AIMC Topic: Machine Learning

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The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

Implementation science : IS
BACKGROUND: Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evid...

A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction.

Scientific reports
During cellular reprogramming, the mesenchymal-to-epithelial transition is accompanied by changes in morphology, which occur prior to iPSC colony formation. The current approach for detecting morphological changes associated with reprogramming purely...

High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision.

Radiology
Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and t...

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

Molecules (Basel, Switzerland)
With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological...

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model.

PloS one
This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting inte...

Error-Correcting Factorization.

IEEE transactions on pattern analysis and machine intelligence
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupl...

Root traits of European Vicia faba cultivars-Using machine learning to explore adaptations to agroclimatic conditions.

Plant, cell & environment
Faba bean (Vicia faba L.) is an important source of protein, but breeding for increased yield stability and stress tolerance is hampered by the scarcity of phenotyping information. Because comparisons of cultivars adapted to different agroclimatic zo...

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentiall...

Semantic biclustering for finding local, interpretable and predictive expression patterns.

BMC genomics
BACKGROUND: One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of under...

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.

Scientific reports
Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a ...