ReviewThe Journal of biological chemistry2011
Advancing science through mining libraries, ontologies, and communities.
Review in The Journal of biological chemistry, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text mining applications in psychiatry: a systematic literature review.International journal of methods in psychiatric research · 2016Pooled it
- Utilizing Omic Data to Understand Integrative Physiology.Physiology (Bethesda, Md.) · 2025Review
- An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains.Scientometrics · 2023Article
- Predicting research trends with semantic and neural networks with an application in quantum physics.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications.Journal of chemical information and modeling · 2019Article
- MOLIERE: Automatic Biomedical Hypothesis Generation System.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2017Article
- Database of osmoregulated proteins in mammalian cells.Physiological reports · 2014Article
- A knowledge base of vasopressin actions in the kidney.American journal of physiology. Renal physiology · 2014Article
- Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.Expert opinion on medical diagnostics · 2013Article
- Data analysis and data mining: current issues in biomedical informatics.Methods of information in medicine · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Life scientists today cannot hope to read everything relevant to their research. Emerging text-mining tools can help by identifying topics and distilling statements from books and articles with increased accuracy. Researchers often organize these statements into ontologies, consistent systems of reality claims. Like scientific thinking and interchange, however, text-mined information (even when accurately captured) is complex, redundant, sometimes incoherent, and often contradictory: it is rooted in a mixture of only partially consistent ontologies. We review work that models scientific reason and suggest how computational reasoning across ontologies and the broader distribution of textual statements can assess the certainty of statements and the process by which statements become certain. With the emergence of digitized data regarding networks of scientific authorship, institutions, and resources, we explore the possibility of accounting for social dependences and cultural biases in reasoning models. Computational reasoning is starting to fill out ontologies and flag internal inconsistencies in several areas of bioscience. In the not too distant future, scientists may be able to use statements and rich models of the processes that produced them to identify underexplored areas, resurrect forgotten findings and ideas, deconvolute the spaghetti of underlying ontologies, and synthesize novel knowledge and hypotheses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.