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Simple Summary: Cancer arises through the accumulation of somatic mutations in key biological pathways. This paper aims to develop a probabilistic approach to delineate the temporal order of mutations during cancer development based on mutation profile data from a cohort of patients. A unique feature of our method is that it incorporates intra-tumor heterogeneity (ITH) information, which refers to the heterogeneous cell populations within a tumor and characterizes the evolutionary history of the tumor. We showed that by integrating ITH, pathways, and functional annotation information, our method yielded high accuracy in inferring the temporal order of pathway mutations during carcinogenesis. The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method's ability to recover the temporal order of pathway mutations in several cancer types. [ABSTRACT FROM AUTHOR]

Deep learning is increasingly used in short-term load forecasting. However, deep learning models are difficult to train, and adjusting training hyper-parameters takes time and effort. Automated machine learning (AutoML) can reduce human participation in machine learning process and improve the efficiency of modelling while ensuring the accuracy of prediction. In this paper, we compare the usage of three AutoML approaches in short-term load forecasting. The experiments on a real-world dataset show that the predictive performance of AutoGluon outperforms that of AutoPytorch and Auto-Keras, according to three performance metrics: MAE, RMSE and MAPE. AutoPytorch and Auto-Keras have similar performance and are not easy to compare.

Motivation: Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency., Results: We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets., Availability and Implementation: MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Fluorinated, phenylethynyl-substituted heterocycles that possessed either an N -methylamino or N,N -dimethylamino group attached to heterocycles including pyridines, indoles, 1 H -indazoles, quinolines, and isoquinolines inhibited the proliferation of LS174T colon cancer cells in which the inhibition of cyclin D1 and induction of the cyclin-dependent kinase inhibitor-1 ( i.e ., p21 Wif1/Cip1 ) served as a readout for antineoplastic activity at a cellular level. On a molecular level, these agents, particularly 4-((2,6-difluorophenyl)ethynyl)- N -methylisoquinolin-1-amine and 4-((2,6-difluorophenyl)ethynyl)- N , N -dimethylisoquinolin-1-amine, bound and inhibited the catalytic subunit of methionine S-adenosyltransferase-2 (MAT2A)., Competing Interests: Conflict of Interest The authors declare no competing interest.

The objective of this research was to develop and evaluate high drug-loading ligand-modified nanomicelles to deliver a steroidal compound to the brain. YC1 (5α-cholestane-24-methylene-3β, 5α, 6β, 19-tetraol), with poor solubility and limited access to the brain, for the first time, has been proved to be an effective neuroprotective steroid by our previous studies. Based on the principle of 'like dissolves like', cholesterol, which shares the same steroidal parent nucleus with YC1, was selected to react with sodium alginate, producing amphiphilic sodium alginate- cholesterol derivatives (SACDs). To increase the grafting ratio and drug loading, cholesterol was converted to cholesteryl chloroformate, for the first time, before reacting with sodium alginate. Further, lactoferrin was conjugated on SACDs to provide lactoferrin-SACDs (Lf-SACD), which was established by immune electron microscopy (IEM) and self-assembled into brain-targeting nanomicelles. These nanomicelles were negatively charged and spherical in nature, with an average size of <200 nm. The YC1 drug loading was increased due to the cholesteryl inner cores of the nanomicelles, and the higher the grafting ratio was, the lower the critical micelle concentration (CMC) value of SACD, and the higher drug loading. The in vitro drug release, studied by bulk-equilibrium dialysis in 20 mL of 6% hydroxypropyl-β-cyclodextrin solution at 37°C, indicated a prolonged release profile. The YC1 concentration in mouse brain delivered by lactoferrin-modified nanomicelles was higher than in those delivered by non-modified nanomicelles and YC1 solution. The unique brain-targeting nanomicelle system may provide a promising carrier to deliver hydrophobic drugs across the blood-brain barrier for the treatment of brain diseases.