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Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks

Wagner, T.; Awasthi, S.; Wittenberg, G.; Venkatakrishnan, A.; Tarjan, D.; Anyanwu-Ofili, A.; Badley, A.; Halamka, J.; Flores, C.; Khan, N.; Barve, R.; Soundararajan, V.

2020-04-04 bioinformatics
10.1101/2020.04.03.020602 bioRxiv
Show abstract

Decoding disease mechanisms for addressing unmet clinical need demands the rapid assimilation of the exponentially growing biomedical knowledge. These are either inherently unstructured and non-conducive to current computing paradigms or siloed into structured databases requiring specialized bioinformatics. Despite the recent renaissance in unsupervised neural networks for deciphering unstructured natural languages and the availability of numerous bioinformatics resources, a holistic platform for real-time synthesis of the scientific literature and seamless triangulation with deep omic insights and real-world evidence has not been advanced. Here, we introduce the nferX platform that makes the highly unstructured biomedical knowledge computable and supports the seamless visual triangulation with statistical inference from diverse structured databases. The nferX platform will accelerate and amplify the research potential of subject-matter experts as well as non-experts across the life science ecosystem (https://academia.nferx.com/).

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Patterns
○ 78 papers in training set
Top 0.1%
9.7%
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2
Nature Methods
○ 385 papers in training set
Top 1%
9.7%
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3
Nature Communications
○ 5641 papers in training set
Top 21%
7.8%
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4
Nature Biotechnology
○ 172 papers in training set
Top 0.4%
6.6%
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5
Bioinformatics
◐ 1204 papers in training set
Top 4%
6.6%
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6
npj Digital Medicine
○ 118 papers in training set
Top 0.9%
6.2%
7
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
5.4%
50% of probability mass above
8
Nature Machine Intelligence
○ 70 papers in training set
Top 0.8%
3.2%
9
Molecular Systems Biology
○ 162 papers in training set
Top 0.7%
3.1%
10
Cell Reports Methods
○ 165 papers in training set
Top 0.8%
2.7%
11
iScience
○ 1154 papers in training set
Top 11%
2.4%
12
Scientific Reports
○ 3612 papers in training set
Top 46%
2.1%
13
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
2.1%
14
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 3%
2.1%
15
Nucleic Acids Research
◐ 1281 papers in training set
Top 8%
2.1%
16
Cell Genomics
○ 172 papers in training set
Top 2%
1.9%
17
Genome Medicine
○ 183 papers in training set
Top 3%
1.7%
18
GigaScience
◐ 212 papers in training set
Top 3%
1.5%
19
Nature
○ 645 papers in training set
Top 8%
1.4%
20
Genome Biology
○ 637 papers in training set
Top 7%
1.1%
21
Scientific Data
○ 209 papers in training set
Top 2%
1.1%
22
Database
◐ 61 papers in training set
Top 0.7%
1.1%
23
Nature Computational Science
○ 55 papers in training set
Top 1%
1.0%
24
npj Systems Biology and Applications
○ 125 papers in training set
Top 2%
0.8%
25
Cell Systems
○ 201 papers in training set
Top 5%
0.6%
26
The Lancet Digital Health
○ 25 papers in training set
Top 1%
0.6%
27
Genetics in Medicine
○ 78 papers in training set
Top 1%
0.6%
28
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 45%
0.6%
29
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 5%
0.6%
30
Communications Medicine
○ 113 papers in training set
Top 5%
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.