Back

A Metric for Quantifying the Resolution of Molecular Assays

Wilson, B. D.; Eisenstein, M.; Soh, H. T.

2021-05-09 bioengineering
10.1101/2021.05.07.443139 bioRxiv
Show abstract

Many assay developers focus on limit of detection (LOD) as a primary performance metric, and LOD is indeed useful for assays designed to determine the presence or absence of an analyte. However, LOD is less useful for continuous assays designed to discriminate between concentrations of an analyte--e.g., glucose monitoring in diabetes, where clinical care is guided by particular concentration ranges and thresholds. In such scenarios, it would be valuable to quantify discriminatory resolution--i.e., whether an assay can differentiate 1 pM from 10 pM-- but no such standardized metric currently exists. Here, we propose a useful solution, termed resolution of molecular concentration (RMC). RMC offers a simple means for characterizing quantitative resolution and quickly comparing the quantitative performance of assays. By raising awareness of the limitations of current metrics for evaluating assay performance, we hope to empower the molecular diagnostics community to evaluate their methods in a more application-appropriate manner.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Biosensors and Bioelectronics
○ 57 papers in training set
Top 0.1%
18.5%
Similar papers in this journal
2
Analytical Chemistry
● 218 papers in training set
Top 0.3%
12.9%
Similar papers in this journal
3
ACS Sensors
● 49 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
4
Lab on a Chip
● 96 papers in training set
Top 0.2%
6.2%
Similar papers in this journal
5
Scientific Reports
○ 3612 papers in training set
Top 16%
5.5%
Similar papers in this journal
6
The Analyst
● 16 papers in training set
Top 0.1%
5.5%
50% of probability mass above
7
Journal of Immunological Methods
○ 24 papers in training set
Top 0.1%
5.1%
8
npj Digital Medicine
○ 118 papers in training set
Top 1%
4.8%
9
Nature Communications
○ 5641 papers in training set
Top 36%
3.2%
10
Analytical and Bioanalytical Chemistry
○ 18 papers in training set
Top 0.1%
2.4%
11
ACS Omega
● 105 papers in training set
Top 1%
1.7%
12
ACS Synthetic Biology
● 287 papers in training set
Top 2%
1.1%
13
SLAS Technology
○ 14 papers in training set
Top 0.1%
1.1%
14
PLOS ONE
● 5266 papers in training set
Top 53%
1.1%
15
Communications Biology
○ 993 papers in training set
Top 24%
1.1%
16
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.0%
17
Clinical Chemistry
◐ 22 papers in training set
Top 0.3%
0.9%
18
ACS Central Science
● 71 papers in training set
Top 1%
0.9%
19
Clinical Chemistry and Laboratory Medicine (CCLM)
○ 13 papers in training set
Top 0.1%
0.8%
20
Sensors
○ 43 papers in training set
Top 1%
0.8%
21
SLAS Discovery
○ 25 papers in training set
Top 0.3%
0.8%
22
Communications Medicine
○ 113 papers in training set
Top 4%
0.8%
23
ACS Applied Bio Materials
● 24 papers in training set
Top 0.7%
0.8%
24
Heliyon
○ 152 papers in training set
Top 8%
0.8%
25
eneuro
● 439 papers in training set
Top 8%
0.6%
26
Journal of Clinical Virology
○ 63 papers in training set
Top 0.9%
0.6%
27
Infectious Diseases
○ 14 papers in training set
Top 0.2%
0.6%
28
Journal of Biomedical Optics
● 28 papers in training set
Top 0.5%
0.6%
29
Journal of Infection
○ 78 papers in training set
Top 1%
0.6%
30
Talanta
○ 14 papers in training set
Top 0.4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.