### Table 1. Distribution of membrane organization classes and high-quality localization data in LOCATE

2006

"... In PAGE 2: ... Because it was not always possible to determine to which protein isoform the literature data referred, we assigned the literature-mined location to all protein isoforms encoded by the corresponding TU. Table1 summarizes the subcellular localization statistics by membrane organization class. To provide as complete a location description as possible for any given protein, we also include localization data mined from other online databases including LIFEdb (11), Mouse Genome Informatics (12), UniProt (13), RefSeq (14) and oth- ers.... ..."

Cited by 4

### TABLE V HIGH-QUALITY VIEWING WITH DYNAMIC THRESHOLD HEURISTIC.

2000

Cited by 8

### Table 2: Schemata tness for the integer one-max problem using binary versus gray encoding. The integer one-max problem is completely easy for the binary encoding. Using gray encoding results in a more di cult problem, because some of the high quality schemata have the same tness as misleading schemata.

2002

"... In PAGE 11: ...Bs k = 1. Then all schemata containing the global optimum are superior to their competitors. In contrast, a problem is di cult and deceptive of order k if all schemata of lower order than k 2 f1; : : : lg containing the global optimum are inferior to their competitors (Deb amp; Goldberg, 1994). In Table2 , we present the average tness of the schemata for the integer one-max problem using binary and gray encoding for l = 3. The numbers reveal that for the integer one-max problem with binary encoding all schemata containing the global optimum xg = 111 are superior to their competitors.... ..."

Cited by 2

### TABLE 1. Number of target and NN sequences required for development of high-quality diagnostic signaturesa

2004

Cited by 5

### Table 1: Summary of medical imaging applications that can benefit from high-quality interpolation and splines in particular.

2002

Cited by 2

### Table 4: Statistics relative to high-quality mesh enrichment (option -O 1).

2001

"... In PAGE 18: ... As for the corresponding dec- imation option, an internal curvature-based metric is constructed that prescribe, at each mesh vertex, the desired element size, depending on the Tolerance, GeomApp and Gradation values specified. Table4 reports statistics about the mesh enrichment procedure for various surface meshes. In this table, a50a21a51 and a50a74a52 represent the number of points and triangles, a54a75a38a48a44a49a46 and a54a55a44a47a57a47a58 represent respectively the worst and average element shape quality, a54 a59 a27a62a61a64a63 is the percentage of elements having a quality better than 2, a65a66a44a47a57a47a58 and a67 represent the average edge lengths and the efficiency coefficient, a68a69a51a71a70 denotes the CPU time (on a HP 9000 workstation).... ..."

### Table 1: Statistics relative to high-quality mesh simplification (option -O -1).

252

"... In PAGE 15: ...he surface). In this kind of mesh, the element sizes are directly related to the local curvature. This op- tion involves three parameters : the tolerance Tolerance, the geometric approximation GeomApp and the mesh gradation Gradation. Table1 reports some results for this type of mesh simplification procedure. In this table, a22 a0 and a22 a2a1 represent the number of points and triangles, a3 a11 a17a20a19 and a3 a17 a5a4a5a6 represent respectively the worst and average element shape quality (see Appendix), a3a8a7 a1 a10a9a12a11 is the percentage of elements having a quality better than 2, a13 a17 a14a4a5a6 and a15 represent the average edge lengths and the efficiency coefficient, a16 a0a18a17 denotes the CPU time (on a 550 Mhz HP 9000 workstation).... ..."