’ prominence, and which elite group is By one definition (Jeong, 2008), a scholarly event is the most prestigious. We collected data about 15 global “a sequentially and spatially organized collection of schol- (excluding regional) bioinformatics scholarly events held in 2007. We sampled (via stratified random sampling) ars’ interactions with the intention of delivering and shar- participants from elite groups in each event. Then, bib- ing knowledge, exchanging research ideas, and performing liometric indicators (total citations and h index) of seven related activities.” As such, scholarly events are communica- elite groups and a non-elite group, consisting of authors tion channels from which our new evaluation tool can draw who submitted at least one paper to an event but were its supporting evidence. -
BIOINFORMATICS ISCB NEWS Doi:10.1093/Bioinformatics/Btp280
Vol. 25 no. 12 2009, pages 1570–1573 BIOINFORMATICS ISCB NEWS doi:10.1093/bioinformatics/btp280 ISMB/ECCB 2009 Stockholm Marie-France Sagot1, B.J. Morrison McKay2,∗ and Gene Myers3 1INRIA Grenoble Rhône-Alpes and University of Lyon 1, Lyon, France, 2International Society for Computational Biology, University of California San Diego, La Jolla, CA and 3Howard Hughes Medical Institute Janelia Farm Research Campus, Ashburn, Virginia, USA ABSTRACT Computational Biology (http://www.iscb.org) was formed to take The International Society for Computational Biology (ISCB; over the organization, maintain the institutional memory of ISMB http://www.iscb.org) presents the Seventeenth Annual International and expand the informational resources available to members of the Conference on Intelligent Systems for Molecular Biology bioinformatics community. The launch of ECCB (http://bioinf.mpi- (ISMB), organized jointly with the Eighth Annual European inf.mpg.de/conferences/eccb/eccb.htm) 8 years ago provided for a Conference on Computational Biology (ECCB; http://bioinf.mpi- focus on European research activities in years when ISMB is held inf.mpg.de/conferences/eccb/eccb.htm), in Stockholm, Sweden, outside of Europe, and a partnership of conference organizing efforts 27 June to 2 July 2009. The organizers are putting the finishing for the presentation of a single international event when the ISMB touches on the year’s premier computational biology conference, meeting takes place in Europe every other year. with an expected attendance of 1400 computer scientists, The multidisciplinary field of bioinformatics/computational mathematicians, statisticians, biologists and scientists from biology has matured since gaining widespread recognition in the other disciplines related to and reliant on this multi-disciplinary early days of genomics research. -
Applications of Case-Based Reasoning in Molecular Biology
Articles Applications of Case-Based Reasoning in Molecular Biology Igor Jurisica and Janice Glasgow ■ Case-based reasoning (CBR) is a computational problems by recalling old problems and their reasoning paradigm that involves the storage and solutions and adapting these previous experi- retrieval of past experiences to solve novel prob- ences represented as cases. A case generally lems. It is an approach that is particularly relevant comprises an input problem, an output solu- in scientific domains, where there is a wealth of data but often a lack of theories or general princi- tion, and feedback in terms of an evaluation of ples. This article describes several CBR systems that the solution. CBR is founded on the premise have been developed to carry out planning, analy- that similar problems have similar solutions. sis, and prediction in the domain of molecular bi- Thus, one of the primary goals of a CBR system ology. is to find the most similar, or most relevant, cases for new input problems. The effective- ness of CBR depends on the quality and quan- tity of cases in a case base. In some domains, even a small number of cases provide good so- lutions, but in other domains, an increased number of unique cases improves problem- he domain of molecular biology can be solving capabilities of CBR systems because characterized by substantial amounts of there are more experiences to draw on. Howev- Tcomplex data, many unknowns, a lack of er, larger case bases can also decrease the effi- complete theories, and rapid evolution; rea- ciency of a system. The reader can find detailed soning is often based on experience rather descriptions of the CBR process and systems in than general knowledge. -
