DRBMDirector Relocation Business Management (Canada)
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References in periodicals archive ?
In order to comparatively evaluate the performance of DRBM, we compare it with SvD [15], NtN [16], dRW [18], and AE [29].
Based on the experimental protocols introduced above, we conduct experiments to investigate the performance of DRBM on protein function prediction.
From these tables, we can see that DRBM achieves better results than NtN, dRW, SVD, and AE in most cases.
Although dRW utilizes the hierarchical structure relationship between terms, it is still a shallow machine learning method and it does not capture the deep associations between proteins and GO terms as DRBM does, so it is often outperformed by DRBM.
The results of NtN and SVD are always lower than those of AE and DRBM. The possible reason is that singular value decomposition on sparse matrix is not suitable for this kind of protein function prediction problem, in which there are complex hierarchical relationships between GO terms.
From this table, we can see that DRBM is faster than these comparing methods, except SVD.
We investigate deep restricted Boltzmann machines (DRBM) for this purpose.