AIMC Topic: Machine Learning

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A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Journal of biomedical informatics
Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-...

Prediction and Structure-Activity Relationship Analysis on Ready Biodegradability of Chemical Using Machine Learning Method.

Chemical research in toxicology
Persistent contaminants from different industries have already caused significant risks to the environment and public health. In this study, a data set containing 1306 not readily biodegradable (NRB) and 622 readily biodegradable (RB) chemicals was c...

Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture.

Scientific reports
The complexity of the cerebral cortex underlies its function and distinguishes us as humans. Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron-...

Comparing machine learning methods for predicting land development intensity.

PloS one
Land development intensity is a comprehensive indicator to measure the degree of saving and intensive land construction and economic production activities. It is also the result of the joint action of natural, social, economic, and ecological element...

Interpretability and Optimisation of Convolutional Neural Networks Based on Sinc-Convolution.

IEEE journal of biomedical and health informatics
Interpretability often seeks domain-specific facts, which is understandable to human, from deep-learning (DL) or other machine-learning (ML) models of black-box nature. This is particularly important to establish transparency in ML model's inner-work...

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Medical & biological engineering & computing
Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the ...

Serverless Prediction of Peptide Properties with Recurrent Neural Networks.

Journal of chemical information and modeling
We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solu...

Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.

Biomolecules
CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scientific and medical advances in recent times. The advances in biomedical research are hindered because of the inadvertent burden on the genome when genom...

EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction Through Metagenomics.

IEEE/ACM transactions on computational biology and bioinformatics
A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimension...

Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation.

IEEE/ACM transactions on computational biology and bioinformatics
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein str...