AIMC Topic: Machine Learning

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Can machine learning predict resecability of a peritoneal carcinomatosis?

Surgical oncology
BACKGROUND: Approximately 20% of initially eligible patients in a HIPEC procedure eventually underwent a simple surgical exploration. These procedures are called 'open & close' (O & C) representing up to 48% of surgery. The objective of this study wa...

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters.

Journal of pharmacological sciences
Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to ...

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

Magnetic resonance imaging
PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the ...

Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.

Molecular pharmaceutics
Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have c...

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

JAMA network open
IMPORTANCE: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission.

Machine-learned target volume delineation of F-FDG PET images after one cycle of induction chemotherapy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTV. Automatic segmentation algorithms applied to F-FDG PET (PET-AS) imaging hav...

Classification of Patients with Coronary Microvascular Dysfunction.

IEEE/ACM transactions on computational biology and bioinformatics
While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 Natio...

Statistical learning approaches in the genetic epidemiology of complex diseases.

Human genetics
In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic varia...

Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Artificial visual attention has been an active research area for over two decades. Especially, the concept of saliency has been implemented in many different ways. Early approaches aimed at closely modeling saliency processing with concepts from biol...

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering.

Biotechnology journal
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have beco...