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PATTIE: Publication access through tiered interaction & exploration

Ortiz, M. S.; Wang, M.; Seki, K.; Kim, H.; Mostafa, J.

2019-10-16 scientific communication and education
10.1101/807404 bioRxiv
Show abstract

In this work we present Publication Access Through Tiered Interaction & Exploration (PATTIE) - an information foraging, sense-making, and exploratory spatial-semantic information retrieval (IR) system (http://pattie.unc.edu/plos). Non-spatial, spatial IR systems, and some recent studies focused on their principal functions are discussed and compared. To interactively work through a use-case from the biomedical domain, instructions are provided for readers to conduct exploratory searches directly on the PLOS archive based on the software embedded in the online version of this paper (http://vzlib.unc.edu/software/). To carefully evaluate some of the critical parameters of the PATTIE algorithm, and the core functions of the implemented system, a set of experiments were conducted. Along with details on the experimental methods and their rationale, key findings from the experiments are analyzed and presented. Finally, with an eye toward the future of software-embedded scientific papers, their potential benefits for supporting direct engagement with scientific content, replication, and validation are discussed.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 6%
26.9%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 4%
8.0%
Similar papers in this journal
3
Gigabyte
● 62 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
4
GigaScience
◐ 212 papers in training set
Top 0.5%
5.6%
Similar papers in this journal
5
PLOS Digital Health
● 106 papers in training set
Top 1%
4.9%
Similar papers in this journal
50% of probability mass above
6
Heliyon
○ 152 papers in training set
Top 0.7%
4.4%
7
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
3.3%
8
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.4%
9
Ecological Informatics
○ 33 papers in training set
Top 0.4%
1.8%
10
Bioinformatics
◐ 1204 papers in training set
Top 7%
1.8%
11
Scientific Reports
○ 3612 papers in training set
Top 51%
1.8%
12
F1000Research
○ 88 papers in training set
Top 1%
1.8%
13
Computers in Biology and Medicine
○ 128 papers in training set
Top 2%
1.7%
14
BioData Mining
○ 22 papers in training set
Top 0.2%
1.7%
15
Royal Society Open Science
● 214 papers in training set
Top 4%
1.4%
16
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.3%
1.4%
17
IEEE Journal of Biomedical and Health Informatics
● 37 papers in training set
Top 0.8%
1.1%
18
IEEE Access
● 35 papers in training set
Top 0.9%
1.1%
19
BMC Research Notes
○ 33 papers in training set
Top 0.7%
1.1%
20
eLife
● 5828 papers in training set
Top 58%
1.1%
21
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 2%
1.1%
22
Expert Systems with Applications
○ 11 papers in training set
Top 0.3%
1.1%
23
PeerJ
◐ 308 papers in training set
Top 9%
1.0%
24
PROTEOMICS
○ 43 papers in training set
Top 0.6%
1.0%
25
Database
◐ 61 papers in training set
Top 0.8%
0.9%
26
Patterns
○ 78 papers in training set
Top 3%
0.9%
27
Frontiers in Microbiology
○ 427 papers in training set
Top 8%
0.9%
28
Frontiers in Psychology
○ 56 papers in training set
Top 2%
0.6%
29
Brain Sciences
○ 55 papers in training set
Top 2%
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.