AIMC Topic: Neoplasms

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Learning with multiple pairwise kernels for drug bioactivity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel...

New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome.

Immunology
At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T-cell immunity, there is a need for robust tools to enable accurate predictions ...

Arash: A social robot buddy to support children with cancer in a hospital environment.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
This article presents the thorough design procedure, specifications, and performance of a mobile social robot friend Arash for educational and therapeutic involvement of children with cancer based on their interests and needs. Our research focuses on...

Implementation and evaluation of a sterile compounding robot in a satellite oncology pharmacy.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
PURPOSE: The purpose of this study was to quantify the impact of robotic technology on efficiency, accuracy, and cost in a satellite oncology pharmacy.

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different...

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

Bioinformatics (Oxford, England)
MOTIVATION: While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a tim...

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cell
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovati...

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.

Cell reports
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 ...