AIMC Topic: Neoplasms

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Optimal dynamic regimens with artificial intelligence: The case of temozolomide.

PloS one
We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et...

Drug Selection via Joint Push and Learning to Rank.

IEEE/ACM transactions on computational biology and bioinformatics
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...

Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering.

IEEE/ACM transactions on computational biology and bioinformatics
Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, diff...

Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.

Journal of biomedical informatics
Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting ...

Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification.

Scientific reports
Tumor classification is crucial to the clinical diagnosis and proper treatment of cancers. In recent years, sparse representation-based classifier (SRC) has been proposed for tumor classification. The employed dictionary plays an important role in sp...

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

Analytical biochemistry
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. I...

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

Medical physics
PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (r...

Using the MEDiPORT humanoid robot to reduce procedural pain and distress in children with cancer: A pilot randomized controlled trial.

Pediatric blood & cancer
BACKGROUND: Subcutaneous port needle insertions are painful and distressing for children with cancer. The interactive MEDiPORT robot has been programmed to implement psychological strategies to decrease pain and distress during this procedure. This s...

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.

Scientific reports
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...

Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images.

IEEE/ACM transactions on computational biology and bioinformatics
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...