Sentence examples for approach still outperforms from inspiring English sources

Since the links emanating from a node must be assigned different channels, this increases the NCR as well as decreasing the LCR for both CG approaches, as shown in Figures 1 and 2. However, the TCA-based approach still outperforms the MP-based approach by controlling the network connectivity using topology control, as shown next.

We still achieve higher precision and recall than the other approaches, showing that our integrated approach still outperforms other approaches when considering large complexes only.

The Neighborhood approach still outperforms the clustering-based approaches, though clustering algorithms such as Mcode which leave many proteins unclustered see a clear boost in performance.

Although a longer stochastic sequence length is required to meet a higher accuracy, the proposed SBN approach still outperforms an analytical approach for networks with a large number of genes and BNs, because its efficiency is not directly limited by the number of BNs.

Additionally, when the number of mobile ferries are reduced by 34% compared to gateways that are distributed based on geographical area, the mobile ferries approach still outperform geographical area based approach when it comes to messages delivery probability.

Figures 2 d and 3 d also show that these approaches are hampered by trying to predict small complexes: when their predicted small clusters are removed, their precisions increase substantially; nonetheless even here our approaches still outperform them.

Nonetheless, our approach (SSS + Extract) still outperforms all the other approaches, including using the PPIREL or SSS-weighted networks with standard clustering algorithms, or the PPIREL-weighted network with Extract.

The PMA algorithm performs 22%% worse compared to centralized algorithms on the average, but it still outperforms RAND approach by 21%% on the average.

See Supplementary Figure S7 for our results on the Y2H network, where Neighborhood still outperforms the clustering approaches in function prediction on the original network and on networks with a considerable fraction of annotations removed.

On the human physical interaction network from BioGRID, where a somewhat smaller fraction of proteins are annotated (51% with BP terms and 31% with CC terms), Neighborhood still outperforms the clustering approaches, though MCL is competitive with it (Supplementary Fig. S8) and performs better than it when ∼10% of the annotations are removed.

On the other hand, by exploiting the whole received signal through its second-order statistics, the proposed method offers good performance even with one pilot and still outperforms the frequency domain approach (even with much larger number of pilots).