GenEpi: gene-based epistasis discovery using machine learning
Yu-Chuan Chang, June-Tai Wu, Ming-Yi Hong, Yi-An Tung, Ping-Han Hsieh, Sook Wah Yee, Kathleen M Giacomini, Yen-Jen Oyang et al.. BMC bioinformatics, 2020-02-24
Topic: Genetics and APOE
In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.
Key findings
- In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach.
- GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow.
- In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection.
- The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis.
- As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.
New to this subject? Start with our plain-English guide: Genetics →
Study details
- Evidence strengthLow
How this study scored 39/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 22 of the 56 points we could judge it on, which is 39 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (BMC bioinformatics) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32093643) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 22 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 22 of the 56 points we could judge it on is 39 out of 100. |
| Total | 39 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Chang, Y.-C., Wu, J.-T., Hong, M.-Y., Tung, Y.-A., Hsieh, P.-H., Yee, S. W., et al. (2020). GenEpi: gene-based epistasis discovery using machine learning. BMC bioinformatics. https://doi.org/10.1186/s12859-020-3368-2
Vancouver
Chang Y.-C, Wu J.-T, Hong M.-Y, Tung Y.-A, Hsieh P.-H, Yee SW, et al. GenEpi: gene-based epistasis discovery using machine learning. BMC bioinformatics. 2020. doi:10.1186/s12859-020-3368-2. PMID: 32093643.
BibTeX
@article{chang2020-32093643,
author = {Chang, Yu-Chuan and Wu, June-Tai and Hong, Ming-Yi and Tung, Yi-An and Hsieh, Ping-Han and Yee, Sook Wah and Giacomini, Kathleen M and Oyang, Yen-Jen and others},
title = {{GenEpi: gene-based epistasis discovery using machine learning}},
journal = {BMC bioinformatics},
year = {2020},
doi = {10.1186/s12859-020-3368-2},
pmid = {32093643},
url = {https://doi.org/10.1186/s12859-020-3368-2}
}
Related studies in Genetics
Also tagged “genetics”
346 studies in the library carry the topic tag “genetics”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 346 tagged studies.