AIMC Topic: Machine Learning

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Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.

International journal of environmental research and public health
A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to cl...

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.

Surgery today
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the developme...

Real-to-bin conversion for protein residue distances.

Computational biology and chemistry
Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. ...

GPCRLigNet: rapid screening for GPCR active ligands using machine learning.

Journal of computer-aided molecular design
Molecules with bioactivity towards G protein-coupled receptors represent a subset of the vast space of small drug-like molecules. Here, we compare machine learning models, including dilated graph convolutional networks, that conduct binary classifica...

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention.

International journal of environmental research and public health
This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes...

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Communications biology
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. Th...

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

Molecular diversity
To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of S...

Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Calcium-activated chloride channels (CaCCs) are chloride channels that are regulated according to intracellular calcium ion concentrations. The channel protein ANO1 is widely present in cells and is involved in physiological activities including cell...

Structure-based drug design with geometric deep learning.

Current opinion in structural biology
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has be...