AIMC Topic: Algorithms

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Statistical learning approaches in the genetic epidemiology of complex diseases.

Human genetics
In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic varia...

Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Artificial visual attention has been an active research area for over two decades. Especially, the concept of saliency has been implemented in many different ways. Early approaches aimed at closely modeling saliency processing with concepts from biol...

Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Data augmentation is a widely used technique for enhancing the generalization ability of deep neural networks for skeleton-based human action recognition (HAR) tasks. Most existing data augmentation methods generate new samples by means of handcrafte...

A review of automated sleep stage scoring based on physiological signals for the new millennia.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiologic...

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods.

Molecular omics
The zinc (Zn) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in pro...

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.

International journal of molecular sciences
Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNA...

Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.

International journal of molecular sciences
Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identif...

Emotion classification using a CNN_LSTM-based model for smooth emotional synchronization of the humanoid robot REN-XIN.

PloS one
In this paper, we propose an Emotional Trigger System to impart an automatic emotion expression ability within the humanoid robot REN-XIN, in which the Emotional Trigger is an emotion classification model trained from our proposed Word Mover's Distan...

Potential Role of Machine Learning in Oncology.

The journal of contemporary dental practice
Machine learning (ML) is the ability of computers to learn from data autonomously. It is a core branch of artificial intelligence (AI), which is defined as the ability of a machine to replicate the intellectual processes of humans independently. The ...