Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/1134030acmotherconferencesBook PagePublication PageskddConference Proceedingsconference-collections
BIOKDD '05: Proceedings of the 5th international workshop on Bioinformatics
ACM2005 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Chicago Illinois 21 August 2005
ISBN:
978-1-59593-213-6
Published:
21 August 2005

Reflects downloads up to 30 Aug 2024Bibliometrics
Skip Abstract Section
Abstract

Bioinformatics is the science of managing, mining, and interpreting information from biological entities. Genome sequencing projects have contributed to an exponential growth in complete and partial sequence databases. The structural genomics initiative aims to catalog the structure-function information for proteins. Advances in technology such as microarrays have launched the subfield of genomics and proteomics to study the genes, proteins, and the regulatory gene expression circuitry inside the cell. What characterizes the state of the field is the flood of data that exists today or that is anticipated in the future; data that needs to be mined to help unlock the secrets of the cell. Knowledge extracted from such analysis can be used effectively to better design new drugs, offer better medical care via diagnostic tests that combine information from multiple sources, and improve scientific and clinical practice.While tremendous progress has been made over the years, many of the fundamental problems in bioinformatics, such as protein structure prediction or gene finding, are still open. Data mining will play a fundamental role in understanding gene expression, drug design and other emerging problems in genomics and proteomics. Furthermore, text mining will be fundamental in extracting knowledge from the growing literature in bioinformatics.The goal of this workshop was to encourage KDD researchers to take on the numerous challenges that Bioinformatics offers. The workshop features an invited talk from a noted expert in the field, and the latest data mining research in bioinformatics from world class researchers. We encouraged papers that propose novel data mining techniques for tasks such as: Gene expression analysis; Protein/RNA structure prediction; Phylogenetics; Sequence and structural motifs; Genomics and Proteomics; Gene finding; Drug design; RNAi and microRNA Analysis; Text mining in bioinformatics; Modeling of biochemical pathways; and Biomedical and clinical informatics.These proceedings contain 10 papers (5 long and 5 short), out of 20 submissions that were accepted for presentation at the workshop. Each paper was reviewed by at least three members of the program committee. In some cases where there was a wide variance in reviews a fourth was sought. Each long paper selected had at least two strong supporters and no strong detractor. Each short paper selected had at least one strong supporter and typically no strong detractor. As a result along with a distinguished invited talk, we were able to assemble a very exciting program.This workshop follows the previous four highly successful workshops: BIOKDD04, held in Seattle, BIOKDD03, held in Washington, DC; BIOKDD02, held in Edmonton, Canada; and BIOKDD01 held in San Francisco, CA. We expect BIOKDD05 to be equally successful.

Skip Table Of Content Section
SESSION: Proteins
Article
Motif discovery for proteins using subsequence clustering

We propose an algorithm for discovering motifs using clustering of subsequences. In our previous approach, we were successful in guiding motif discovery by sampling subsequences and inputting them to an existing motif discovery tool MEME. In this paper, ...

Article
Graphical models of residue coupling in protein families

Identifying residue coupling relationships within a protein family can provide important insights into the family's evolutionary record, and has significant applications in analyzing and optimizing sequence-structure-function relationships. We present ...

SESSION: Genomics and health
Article
Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma

This paper asks whether susceptibility to early-onset (diagnosis before age 40) of a particularly deadly form of cancer, Multiple Myeloma, can be predicted from single-nucleotide polymorphism (SNP) profiles with an accuracy greater than chance. ...

SESSION: Sequences and microarrays
Article
Accelerating DNA sequencing-by-hybridization with noise

As a potential alternative to current wet-lab technologies, DNA sequencing-by-hybridization (SBH) has received much attention from different research communities. In order to deal with real applications, experiment environments should not be considered ...

Article
On discovery of maximal confident rules without support pruning in microarray data

Microarray data provides a perfect riposte to the original assumption underlying association rule mining -- large but sparse transaction sets. In a typical microarray the number of columns (genes) is an order of magnitude larger than the number of rows (...

Article
A datamining approach to cell population deconvolution from gene expressions using particle filters

Microarrays generally measure gene expressions from a mixture of cell subpopulations in different stages of a biological process. However, little or no information about these sub-populations is actually incorporated in existing data analyses. ...

Article
siRNA off-target search: a hybrid q-gram based filtering approach

Designing highly effective and gene-specific short interfering RNA (siRNA) sequences is crucial for any biological applications involving RNA interference (RNAi). A critical requirement for applying RNAi process in therapeutic applications is the ...

SESSION: Protein interaction networks and bioentity recognition
Article
Analysis of protein-protein interaction networks using random walks

Genome wide protein networks have become reality in recent years due to high throughput methods for detecting protein interactions. Recent studies show that a networked representation of proteins provides a more accurate model of biological systems and ...

Article
Finding cliques in protein interaction networks via transitive closure of a weighted graph

Finding protein functional modules in protein interaction networks amounts to finding densely connected subgraphs. Standard methods such as cliques and k-cores produce very small subgraphs due to highly sparse connections in most protein networks. ...

Article
Boosting performance of bio-entity recognition by combining results from multiple systems

The task of biomedical named-entity recognition is to identify technical terms in the domain of biology that are of special interest to domain experts. While numerous algorithms have been proposed for this task, biomedical named-entity recognition ...

Contributors
  • Rensselaer Polytechnic Institute
  1. Proceedings of the 5th international workshop on Bioinformatics

      Recommendations

      Acceptance Rates

      Overall Acceptance Rate 7 of 16 submissions, 44%
      YearSubmittedAcceptedRate
      BioKDD '1316744%
      Overall16744%