AIMC Topic: Machine Learning

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Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D.

Proceedings of the National Academy of Sciences of the United States of America
Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters ...

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis.

Journal of medical Internet research
BACKGROUND: Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making...

Identification of synthetic lethality based on a functional network by using machine learning algorithms.

Journal of cellular biochemistry
Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 ge...

Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review.

Computers in biology and medicine
PURPOSE OF REVIEW: Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and ...

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.

Omics : a journal of integrative biology
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and...

Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins.

ACS synthetic biology
Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space. Here, we propose a novel approach that combines molecular evolution with machine le...

Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach.

Journal of healthcare engineering
Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The ob...

Low-rank and sparse embedding for dimensionality reduction.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a robust subspace learning (SL) framework for dimensionality reduction which further extends the existing SL methods to a low-rank and sparse embedding (LRSE) framework from three aspects: overall optimum, robustness and gen...

Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data.

NeuroImage
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they ...