AIMC Topic: Machine Learning

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Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.

Neuroinformatics
Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise ...

Adolescent Depression Detection Model Based on Multimodal Data of Interview Audio and Text.

International journal of neural systems
Depression is a common mental disease that has a tendency to develop at a younger age. Early detection of depression with psychological intervention may effectively prevent youth suicide. The establishment of the computer-aided model may be efficient...

Leveraging Theory for Enhanced Machine Learning.

ACS macro letters
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is...

Evaluating molecular representations in machine learning models for drug response prediction and interpretability.

Journal of integrative bioinformatics
Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in r...

Research on imbalance machine learning methods for MRWI soft tissue sarcoma data.

BMC medical imaging
BACKGROUND: Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma ...

Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity.

eLife
Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituent...

A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping.

Physiological measurement
A segmentation method for pre-impact fall detection data is investigated. Specifically, it studies how to partition data segments that are important for classification from continuous inertial sensor data for pre-impact fall detection.In this study, ...

Using natural language processing and machine learning to replace human content coders.

Psychological methods
Content analysis is a common and flexible technique to quantify and make sense of qualitative data in psychological research. However, the practical implementation of content analysis is extremely labor-intensive and subject to human coder errors. Ap...

Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose.

Cells & development
Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time...

De novo prediction of RNA-protein interactions with graph neural networks.

RNA (New York, N.Y.)
RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein ...