ISCB/SPRINGER series in computational biology
Bibliographic record
Abstract
TITLES IN THIS SERIES NOW INCLUDED IN THE THOMSON REUTERS BOOK CITATION INDEX! In late 2012, the International Society for Computational Biology (ISCB) and Springer partnered together to enhance the Springer book series in computational biology. The two worked closely together to come up with a strategy to bring to ISCB members and the community at large educational materials that would not only educate the community but also help advance the science. Sponsored by ISCB, the computational biology series publish the latest high-quality research devoted to specific issues in computer-assisted analysis of biological data. The main emphasis is on current scientific developments and innovative techniques in computational biology (bioinformatics), bringing to light methods from mathematics, statistics and computer science that directly address biological problems currently under investigation. The series offer publications that present the state-of-the-art regarding the problems in question, show computational biology/bioinformatics methods at work and discuss anticipated demands regarding developments in future methodology. Titles can range from focused monographs, to undergraduate and graduate textbooks and professional text/reference works. Additionally, ISCB members will receive a 25% discount on book purchases within the series. Springer is seeking to publish quality books in the areas including, but not limited to, databases, data analysis and ontologies; functional and comparative genomics; gene regulation and transcriptomics; protein interactions and networks; data, literature and text mining; molecular sequence analysis; biological networks; sequencing and genotyping technologies; population genetics; systems biology; imaging and visualization; computational proteomics; molecular structural biology; evolution and phylogenetics; metagenomics; biomedical applications; high performance bio-computing; and synthetic biological systems. Book proposal submission details can be found at the book series Web site (http://www.springer.com/series/5769). Andreas Dress, CAS-MPG Partner Institute for Computational Biology, China Michal Linial, Hebrew University of Jerusalem, Israel Olga Troyanskaya, Princeton University, USA Martin Vingron, Max Planck Institute for Molecular Genetics, Germany Gene Myers, Janelia Farm Research Campus, Howard Hughes Medical Institute, USA Robert Giegerich, University of Bielefeld, Germany Walter Fitch, University of California, Irvine, USA Pavel A. Pevzner, University of California, San Diego, USA Janet Kelso, Max-Planck Institute for Evolutionary Anthropology, Germany Gordon Crippen, University of Michigan, USA Joe Felsenstein, University of Washington, USA Dan Gusfield, University of California, Davis, USA Sorin Istrail, Brown University, Providence, USA Samuel Karlin, Stanford University, USA Thomas Lengauer, Max Planck Institut Informatik, Germany Marcella McClure, Montana State University, USA Martin Nowak, Harvard University, USA David Sankoff, University of Ottawa, Canada Ron Shamir, Tel Aviv University, Israel Mike Steel, University of Canterbury, New Zealand Gary Stormo, Washington University Medical School, USA Simon Tavaré, University of Southern California, USA Tandy Warnow, University of Texas, Austin, USA Phenotypes and Genotypes Search for Influential Genes Series: Computational Biology, Vol. 18 Frommlet, Florian; Bogdan, Malgorzata 2014 Introduction to Evolutionary Genomics Series: Computational Biology, Vol. 17 Saitou, Naruya 2014 Models and Algorithms for Genome Evolution Series: Computational Biology, Vol. 19 Chauve, Cedric; El-Mabrouk, Nadia; Tannier, Eric (Eds.) 2013 Modeling in Systems Biology The Petri Net Approach Series: Computational Biology, Vol. 16 Koch, Ina; Reisig, Wolfgang; Schreiber, Falk (Eds.) 2011 Frontiers in Computational and Systems Biology Series: Computational Biology, Vol. 15 Feng, Jianfeng; Fu, Wenjiang; Sun, Fengzhu (Eds.) 2010 Comparative Gene Finding Models, Algorithms and Implementation Series: Computational Biology, Vol. 11 Axelson-Fisk, Marina 2010 Foundations of Systems Biology Using Cell Illustrator and Pathway Databases Series: Computational Biology, Vol. 13 Nagasaki, M., Saito, A., Doi, A., Matsuno, H., Miyano, S. 2009 Bioinformatics An Introduction Series: Computational Biology, Vol. 10 Ramsden, Jerem 2009 Computing for Comparative Microbial Genomics Bioinformatics for Microbiologists Series: Computational Biology, Vol. 8 Ussery, David Wayne, Wassenaar, Trudy M., Borini, Stefano 2009 Sequence Comparison Theory and Methods Series: Computational Biology, Vol. 7 Chao, Kun-Mao, Zhang, Louxin 2009 Protein–protein Interactions and Networks Identification, Computer Analysis and Prediction Series: Computational Biology, Vol. 9 Panchenko, Anna; Przytycka, Teresa M. (Eds.) 2008 Anatomy Ontologies for Bioinformatics Principles and Practice Series: Computational Biology, Vol. 6 Burger, Albert; Davidson, Duncan; Baldock, Richard (Eds.) 2008 Artificial Intelligence Methods and Tools for Systems Biology Series: Computational Biology, Vol. 5 Dubitzky, W.; Azuaje, Francisco (Eds.) 2004 Bioinformatics: An Introduction Series: Computational Biology, Vol. 3 Ramsden, Jeremy J. 2004 Phylogenetic Supertrees Combining Information to reveal the Tree of Life Series: Computational Biology, Vol. 4 Bininda-Emonds, Olaf R.P. (Eds.) 2004 Hidden Markov Models for Bioinformatics Series: Computational Biology, Vol. 2 Koski, T. 2001 Comparative Genomics Empirical and Analytical Approaches to Gene Order Dynamics, Map Alignment and the Evolution of Gene Families Series: Computational Biology, Vol. 1 Sankoff, D; Nadeau, J.H. (Eds.) 2000
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.327 | 0.345 |
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".