Accurate knowledge of key genes that promote hair follicle growth and development is of great value in the field of hair research and dermatology. Compared with the traditional time-consuming and laborious experimental methods for obtaining key genes, the literature mining method can extract proven key genes for hair follicle growth from the vast amount of literature more quickly and comprehensively, i.e., perform the tasks of Named Entity Recognition (NER) and Relationship Extraction (RE) of related entities.

DOI Link: 
10.21227/3ajx-4z98
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[1] Tao Zhang, "a Hair Follicle Growth Association Gene Dataset", IEEE Dataport, 2023. [Online]. Available: http://dx.doi.org/10.21227/3ajx-4z98. Accessed: Mar. 22, 2025.
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doi = {10.21227/3ajx-4z98},
url = {http://dx.doi.org/10.21227/3ajx-4z98},
author = {Tao Zhang },
publisher = {IEEE Dataport},
title = {a Hair Follicle Growth Association Gene Dataset},
year = {2023} }
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T1 - a Hair Follicle Growth Association Gene Dataset
AU - Tao Zhang
PY - 2023
PB - IEEE Dataport
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Tao Zhang. (2023). a Hair Follicle Growth Association Gene Dataset. IEEE Dataport. http://dx.doi.org/10.21227/3ajx-4z98
Tao Zhang, 2023. a Hair Follicle Growth Association Gene Dataset. Available at: http://dx.doi.org/10.21227/3ajx-4z98.
Tao Zhang. (2023). "a Hair Follicle Growth Association Gene Dataset." Web.
1. Tao Zhang. a Hair Follicle Growth Association Gene Dataset [Internet]. IEEE Dataport; 2023. Available from : http://dx.doi.org/10.21227/3ajx-4z98
Tao Zhang. "a Hair Follicle Growth Association Gene Dataset." doi: 10.21227/3ajx-4z98