AIMC Topic: Machine Learning

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Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

Biochemical pharmacology
For medicines, the apparent membrane permeability coefficients (P) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P value...

Davis Computational Spectroscopy Workflow-From Structure to Spectra.

Journal of chemical information and modeling
We describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the at...

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts.

Sensors (Basel, Switzerland)
Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for th...

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops.

Nature communications
Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-d...

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.

BMC cancer
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, ...

X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing.

Biomedical physics & engineering express
This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These fea...

Machine Learning to Predict Fascial Dehiscence after Exploratory Laparotomy Surgery.

The Journal of surgical research
BACKGROUND: Fascial dehiscence following exploratory laparotomy is associated with significant morbidity and increased mortality. Previously published risk prediction models for fascial dehiscence are dated and limit a surgeon's ability to perform re...

A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes.

Molecular immunology
The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can on...

Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties.

Journal of chemical information and modeling
Machine learning is emerging as a new paradigm to rationalize chemical properties for deepening our understanding of chemistry and providing instructive clues on better materials performance. While the complex architecture of machine learning contrib...