| Title |
The challenges of making decisions using uncertain data
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, July 2015
|
| DOI | 10.1007/s10822-015-9855-2 |
| Pubmed ID | |
| Authors |
Matthew D. Segall, Edmund J. Champness |
| Abstract |
All of the experimental compound data with which we work have significant uncertainties, due to imperfect correlations between experimental systems and the ultimate in vivo properties of compounds and the inherent variability in experimental conditions. When using these data to make decisions, it is essential that these uncertainties are taken into account to avoid making inappropriate decisions in the selection of compounds, which can lead to wasted effort and missed opportunities. In this paper we will consider approaches to rigorously account for uncertainties when selecting between compounds or assessing compounds against a property criterion; first for an individual measurement of a single property and then for multiple measurements of a property for the same compound. We will then explore how uncertainties in multiple properties can be combined when assessing compounds against a profile of criteria, a process known as multi-parameter optimisation. This guides rigorous decision-making using complex, uncertain data to focus on compounds with the best chance of success, while avoiding missed opportunities by inappropriately rejecting compounds. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 41 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 40% |
| Student > Ph. D. Student | 8 | 19% |
| Student > Master | 5 | 12% |
| Student > Doctoral Student | 2 | 5% |
| Student > Bachelor | 2 | 5% |
| Other | 6 | 14% |
| Unknown | 2 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 17 | 40% |
| Computer Science | 3 | 7% |
| Decision Sciences | 3 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 7% |
| Agricultural and Biological Sciences | 2 | 5% |
| Other | 11 | 26% |
| Unknown | 3 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2017.
All research outputs
#15,576,527
of 25,457,297 outputs
Outputs from Perspectives in Drug Discovery and Design
#687
of 949 outputs
Outputs of similar age
#139,059
of 277,768 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#5
of 8 outputs
Altmetric has tracked 25,457,297 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.