Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization.

Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep Matrix Factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF's ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding (RT) procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open source package available at https://github.com/tomwhoooo/rtdmf).

Comparative Study on Volatile Oils among Bupleuri radix Species and Habitats: Yields, Chemical Characterization and Antipyretic Activities.

Volatile oils from several Bupleuri radix (BR) are reported as potential sources of drugs. To provide evidence for the application of BR, the volatile oils from 19 batches of different species and habitats of BR including Bupleurum chinese DC. (BCD), Bupleurum scorzonerifolium Willd. (BSW), Bupleurum bicaule Helm (BBH), Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y.Li (BMS), Bupleurum marginatum Wall.ex DC. (BMW) and Bupleurum falcatum L. (BFL) were investigated. The composition of BR volatile oils was determined by GC/MS. Samples were clustered by hierarchical cluster analysis (HCA). Fever was induced by Lipopolysaccharide (LPS), and antipyretic activities of BR volatile oils were evaluated with Chaihu injection (CI) as the positive control. The yields of volatile oils were among 360-5320 ppm. A total of 229 components were identified by GC/MS. Samples could be divided into 4 clusters by HCA. 4 representative samples, one for each cluster, were selected to further compare their antipyretic activities. For the highest content of volatile oil (5320 ppm) and the best activity, BSW has great potential for utilization.