"... In PAGE 8: ... It is interesting that the spherical model of the IMM approach produces unstable clusters at both high and low noise levels. Yeast galactose data Table4 a,b show selected results on cluster accuracy and cluster stability on real yeast galactose data. The true mean column in Table 4a refers to clustering the true mean data R34.... In PAGE 8: ... Yeast galactose data Table 4a,b show selected results on cluster accuracy and cluster stability on real yeast galactose data. The true mean column in Table4 a refers to clustering the true mean data R34.8 Genome Biology 2003, Volume 4, Issue 5, Article R34 Yeung et al.... In PAGE 9: ... The highest level of cluster accuracy (adjusted Rand index = 0.968 in Table4 a) was obtained with several algorithms: centroid linkage hierarchical algorithm with Euclidean dis- tance and averaging over the repeated measurements; hier- archical model-based algorithm (MCLUST-HC); complete linkage hierarchical algorithm with SD-weighted distance; and IMM with complete linkage. Clustering with repeated measurements produced more accurate clusters than clus- tering with the estimated true mean data in most cases.... In PAGE 9: ... Clustering with repeated measurements produced more accurate clusters than clus- tering with the estimated true mean data in most cases. Table4 b shows that different clustering approaches lead to different cluster stability with respect to remeasured data. Similar to the results from the completely synthetic data, Euclidean distance tends to produce more stable clusters than correlation (both variability-weighted and average over repeated measurements).... ..."

"... In PAGE 3: ...Phenethanolamine N-methyltransferase, Monoamine oxidase A, Monoamine oxidase B, Catechol-O-methyltransferase Catecholamine synthetic enzymes 3 Actin, Alpha-tubulin, Beta-tubulin, Alpha-spectrin, Dynein Cytoskeletal proteins 4 Chorismate mutase, Prephenate dehydratase, Prephenate dehydrogenase, Tyrosine transaminase Enzymes in tyrosine and phenylalanine synthesis Table 2. 44 Yeast Genes grouped by transcriptional activators and cell cycle functions [1] Group Activators Genes Functions 1 Swi4, Swi6 Cln1, Cln2, Gic1, Gic2, Msb2, Rsr1, Bud9, Mnn1, Och1, Exg1, Kre6, Cwp1 Budding 2 Swi6, Mbp1 Clb5, Clb6, Rnr1, Rad27, Cdc21, Dun1, Rad51, Cdc45, Mcm2 DNA replication and repair 3 Swi4, Swi6 Htb1, Htb2, Hta1, Hta2, Hta3, Hho1 Chromatin 4 Fkh1 Hhf1, Hht1, Tel2, Apr7 Chromatin 5 Fkh1 Tem1 Mitosis control 6 Ndd1, Fkh2, Mcm1 Clb2, Ace2, Swi5, Cdc20 Mitosis control 7 Ace2, Swi5 Cts1, Egt2 Cytokinesis 8 Mcm1 Mcm3, Mcm6, Cdc6, Cdc46 Prereplication complex formation 9 Mcm1 Ste2, Far1 Mating well-defined functional groups consisting of ten glutamate receptor subunits, seven enzymes in catecholamine metabolism, five cytoskeletal proteins and four enzymes in tyrosine and phenylalanine synthesis ( Table1 ). This experiment was performed to determine the quality of the keywords derived from the two weighting schemes.... ..."

"... In PAGE 5: ...able 20 Response Element...........................................................................................................35 Table21 NonSequenceReply Element.... ..."

"... In PAGE 14: ... The data were then divided into a training set containing two- thirds of the samples and a test set containing the remaining third. All the reported results except those reported in Table1 are computed for the test set. The functional classification was obtained from the Munich Information Center for Protein Sequences Yeast Genome Database (MYGD).... In PAGE 16: ... To characterize all the genes, the learning and the test sets were now combined. In Table1 , each gene is assigned to the cluster having the largest value of the membership function for that gene. The table reveals that many subclasses are concentrated in one of the clusters found by our algorithm.... In PAGE 16: ...s 1.2 bits for our approach and 0.92 for the other two. In Table1 , produced by our method, three of the subclasses (c, e, and f) have been clearly divided into two clusters, suggesting a possible biologi- cally interesting division. Its relevance will be determined later by further biological inspection; in this article, our goal is to demonstrate that the semisupervised clustering approach can be used to explore the data set and... ..."

