AIMC Topic: Machine Learning

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WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants.

Genome biology
The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks lim...

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain.

NeuroImage. Clinical
Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state ...

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems.

PloS one
The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some...

Computational approaches and machine learning for individual-level treatment predictions.

Psychopharmacology
RATIONALE: The impact of neuroscience-based approaches for psychiatry on pragmatic clinical decision-making has been limited. Although neuroscience has provided insights into basic mechanisms of neural function, these insights have not improved the a...

Pharmacophore features for machine learning in pharmaceutical virtual screening.

Molecular diversity
Methods of three-dimensional molecular alignment generally treat all pharmacophore features equally when superimposing. However, some pharmacophore features can be more important in a specific system. In this work, we derived the overlap volume of ph...

Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell ...

A neural network architecture for learning word-referent associations in multiple contexts.

Neural networks : the official journal of the International Neural Network Society
This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence collected from the literature of Psycholinguistics and Neurolinguistics. T...

Robust manifold broad learning system for large-scale noisy chaotic time series prediction: A perturbation perspective.

Neural networks : the official journal of the International Neural Network Society
Noises and outliers commonly exist in dynamical systems because of sensor disturbations or extreme dynamics. Thus, the robustness and generalization capacity are of vital importance for system modeling. In this paper, the robust manifold broad learni...

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Environmental pollution (Barking, Essex : 1987)
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather R...

Biotechnology, Big Data and Artificial Intelligence.

Biotechnology journal
Developments in biotechnology are increasingly dependent on the extensive use of big data, generated by modern high-throughput instrumentation technologies, and stored in thousands of databases, public and private. Future developments in this area de...