AIMC Topic: Machine Learning

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ToxMVA: An end-to-end multi-view deep autoencoder method for protein toxicity prediction.

Computers in biology and medicine
Effectively predicting protein toxicity plays an essential step in the early stage of protein-based drug discovery, which is of great help to speed up novel drug screening and reduce costs. Recently, several relevant datasets have been designed, and ...

Machine learning applications for transcription level and phenotype predictions.

IUBMB life
Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of g...

Machine Learning with Neural Networks to Enhance Selectivity of Nonenzymatic Electrochemical Biosensors in Multianalyte Mixtures.

ACS applied materials & interfaces
Nonenzymatic biosensors hold great potential in the field of analysis and detection due to long-term stability, high sensitivity, and low cost. However, the relative low selectivity, especially the overlapped oxidation peaks of biomarkers, in the bio...

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.

Sensors (Basel, Switzerland)
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected accordi...

Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.

Molecules (Basel, Switzerland)
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and ...

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

International journal of molecular sciences
Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among th...

Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model.

Scientific reports
With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selectio...

Machine learning of pair-contact process with diffusion.

Scientific reports
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified...

The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.

Scientific reports
Animal-borne tagging (bio-logging) generates large and complex datasets. In particular, accelerometer tags, which provide information on behaviour and energy expenditure of wild animals, produce high-resolution multi-dimensional data, and can be chal...

Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach.

Nanoscale
Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge. Understanding how interactions between particles and cells are impacted by the physicochemical prop...