Dear Delegates,History of Productive Scientific Discussions of New Challenging Ideas and Participants Contributing from a Wide Range of Interdisciplinary fields
3rd IS CB S t u d ent Co u ncil S ymp os ium Welcome To The 3rd ISCB Student Council Symposium! Welcome to the Student Council Symposium 3 (SCS3) in Vienna. The ISCB Student Council's mis- sion is to develop the next generation of computa- tional biologists. We would like to thank and ac- knowledge our sponsors and the ISCB organisers for their crucial support. The SCS3 provides an ex- citing environment for active scientific discussions and the opportunity to learn vital soft skills for a successful scientific career. In addition, the SCS3 is the biggest international event targeted to students in the field of Computational Biology. We would like to thank our hosts and participants for making this event educative and fun at the same time. Student Council meetings have had a rich Dear Delegates,history of productive scientific discussions of new challenging ideas and participants contributing from a wide range of interdisciplinary fields. Such meet- We are very happy to welcomeings have you proved all touseful the in ISCBproviding Student students Council and postdocs Symposium innovative inputsin Vienna. and an Afterincreased the network suc- cessful symposiums at ECCBof potential 2005 collaborators. in Madrid and at ISMB 2006 in Fortaleza we are determined to con- tinue our efforts to provide an event for students and young researchers in the Computational Biology community. Like in previousWe ar yearse extremely our excitedintention to have is toyou crhereatee and an the opportunity vibrant city of Vforienna students welcomes to you meet to our their SCS3 event. peers from all over the world for exchange of ideas and networking. -
Improved Prediction of Protein Secondary Structure by Use Of
Proc. Natl. Acad. Sci. USA Vol. 90, pp. 7558-7562, August 1993 Biophysics Improved prediction of protein secondary structure by use of sequence profiles and neural networks (protein structure prediction/multiple sequence alinment) BURKHARD ROST AND CHRIS SANDER Protein Design Group, European Molecular Biology Laboratory, D-6900 Heidelberg, Germany Communicated by Harold A. Scheraga, April 5, 1993 ABSTRACT The explosive accumulation of protein se- test set (7-fold cross-validation). The use of multiple cross- quences in the wake of large-scale sequencing projects is in validation is an important technical detail in assessing per- stark contrast to the much slower experimental determination formance, as accuracy can vary considerably, depending of protein structures. Improved methods of structure predic- upon which set of proteins is chosen as the test set. For tion from the gene sequence alone are therefore needed. Here, example, Salzberg and Cost (3) point out that the accuracy of we report a subsantil increase in both the accuracy and 71.0% for the initial choice of test set drops to 65.1% quality of secondary-structure predictions, using a neural- "sustained" performance when multiple cross-validation is network algorithm. The main improvements come from the use applied-i.e., when the results are averaged over several ofmultiple sequence alignments (better overall accuracy), from different test sets. We suggest the term sustained perfor- "balanced tninhg" (better prediction of«-strands), and from mance for results that have been multiply cross-validated. "structure context training" (better prediction of helix and The importance of multiple cross-validation is underscored strand lengths). This method, cross-validated on seven differ- by the difference in accuracy of up to six percentage points ent test sets purged of sequence similarity to learning sets, between two test sets for the reference network (58.3- achieves a three-state prediction accuracy of 69.7%, signi- 63.8%). -
A Novel Approach for Predicting Protein Functions by Transferring Annotation Via Alignment Networks Warith Djeddi, Sadok Ben Yahia, Engelbert Mephu Nguifo