"... In PAGE 14: ... The data were then divided into a training set containing two- thirds of the samples and a test set containing the remaining third. All the reported results except those reported in Table1 are computed for the test set. The functional classification was obtained from the Munich Information Center for Protein Sequences Yeast Genome Database (MYGD).... In PAGE 16: ... To characterize all the genes, the learning and the test sets were now combined. In Table1 , each gene is assigned to the cluster having the largest value of the membership function for that gene. The table reveals that many subclasses are concentrated in one of the clusters found by our algorithm.... In PAGE 16: ...s 1.2 bits for our approach and 0.92 for the other two. In Table1 , produced by our method, three of the subclasses (c, e, and f) have been clearly divided into two clusters, suggesting a possible biologi- cally interesting division. Its relevance will be determined later by further biological inspection; in this article, our goal is to demonstrate that the semisupervised clustering approach can be used to explore the data set and... ..."

"... In PAGE 2: ...vailable at http://www.yeastgenome.org/, which is the source of our data. Table1 lists the data obtained from Saccharomyces Genome Database (SGD). This data includes structural and functional chromosome features (telomeres, genes, etc), database cross references, molecular function, cellular component, biological process, interactions, pathways, phenotypes and literature references.... In PAGE 3: ... We will now describe how we overcome the challenges of the data mapping. Tab-delimited files used for this study are listed in Table1 . Normalization of certain files (complex, interactions) was necessary as multi-valued entries were separated by / or | delimiters.... ..."

"... In PAGE 4: ... This connectivity between genes and functional classes is provided by GiGA. Table3 summarizes the results for the 20.5 hour time point, using two different networks, one for GeneOntology classes, and one for enzyme substrates, extracted from the SwissProt catalytic activity descriptors of yeast proteins.... In PAGE 9: ...entral biological processes detected by DeRisi et al. (1997) and by iGA (see Table 1 and 2). N, number of genes in each subgraph. Table3 : Graph-based iterative Group Analysis of gene expression during the yeast diauxic shift. (Continued) Page 9 of 10 (page number not for citation purposes)... ..."

"... In PAGE 4: ...rrors have no observable effect (i.e., do not manifest). This result is consistent with previous studies [20] and can be partially attributed to an inherent redundancy in the code. a2 Table3 shows that fail silence violations are rare for group (0.5%) but not absent, as the lack of application- level communication would suggest.... In PAGE 4: ... In this set of experiments, after the initial multicast group is formed, random bit errors are injected peri- odically in the allocated regions of the heap memory of a target process. The heap-injection results of Table3 show no occurrence of fail silence violations for the group benchmark. For the other two benchmarks, the presence of application-level communica- tion makes fail silence violations account for 5% of the mani- fested errors, a number similar to that for the text-error injections (shown in the same table).... In PAGE 5: ...Table3 . Text- and heap-error injection in all Ensemble subsystems.... In PAGE 6: ... Initially, we consider only errors whose propagation is limited to the injected process; error propagation involving the network is considered separately in a4 8. Figure 4 shows the error latency distribution for the text-error injections of Table3 . Four levels of latency are considered: (1) Same Location, the injected process crashes at the time of exe- cuting the injected instruction; (2) Same Function, the injected process crashes while executing code from the injected function (excluding the previous case); (3) Different Function, the in- jected process crashes while executing a function that is not the injected one; (4) Invalid Location, the injected process crashes due to an attempt to access an invalid memory location (e.... In PAGE 6: ...igure 4. Text-error latency. To refine our analysis, we show in Figure 5 the probabili- ties for a text error occurring in one subsystem to propagate and manifest as a crash in another subsystem.7 The data are from the experiments corresponding to Table3 . The left node on each graph refers to the faulted subsystem (i.... ..."

