The Second International Symposium on Languages in Biology and Medicine (LBM) 2007
Bibliographic record
Abstract
The 2nd International Symposium on Languages in Biology and Medicine (LBM2007) was held in Singapore in December 2007. It provided a renewed opportunity for interaction between language professionals with different methodological backgrounds. In particular original research and applications of language technologies in biology and medicine were solicited. The relevant themes are listed below. · Natural language: text mining, retrieval and management; · Ontology language: ontology construction, extension and management; · Logic language: knowledge representation and induction; · Sequence language: RNA structure prediction, protein domain prediction; · Database language: database interface, query language; · Visualization language: information visualization, molecular visualization; A total of 47 submissions were received and reviewed by a 37 strong program committee and 19 additional reviewers from Asia, Europe and North America. The committee represented the six afore mentioned research disciplines and participated in a double blind review process with 3 reviews per paper. A selection of 12 papers was accepted (25.5% acceptance rate) for long oral presentations during the symposium and publication in the LBM special issue of BMC Bioinformatics. A further 11 out of the remaining 35 papers were selected for short oral presentation (31.4% acceptance rate). This LBM special issue of BMC Bioinformatics consists of 10 long oral presentation papers, as some papers were withdrawn due to unforeseen circumstances. A proceedings of LBM short paper presentations comprising of 7 papers was published by CEUR and is available at http://ceur-ws.org/Vol-319. From these two editions 8 papers are from Asia (China 1, Japan 3, Korea 1, Singapore 1, TaiWan 2), and 9 papers are from Europe and North America (Finland 1, Hungary 1, Sweden 1, UK 4, Canada 1, USA 1). These papers were presented in five sessions, namely (a) Terminology and Named Entities, (b) Text Classification 1, (c) Text Classification 2, (d) Text Mining, (e) Ontology and Logic. In addition to technical paper presentations, the 3 keynote presentations of the symposium addressed terminology integration (Olivier Bodenreider), text mining services (Sophia Ananiadou) and ontology alignment (Patrick Lambrix). The panel discussion chaired by Junichi Tsujii concluded the symposium with an examination of the synergies among the biomedical language and knowledge technologies. In conclusion we express our deep appreciation to the program committee members and the additional reviewers who worked on a very tight schedule, sharing their valuable time and formidable expertise in support of the LBM review process. We also thank, Ho-Joon Lee from KAIST and Chen Bin from I2R / NUS for their assistance with the EasyChair system, the LBM website and other miscellaneous tasks. We also wish to thank Jong C. Park, Limsoon Wong, the two general chairs and See Kiong Ng for their help and suggestions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".