AIMC Topic: Machine Learning

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Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery.

Current topics in medicinal chemistry
BACKGROUND: The lengthy and expensive process of developing a novel medicine often takes many years and entails a significant financial burden due to its poor success rate. Furthermore, the processing and analysis of quickly expanding massive data ne...

Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed.

Methods in molecular biology (Clifton, N.J.)
In the modern health care research, protein phosphorylation has gained an enormous attention from the researchers across the globe and requires automated approaches to process a huge volume of data on proteins and their modifications at the cellular ...

Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.

Methods in molecular biology (Clifton, N.J.)
The major outcomes and insights of scientific research and clinical study end up in the form of publication or clinical record in an unstructured text format. Due to advancements in biomedical research, the growth of published literature is getting t...

Biomedical Literature Mining for Repurposing Laboratory Tests.

Methods in molecular biology (Clifton, N.J.)
Epidemiological studies identifying biological markers of disease state are valuable, but can be time-consuming, expensive, and require extensive intuition and expertise. Furthermore, not all hypothesized markers will be borne out in a study, suggest...

Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation.

Methods in molecular biology (Clifton, N.J.)
Protein posttranslational modifications (PTMs) are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof, the number of functionals PTMs in the eukaryotic proteome is currently underes...

A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction.

Methods in molecular biology (Clifton, N.J.)
Phosphorylation plays a vital role in signal transduction and cell cycle. Identifying and understanding phosphorylation through machine-learning methods has a long history. However, existing methods only learn representations of a protein sequence se...

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database.

Cancer control : journal of the Moffitt Cancer Center
INTRODUCTION: Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificia...

Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

Health informatics journal
BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

Evaluating Representation Learning and Graph Layout Methods for Visualization.

IEEE computer graphics and applications
Graphs and other structured data have come to the forefront in machine learning over the past few years due to the efficacy of novel representation learning methods boosting the prediction performance in various tasks. Representation learning methods...

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Methods in molecular biology (Clifton, N.J.)
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluor...