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Background: Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched. Methods: We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone. Results: The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner. Conclusions: We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood. [ABSTRACT FROM AUTHOR]

Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process.Results: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.Availability: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.Contact:dwkim@cau.ac.kr [ABSTRACT FROM AUTHOR]

We report on low-voltage inverters with two pentacene-based thin-film transistors (TFTs), one of which has been fabricated on a dielectric surface selected by micro-contact printing (μCP). When a self-assembled monolayer (SAM) of 7-octenyltrichlorosilane (7-OTS) was printed on AlOxdielectric oxide, the TFT with the μCP-SAM displayed a large threshold voltage (Vth) shift (3 V shift under −5 V operation), increased device current, and clearly changed pentacene surface morphology as compared to those of the other TFT without μCP-SAM. Those results are attributed to the 7-OTS-modified pentacene/AlOxinterface. Our inverter, composed of two TFTs with and without μCP-SAM, shows a high gain of ∼50 at a supply voltage (VDD) of −20 V. [ABSTRACT FROM AUTHOR]

The authors report on the high current density n-ZnO/p-Si heterojunction diode that has been fabricated by atomic layer deposition (ALD) of 70 nm thin ZnO on a p-Si substrate. While the diode was formed at four different ALD temperatures of 80, 100, 150 and 200 degC, the 100 degC processed diode showed an optimal behaviour of an on-off ratio over 3.3 x 103 and a high forward current density, [?]300 A cm[?]2 at 3 V. Although the highest film conductance appeared from the 200 degC deposited ZnO layer, nanometre thin SiOx was also revealed at the ZnO/p-Si interface; it might cause a high reverse leakage current level. Our high current density diode also demonstrates a fast switching performance without any reverse recovery delay, which is often observed at a usual Si p-n diode. [ABSTRACT FROM AUTHOR]