Proceedings of the 5th international workshop on Bioinformatics
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.073 | 0.052 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".