Relapse prediction in childhood acute lymphoblastic leukemia by time-series gene expression profiling

Ph.DDOCTOR OF PHILOSOPH

Chemically induced graphene to diamond transition: a DFT study

The conversion of graphene into diamond is a new way for preparing ultrathin diamond film without pressure. Herein, we investigated the transformation mechanism of surface-hydrogenated bilayer graphene (SHBG) into surface-hydrogenated single-layer diamond (SHSLD) crystal, inserting fifteen kinds of single metal atoms without any pressure, by using the systematical first-principles calculations. Compared with the configuration without metal atom, SHBG can be transformed into SHSLD spontaneously in thermodynamics under the action of single metal atom, and its formation energy can even decrease from 0.82 eV to -5.79 eV under the action of Hf atom. According to our results, the outer electron orbits and atomic radius of metal atom are two important factors that affect the conversion. For the phase transition to occur, the metal atom needs to have enough empty d orbitals, and the radius of the metal atom is in the range of 0.136-0.159 nm. Through further analysis, we find that the p orbitals of carbon atoms and d orbital of metal atom in SHBG will be strongly hybridized, thereby promoting the conversion. The results supply important significance to experimentally prepare diamond without pressure through hydrogenated graphene

Consistency, comprehensiveness, and compatibility of pathway databases

<p>Abstract</p> <p>Background</p> <p>It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need.</p> <p>Description</p> <p>Our results show a low level of consistency, comprehensiveness and compatibility among three popular pathway databases (KEGG, Ingenuity and Wikipathways). The level of consistency for genes in similar pathways across databases ranges from 0% to 88%. The corresponding level of consistency for interacting genes pairs is 0%-61%. These three original sources can be assumed to be reliable in the sense that the interacting gene pairs reported in them are correct because they are curated. However, the lack of concordance between these databases suggests each source has missed out many genes and interacting gene pairs.</p> <p>Conclusions</p> <p>Researchers will hence find it challenging to obtain consistent pathway information out of these diverse data sources. It is therefore critical to enable them to access these sources via a consistent, comprehensive and unified pathway API. We accumulated sufficient data to create such an aggregated resource with the convenience of an API to access its information. This unified resource can be accessed at <url>http://www.pathwayapi.com</url>.</p

Finding consistent disease subnetworks across microarray datasets

<p>Abstract</p> <p>Background</p> <p>While contemporary methods of microarray analysis are excellent tools for studying individual microarray datasets, they have a tendency to produce different results from different datasets of the same disease. We aim to solve this reproducibility problem by introducing a technique (SNet). SNet provides both quantitative and descriptive analysis of microarray datasets by identifying specific connected portions of pathways that are significant. We term such portions within pathways as “subnetworks”.</p> <p>Results</p> <p>We tested SNet on independent datasets of several diseases, including childhood ALL, DMD and lung cancer. For each of these diseases, we obtained two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produced almost the same list of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks was between 51.18% to 93.01%. In contrast, when the same pairs of microarray datasets were analysed using GSEA, t-test and SAM, this percentage fell between 2.38% to 28.90% for GSEA, 49.60% tp 73.01% for t-test, and 49.96% to 81.25% for SAM. Furthermore, the genes selected using these existing methods did not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway actually affected by the disease.</p> <p>Conclusions</p> <p>These results clearly demonstrate that our technique generates significant subnetworks and genes that are more consistent and reproducible across datasets compared to the other popular methods available (GSEA, t-test and SAM). The large size of subnetworks which we generate indicates that they are generally more biologically significant (less likely to be spurious). In addition, we have chosen two sample subnetworks and validated them with references from biological literature. This shows that our algorithm is capable of generating descriptive biologically conclusions.</p

Genome-wide analysis of regions similar to promoters of histone genes

Background: The purpose of this study is to: i) develop a computational model of promoters of human histone-encoding genes (shortly histone genes), an important class of genes that participate in various critical cellular processes, ii) use the model so developed to identify regions across the human genome that have similar structure as promoters of histone genes; such regions could represent potential genomic regulatory regions, e.g. promoters, of genes that may be coregulated with histone genes, and iii/ identify in this way genes that have high likelihood of being coregulated with the histone genes. Results: We successfully developed a histone promoter model using a comprehensive collection of histone genes. Based on leave-one-out cross-validation test, the model produced good prediction accuracy (94.1% sensitivity, 92.6% specificity, and 92.8% positive predictive value). We used this model to predict across the genome a number of genes that shared similar promoter structures with the histone gene promoters. We thus hypothesize that these predicted genes could be coregulated with histone genes. This hypothesis matches well with the available gene expression, gene ontology, and pathways data. Jointly with promoters of the above-mentioned genes, we found a large number of intergenic regions with similar structure as histone promoters. Conclusions: This study represents one of the most comprehensive computational analyses conducted thus far on a genome-wide scale of promoters of human histone genes. Our analysis suggests a number of other human genes that share a high similarity of promoter structure with the histone genes and thus are highly likely to be coregulated, and consequently coexpressed, with the histone genes. We also found that there are a large number of intergenic regions across the genome with their structures similar to promoters of histone genes. These regions may be promoters of yet unidentified genes, or may represent remote control regions that participate in regulation of histone and histone-coregulated gene transcription initiation. While these hypotheses still remain to be verified, we believe that these form a useful resource for researchers to further explore regulation of human histone genes and human genome. It is worthwhile to note that the regulatory regions of the human genome remain largely un-annotated even today and this study is an attempt to supplement our understanding of histone regulatory regions.Statistic

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

10.1021/pr1010845Journal of Proteome Research1052261-2272JPRO

Enabling more sophisticated gene expression analysis for understanding diseases and optimizing treatments

We survey the progress in the analysis of gene expression data for the purposes of disease subtype diagnosis, new subtype discovery, and understanding of diseases and treatment responses. We find existing works fall short on several issues: these works provide little information on the interplay between selected genes; the collection of pathways that can be used, evaluated, and ranked against the observed expression data is limited; and a comprehensive set of rules for reasoning about relevant molecular events has not been compiled and formalized. We thus envision an advanced integrated framework, and are developing a system based on it, to provide biologically inspired solutions. It comprises: (i) automated analysis and extraction of information from biomedical texts; (ii) targeted construction of known pathways; and (iii) direct hypothesis generation based on logical reasoning on, and tests for, consistencies and inconsistencies of observed data against known pathways. 1