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From bioinformatics user to bioinformatics engineer: a report

Araujo, G. S.

2020-08-04 scientific communication and education
10.1101/2020.08.03.225979 bioRxiv
Show abstract

Teaching computer programming is not a simple task and it is challenging to introduce the concepts of programming in graduate programs of other fields. Little efforts have been made on engaging students in computational development after programming trainings. An emerging need is to establish subjects of bioinformatics and programming languages in genetics and molecular biology graduate programs, when students in these degree programs are immersed in a sea of genomic and transcriptomic data, which demands proficient computational treatment. I report an empirical guideline to introduce programming languages and recommend Python as first language for graduate programs in which students were from genetics and molecular biology backgrounds. Including the development of programming solutions related to graduate students' research activities may improve programming skills and better engagement. These results suggest that the applied approach leads to enhanced learning of introductory to autonomy in highly advanced programming concepts by graduate students. This guide should be extended for other research programs.

Matching journals

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

1
Ecology and Evolution
○ 267 papers in training set
Top 0.1%
15.3%
Similar papers in this journal
2
F1000Research
○ 88 papers in training set
Top 0.1%
12.1%
Similar papers in this journal
3
PLOS ONE
● 5266 papers in training set
Top 16%
11.2%
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4
PLOS Computational Biology
● 1863 papers in training set
Top 3%
9.8%
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5
Heliyon
○ 152 papers in training set
Top 1%
3.5%
Similar papers in this journal
50% of probability mass above
6
Journal of Microbiology & Biology Education
● 11 papers in training set
Top 0.1%
3.3%
7
eneuro
● 439 papers in training set
Top 2%
3.3%
8
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.8%
9
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.7%
10
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.2%
2.2%
11
GigaScience
◐ 212 papers in training set
Top 2%
1.8%
12
Gigabyte
● 62 papers in training set
Top 0.5%
1.8%
13
Acta Crystallographica Section D Structural Biology
● 59 papers in training set
Top 0.3%
1.7%
14
Scientific Reports
○ 3612 papers in training set
Top 56%
1.5%
15
Ecological Informatics
○ 33 papers in training set
Top 0.4%
1.5%
16
Frontiers in Microbiology
○ 427 papers in training set
Top 6%
1.5%
17
PeerJ
◐ 308 papers in training set
Top 7%
1.1%
18
Frontiers in Psychology
○ 56 papers in training set
Top 1%
1.1%
19
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.1%
20
Biology
○ 45 papers in training set
Top 0.3%
1.1%
21
Frontiers in Bioinformatics
○ 49 papers in training set
Top 1%
1.1%
22
BMC Medical Education
○ 21 papers in training set
Top 0.5%
1.1%
23
G3 Genes|Genomes|Genetics
◐ 351 papers in training set
Top 4%
1.0%
24
Brain Sciences
○ 55 papers in training set
Top 2%
0.9%
25
BioData Mining
○ 22 papers in training set
Top 0.6%
0.9%
26
eLife
● 5828 papers in training set
Top 65%
0.9%
27
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
0.9%
28
Sustainability
○ 10 papers in training set
Top 0.4%
0.9%
29
FEBS Open Bio
○ 31 papers in training set
Top 0.8%
0.9%
30
FASEB BioAdvances
○ 18 papers in training set
Top 0.5%
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.