BACKGROUND: As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, ...
The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of gene...
BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression da...
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We wil...
Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers. Experimental techniques in early time are quite popular in detecting oncogenes. However, their def...
BACKGROUND: Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn't allow sufficient training of ML classifiers that could be used f...
We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptio...
OBJECTIVE: In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources an...
Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to s...
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