AIMC Topic: Machine Learning

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Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting.

The Plant journal : for cell and molecular biology
Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as lea...

Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

Clinical imaging
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.

Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning.

Surgical oncology
Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor overall survival (OS) of patients. OS prediction of GBM patients provides useful information for surgical and treatment planning. Radiomics research attempts at predicting ...

Universal method for robust detection of circadian state from gene expression.

Proceedings of the National Academy of Sciences of the United States of America
Circadian clocks play a key role in regulating a vast array of biological processes, with significant implications for human health. Accurate assessment of physiological time using transcriptional biomarkers found in human blood can significantly imp...

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.

Journal of chemical information and modeling
The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the com...

Supporting biomedical ontology evolution by identifying outdated concepts and the required type of change.

Journal of biomedical informatics
The consistent evolution of ontologies is a major challenge for systems using semantically enriched data, for example, for annotating, indexing, or reasoning. The biomedical domain is a typical example where ontologies, expressed with different forma...

Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication.

Journal of chemical information and modeling
Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory...

Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

Acta tropica
The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a major public health challenge. This study compares several approaches to model the spatial distribution ...

Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Gastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our objective was to determine the optimal machine learning technique to predict gastrostomy.

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.

Breast cancer research and treatment
PURPOSE: This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.