Heterocyclic compounds offer a high degree of structural diversity and have proven to be broadly and economically useful as therapeutic agents. Comprehensive research on diverse therapeutic ...potentials of heterocycles compounds has confirmed their immense significance in the pathophysiology of diseases. Heterocyclic pyrimidine nucleus, which is an essential base component of the genetic material of deoxyribonucleic acid, demonstrated various biological activities. The present review article aims to review the work reported on therapeutic potentials of pyrimidine scaffolds which are valuable for medical applications during new generation.
An Interview with Bradley Efron Narasimhan, Balasubramanian
International statistical review,
December 2020, 2020-12-00, 20201201, Letnik:
88, Številka:
S1
Journal Article
The utility of oxazole as intermediates for the synthesis of new chemical entities in medicinal chemistry have been increased in the past few years. Oxazole is an important heterocyclic nucleus ...having a wide spectrum of biological activities which drew the attention of researchers round the globe to synthesize various oxazole derivatives and screen them for their various biological activities. The present review article aims to review the work reported on therapeutic potentials of oxazole scaffolds which are valuable for medical applications during new millennium.
CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to formulate convex optimization ...problems in a natural mathematical syntax rather than the restrictive form required by most solvers. The user specifies an objective and set of constraints by combining constants, variables, and parameters using a library of functions with known mathematical properties. CVXR then applies signed disciplined convex programming (DCP) to verify the problem's convexity. Once verified, the problem is converted into standard conic form using graph implementations and passed to a cone solver such as ECOS or SCS. We demonstrate CVXR's modeling framework with several applications.
There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known ...follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD). The use of SGD enables rapid convergence and application to large datasets that do not fit in memory. The model is flexible, so that the baseline hazard rate and the effect of the input data on hazard probability can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrate the performance of the model on both simulated and real data and compare it to existing models Cox-nnet and Deepsurv.
The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the ...elastic net regularization path for ordinary least squares regression, logistic regression and multinomial logistic regression, while Simon, Friedman, Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data. We further extend the reach of the elastic net-regularized regression to all generalized linear model families, Cox models with (start, stop data and strata, and a simplified version of the relaxed lasso. We also discuss convenient utility functions for measuring the performance of these fitted models.
The standard intervals, for example,
for nominal 95% two-sided coverage, are familiar and easy to use, but can be of dubious accuracy in regular practice. Bootstrap confidence intervals offer an ...order of magnitude improvement-from first order to second order accuracy. This article introduces a new set of algorithms that automate the construction of bootstrap intervals, substituting computer power for the need to individually program particular applications. The algorithms are described in terms of the underlying theory that motivates them, along with examples of their application. They are implemented in the R package bcaboot.
Supplementary materials
for this article are available online.
Cancer is one of the most serious medical problem and second leading cause of death in the world, characterized by a deregulation of the cell cycle which mainly results in a progressive loss of ...cellular differentiation and uncontrolled cellular growth. The benzimidazole is a heterocyclic moiety found in extensive number of natural and biological active molecules. Benzimidazole derivatives might be considered as auxiliary isosters of nucleotides having attached heterocyclic cores in their structures, cooperate effortlessly with biopolymers and have potential action for chemotherapeutic applications. Benzimidazole and its derivatives displayed a wide range of biological activity because of its structural similarity with the naturally occurring nucleotides. Benzimidazole has established huge alertness in current time and is extremely significant heterocyclic pharmacophore in recent drug innovation and medicinal chemistry. The present review summarizes the chemistry of various substituted benzimidazole derivatives with their antiproliferative significance towards the various cancer cell lines such as HCT116, MCF7, HeLa, HepG2, A549 and A431.
Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservables) Θ1, Θ2, ..., ΘN, and each Θi produces an independent observation Xi from pi (Xi | Θi). The marginal density ...fi (Xi) is a convolution of the prior g and pi. The Bayes deconvolution problem is one of recovering g from the data. Although estimation of g - so called g-modeling - is difficult, the results are more encouraging if the prior g is restricted to lie within a parametric family of distributions. We present a deconvolution approach where g is restricted to be in a parametric exponential family, along with an R package deconvolveR designed for the purpose.
High‐dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high‐throughput screening, electronic health ...records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left‐truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left‐truncated and right‐censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high‐dimensional, real‐world clinico‐genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.
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