The identification of compounds for dissecting biological functions and the development of novel drug molecules are central tasks that often require screening campaigns. However, the required architecture is cost- and time-intensive. Herein we descri...
PURPOSE: This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR).
Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in ad...
This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 b...
IEEE/ACM transactions on computational biology and bioinformatics
Jun 25, 2018
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this article, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to ac...
In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a cha...
IEEE/ACM transactions on computational biology and bioinformatics
May 29, 2018
Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of drug discovery. A major contributing factor to this phenomenon is the failure of traditional in vitro...
Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to d...
Defining subtypes of complex diseases such as cancer and stratifying patient groups with the same disease but different subtypes for targeted treatments is important for personalized and precision medicine. Approaches that incorporate multi-omic data...
INTRODUCTION: The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric ...
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