AIMC Topic: Neoplasms

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HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Scientific reports
Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have bee...

Heuristic filter feature selection methods for medical datasets.

Genomics
Gene selection is the process of selecting the optimal feature subset in an arbitrary dataset. The significance of gene selection is in high dimensional datasets in which the number of samples and features are low and high respectively. The major goa...

Cancer classification and pathway discovery using non-negative matrix factorization.

Journal of biomedical informatics
OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.

Variable Selection for Nonparametric Learning with Power Series Kernels.

Neural computation
In this letter, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, and (2) approximate the estim...

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

Genome research
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...

TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

Medical image analysis
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase ima...

Machine learning approaches for risk assessment of peripherally inserted Central catheter-related vein thrombosis in hospitalized patients with cancer.

International journal of medical informatics
OBJECTIVE: The aim of this study was to conduct an effective assessment of peripherally inserted central venous catheter (PICC)-related thrombosis based on machine learning (ML) techniques considering genotype.

A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements.

Sensors (Basel, Switzerland)
This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanica...