Skinner Alston (heavenocean06)

One of the most difficult challenges in precision medicine is determining the best treatment strategy for each patient based on personal information. Since drug response prediction in vitro is extremely expensive, time-consuming, and virtually impossible, and because there are so many cell lines and drug data, computational methods are needed. MinDrug is a method for predicting anti-cancer drug response which try to identify the best subset of drugs that are the most similar to other drugs. MinDrug predicts the anti-cancer drug response on a new cell line using information from drugs in this subset and their connections to other drugs. MinDrug employs a heuristic star algorithm to identify an optimal subset of drugs and a regression technique known as Elastic-Net approaches to predict anti-cancer drug response in a new cell line. To test MinDrug, we use both statistical and biological methods to assess the selected drugs. MinDrug is also compared to four state-of-the-art approaches using various k-fold cross-validations on two large public datasets GDSC and CCLE. MinDrug outperforms the other approaches in terms of precision, robustness, and speed. Furthermore, we compare the evaluation results of all the approaches with an external dataset with a statistical distribution that is not exactly the same as the training data. The results show that MinDrug continues to outperform the other approaches. MinDrug's source code can be found at https//github.com/yassaee/MinDrug. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online.Several microfluidic-based methods for Caenorhabditis elegans imaging have recently been introduced. Existing methods either permit imaging across multiple larval stages without maintaining a stable worm orientation, or allow for very good immobilization but are only suitable for shorter experiments. Here, we present a novel microfluidic imaging method that allows parallel live-imaging across multiple larval stages, while maintaining worm orientation and identity over time. This is achieved through an array of microfluidic trap channels carefully tuned to maintain worms in a stable orientation, while allowing growth and molting to occur. Immobilization is supported by an active hydraulic valve, which presses worms onto the cover glass during image acquisition only. In this way, excellent quality images can be acquired with minimal impact on worm viability or developmental timing. The capabilities of the devices are demonstrated by observing the hypodermal seam and P-cell divisions and, for the first time, the entire process of vulval development from induction to the end of morphogenesis. selleck compound Moreover, we demonstrate feasibility of on-chip RNAi by perturbing basement membrane breaching during anchor cell invasion. MomentClosure.jl is a Julia package providing automated derivation of the time-evolution equations of the moments of molecule numbers for virtually any chemical reaction network using a wide range of moment closure approximations. It extends the capabilities of modelling stochastic biochemical systems in Julia and can be particularly useful when exact analytic solutions of the chemical master equation are unavailable and when Monte Carlo simulations are computationally expensive. MomentClosure.jl is freely accessible under the MIT license. Source code and documentation are available at https//github.com/augustinas1/MomentClosure.jl. MomentClosure.jl is freely accessible under the MIT license. Source code and documentation are available at https//github.com/augustinas1/MomentClosure.jl. Gene functional enrichment analysis represents one of the most popular bioinformatics methods for annotating the pathways and function categories of a given gene list. Current algorithms for enrichment computation such as Fisher's exact test and hypergeometric test totall