AIMC Topic: Machine Learning

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Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms.

Scientific data
Named entity recognition (NER) is a widely used text-mining and natural language processing (NLP) subtask. In recent years, deep learning methods have superseded traditional dictionary- and rule-based NER approaches. A high-quality dataset is essenti...

Rapid prediction of lab-grown tissue properties using deep learning.

Physical biology
The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of...

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.

PloS one
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains ...

Machine learning model to predict the width of maxillary central incisor from anthropological measurements.

Journal of prosthodontic research
PURPOSE: To improve smile esthetics, clinicians should comprehensively analyze the face and ensure that the sizes selected for the maxillary anterior teeth are compatible with the available anthropological measurements. The inter commissural (ICW), i...

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.

Journal of biomolecular structure & dynamics
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can...

An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.

Scientific reports
Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area...

Analyzing omics data by feature combinations based on kernel functions.

Journal of bioinformatics and computational biology
Defining meaningful feature (molecule) combinations can enhance the study of disease diagnosis and prognosis. However, feature combinations are complex and various in biosystems, and the existing methods examine the feature cooperation in a single, f...

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers.

Journal of the Royal Society, Interface
With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominant...

Classification of mathematical test questions using machine learning on datasets of learning management system questions.

PloS one
Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popula...