Pathway-Based Genomics Prediction using Generalized Elastic Net.

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach

The Number Of Titrated Microrna Species Dictates Cerna Regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were coregulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor-and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program (http://www.lincsproject.org/) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response

Penerapan Biaya Standar dalam Pengendalian Biaya Produksi pada PT. Pertani (Persero) Cabang Sulawesi Utara

Penerapan biaya standar dapat mendorong para eksekutif dan penyelia Perusahaan untuk meningkatkan efisiensi dan efektifitas proses produksi untuk mencapai standar yang telah ditetapkan. Penetapan biaya standar dapat memberikan pedoman untuk mengetahui biaya yang seharusnya terjadi dalam proses produksi. Adapun tujuan dari penelitian ini adalah untuk mengetahui besar biaya standar yang telah diterapkan dan bagaimana penerapan biaya standar pada PT. Pertani. Alat analisis data yang digunakan adalah analisa metode deskriptif analisa dengan pendekatan kuantitatif. Dari hasil analisa tersebut Perusahaan sudah menerapkan biaya standar. Pada tahun 2011 besar biaya stadar yang telah diterapkan adalah sebesar Rp. 6.569.771.800 dengn biaya produksi yang terjadi Rp. 5.563.445.750 dengan demikian Perusahaan mengalami efisiensi sebesar Rp. 1.006.326.050 dengan presentase 18,088 %. Oleh karena itu sebaiknya Perusahaan mempertahankan biaya produksi yang telah disepakati dengan para pemasok sehingga efisiensi dapat tetap terjadi dikarenakan lebih murah dari standar harga yang telah ditetapkan oleh Perusahaan

Further clinical and molecular delineation of the 15q24 microdeletion syndrome

Background Chromosome 15q24 microdeletion syndrome is a rare genomic disorder characterised by intellectual disability, growth retardation, unusual facial morphology and other anomalies. To date, 20 patients have been reported; 18 have had detailed breakpoint analysis. Aim To further delineate the features of the 15q24 microdeletion syndrome, the clinical and molecular characterisation of fifteen patients with deletions in the 15q24 region was performed, nearly doubling the number of reported patients. Methods Breakpoints were characterised using a custom, high-density array comparative hybridisation platform, and detailed phenotype information was collected for each patient. Results Nine distinct deletions with different breakpoints ranging in size from 266 kb to 3.75 Mb were identified. The majority of breakpoints lie within segmental duplication (SD) blocks. Low sequence identity and large intervals of unique sequence between SD blocks likely contribute to the rarity of 15q24 deletions, which occur 8-10 times less frequently than 1q21 or 15q13 microdeletions in our series. Two small, atypical deletions were identified within the region that help delineate the critical region for the core phenotype in the 15q24 microdeletion syndrome. Conclusion The molecular characterisation of these patients suggests that the core cognitive features of the 15q24 microdeletion syndrome, including developmental delays and severe speech problems, are largely due to deletion of genes in a 1.1-Mb critical region. However, genes just distal to the critical region also play an important role in cognition and in the development of characteristic facial features associated with 15q24 deletions. Clearly, deletions in the 15q24 region are variable in size and extent. Knowledge of the breakpoints and size of deletion combined with the natural history and medical problems of our patients provide insights that will inform management guidelines. Based on common phenotypic features, all patients with 15q24 microdeletions should receive a thorough neurodevelopmental evaluation, physical, occupational and speech therapies, and regular audiologic and ophthalmologic screenin

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Three-Dimensional Neurophenotyping of Adult Zebrafish Behavior

The use of adult zebrafish (Danio rerio) in neurobehavioral research is rapidly expanding. The present large-scale study applied the newest video-tracking and data-mining technologies to further examine zebrafish anxiety-like phenotypes. Here, we generated temporal and spatial three-dimensional (3D) reconstructions of zebrafish locomotion, globally assessed behavioral profiles evoked by several anxiogenic and anxiolytic manipulations, mapped individual endpoints to 3D reconstructions, and performed cluster analysis to reconfirm behavioral correlates of high- and low-anxiety states. The application of 3D swim path reconstructions consolidates behavioral data (while increasing data density) and provides a novel way to examine and represent zebrafish behavior. It also enables rapid optimization of video tracking settings to improve quantification of automated parameters, and suggests that spatiotemporal organization of zebrafish swimming activity can be affected by various experimental manipulations in a manner predicted by their anxiolytic or anxiogenic nature. Our approach markedly enhances the power of zebrafish behavioral analyses, providing innovative framework for high-throughput 3D phenotyping of adult zebrafish behavior

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense