AIMC Topic: Machine Learning

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Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease.

International journal of neural systems
The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both ...

Machine learning reveals chronic graft--host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.

Haematologica
The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft--host disease. Chronic graft--host disease is classified by an...

Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection.

Neural networks : the official journal of the International Neural Network Society
As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited...

Cost-sensitive multi-label learning with positive and negative label pairwise correlations.

Neural networks : the official journal of the International Neural Network Society
Multi-label learning is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning. However, it may suffer the class-imbalance issue especially when ...

Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness.

Accident; analysis and prevention
Monitoring car drivers for drowsiness is crucial but challenging. The high inter-individual variability observed in measurements raises questions about the accuracy of the drowsiness detection process. In this study, we sought to enhance the performa...

Enhanced Computational Model for Gravitational Search Optimized Echo State Neural Networks Based Oral Cancer Detection.

Journal of medical systems
The Clinical Oncology of American Society report in 2016 predicted deaths are increased upto 9570 due to oral cancer. This cancer occurs due to abnormal tissue growth in the oral cavity. This cancer has limited symptoms, so, it has been difficult to ...

Increasing workflow development speed and reproducibility with Vectools.

F1000Research
Despite advances in bioinformatics, custom scripts remain a source of difficulty, slowing workflow development and hampering reproducibility. Here, we introduce Vectools, a command-line tool-suite to reduce reliance on custom scripts and improve repr...

An accessible and efficient autism screening method for behavioural data and predictive analyses.

Health informatics journal
Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic p...

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery.

Molecular pharmaceutics
Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this...