"... In PAGE 16: ...Table1 in the absence of failures. There are three main points to be noticed in these tables.... In PAGE 26: ...1= = 15:0 1= = 50:0 1= = 100:0 1= = 400:0 Amoeba sequencer 151 151 152 152 non-sequencer 152 152 152 152 PositiveACK sequencer 12 8 6 4 non-sequencer 18 9 8 5 Train trainmaster 44 23 16 8 non-trainmaster 25 12 10 5 Isis ABCAST sequencer 410 417 412 412 non-sequencer 285 283 278 278 Table1 0: Maximum bu er size: group size 5; no failures Group Size: 3 1= = 15:0 1= = 50:0 1= = 100:0 1= = 400:0 Amoeba sequencer 46 62 62 63 non-sequencer 1090 63 63 63 PositiveACK sequencer 14 8 6 4 non-sequencer 17 9 7 5 Train trainmaster 31 16 14 8 non-trainmaster 18 9 8 4 Isis ABCAST sequencer 190 135 131 144 non-sequencer 192 187 189 208 Table 11: Maximum bu er size: group size 3; one failure 6 Average Number of Messages per Broadcast 6.1 The PA Protocol The average number of messages sent per broadcast as a function of mean update interarrival time is shown in Figure 50 in the absence of any failures, and in Figure 51 in the presence of one commu- nication failure per broadcast.... In PAGE 26: ...1= = 15:0 1= = 50:0 1= = 100:0 1= = 400:0 Amoeba sequencer 151 151 152 152 non-sequencer 152 152 152 152 PositiveACK sequencer 12 8 6 4 non-sequencer 18 9 8 5 Train trainmaster 44 23 16 8 non-trainmaster 25 12 10 5 Isis ABCAST sequencer 410 417 412 412 non-sequencer 285 283 278 278 Table 10: Maximum bu er size: group size 5; no failures Group Size: 3 1= = 15:0 1= = 50:0 1= = 100:0 1= = 400:0 Amoeba sequencer 46 62 62 63 non-sequencer 1090 63 63 63 PositiveACK sequencer 14 8 6 4 non-sequencer 17 9 7 5 Train trainmaster 31 16 14 8 non-trainmaster 18 9 8 4 Isis ABCAST sequencer 190 135 131 144 non-sequencer 192 187 189 208 Table1 1: Maximum bu er size: group size 3; one failure 6 Average Number of Messages per Broadcast 6.1 The PA Protocol The average number of messages sent per broadcast as a function of mean update interarrival time is shown in Figure 50 in the absence of any failures, and in Figure 51 in the presence of one commu- nication failure per broadcast.... In PAGE 27: ...1= = 15:0 1= = 50:0 1= = 100:0 1= = 400:0 Amoeba sequencer 152 152 154 152 non-sequencer 165 152 154 152 PositiveACK sequencer 18 9 7 4 non-sequencer 20 11 8 6 Train trainmaster 69 29 19 11 non-trainmaster 42 17 12 6 Isis ABCAST sequencer 412 434 416 420 non-sequencer 292 385 279 278 Table1 2: Maximum bu er size: group size 5; one failure the average number of messages sent per broadcast increases linearly with the group size, and is independent of the mean update interarrival time. The increase with the group size is explained by the fact that the number of messages sent from the sequencer to group members increase with group size.... In PAGE 31: ...69 27.69 Table1 3: Percentage of the number of messages processed (Group size = 3) Group Size: 5 No failure Failure 1= = 15:0 1= = 300:0 1= = 15:0 1= = 300:0 Amoeba sequencer 51.09 51.... In PAGE 31: ...63 14.92 Table1 4: Percentage of the number of messages processed (Group size = 5) The following observations can be made. In the sequencer-based protocols, the percentage of the number of messages processed by the sequencer far exceeds those processed by non-sequencers, while in the train protocol all group members process approximately the same number of messages.... ..."

"... In PAGE 8: ... The most reliable class assignments correspond to a cluster containing mostly tissues of one tumor class, with a surrounding neighborhood of nodes also containing mostly tissues of the same tumor class. Table2 lists the class prediction assignments, grouped according to each of the 14 tumor classes. Each column Table 1 Tissue predictions based on original data Tissue Preference Hits Total Tm-BRa 0.... In PAGE 14: ... The highest accuracies were found for the leukemia training set, with 26 of the 38 data vectors having correct assignments. In general, however, the predic- tion accuracies were 50% poorer than those reported in Table2 . Establishing the reasons for these differences is difficult; however, the limited number of genes in each test dataset most likely contributes to the poorer classification accuracies.... ..."