A novel approach for predicting protein functions by transferring annotation via alignment networks Warith Djeddi, Sadok Ben Yahia, Engelbert Mephu Nguifo To cite this version: Warith Djeddi, Sadok Ben Yahia, Engelbert Mephu Nguifo. A novel approach for predicting protein functions by transferring annotation via alignment networks. 2019. hal-02070419 HAL Id: hal-02070419 https://hal.archives-ouvertes.fr/hal-02070419 Preprint submitted on 17 Mar 2019 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. A novel approach for predicting protein functions by transferring annotation via alignment networks Warith Eddine Djeddi1, Sadok Ben Yahia1;2∗ and Engelbert Mephu Nguifo3∗ 1University of Tunis El Manar, Faculty of Sciences of Tunis, LR11ES14, Capmus Universitaire 2092, Tunis, Tunisia 2Tallinn University of Technology, Department of Software Science, Akadeemia tee 15a, 12618 Tallinn, Estonia and 3 University Clermont Auvergne, CNRS, LIMOS, F-63000 CLERMONT-FERRAND, FRANCE ∗Corresponding author: [email protected], [email protected] Abstract One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactions for many model species, it becomes an opportunity to computational methods to elucidate protein func- tion based on many strategies. -
Basel Computational Biology Conference. from Information to Simulation
Information Extraction in Molecular Biology and Biomedicine Basel Computational Biology Conference. From Information to Simulation Basel, 18-19 March 2004 Alfonso Valencia, CNB - CSIC BC2, 2004 Alfonso Valencia CNB-CSIC Predicted networks Proteomics literature Functional BC2, 2004 Alfonso Valencia CNB-CSIC Genomics Information Extraction in Molecular Biology IE starts in biology [1] Keyword retrieval systems [2] Detection of protein names [3] Detection of protein-protein interactions [4] IE meets experiments [5] 1997 1998 1999 2000 2001 [1] Ohta et al. (1997). "Automatic construction of Knowledge Bases form Biological Papers" [2] Andrade and Valencia (1997). "Automatic annotation for biological sequences by extraction of keywords from MEDLINE abstracts" [3] Fukuda et al. (1998). "Information Extraction: Identifying Protein Names from Biological Papers" Proux et al. (1998). "Detecting Gene Symbols and Names in Biological Texts: a first step ..." [4] Blaschke et al. (1999). "Automatic Extraction of Biological Information ...: Protein-Protein Interactions" Park et al. (2001). "Incremental Parsing for Automatic Pathway Identification with Combinatorial Categorical Grammar" Proux et al. (2000). "... Information Extraction Strategy for gathering Data on Genetic Interactions" Rindflesch et al. (2000). "EDGAR: Extraction of Drugs, Genes and Relations from the Biomedical Literature" Sekimizu et al. (1998). "Identifying the Interaction between Genes and Gene Products based on frequently seen Verbs in Medline Abstracts" Thomas et al. (2000). "Automatic -
Computational Elucidation of the Regulatory Snps in the Non-Coding Regions of the Human Genome
AN ABSTRACT OF THE DISSERTATION OF Yao Yao for the degree of Doctor of Philosophy in Computer Science presented on February 16, 2021. Title: Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome Abstract approved: Stephen Ramsey We describe a series of novel computational models, CERENKOV (Computational Elu- cidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CE- RENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nu- cleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of a post-analysis of a genome-wide association study (GWAS); they include a novel accu- racy scoring metric (average rank, or AVGRANK) and a novel cross-validation strategy (locus-based sampling) that both correctly account for the \sparse positive bag" nature of the GWAS post-analysis rSNP recognition problem. We trained and validated the CERENKOV series models using a set of reference SNPs whose composition is based on selection criteria (linkage disequilibrium and minor allele frequency) that we designed to ensure relevance to GWAS post-analysis. The CERENKOV models are based on a machine-learning algorithm (gradient boosted decision trees) incorporating various SNP annotation features that are from genomic, epigenomic, phylogenetic, and chromatin data. CERENKOV2 includes features based on the geometry of the annotation features in data-space, and the CERENKOV3 models include features derived from SNP clus- tering, molecular network and convolutional output on genomic signals. We compared the validation performance of CERENKOV to nine other methods for rSNP recognition (including GWAVA, RSVP, DeltaSVM, DeepSEA, EIGEN, and DANQ), and found that CERENKOV's validation performance is the strongest out of all of the classifiers that we tested, by both traditional global rank-based measures (AUPRC, AUROC) and AV- GRANK.