AIMC Topic: Algorithms

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QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

Protein science : a publication of the Protein Society
The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target prote...

A deep learning method to map tissue architecture.

Nature reviews. Genetics
A new study in describes a computational method named UTAG (unsupervised discovery of tissue architecture with graphs) that aims to identify and quantify higher-level tissue domains from biological images without previous knowledge.

Improving Methods of Identifying Anaphylaxis for Medical Product Safety Surveillance Using Natural Language Processing and Machine Learning.

American journal of epidemiology
We sought to determine whether machine learning and natural language processing (NLP) applied to electronic medical records could improve performance of automated health-care claims-based algorithms to identify anaphylaxis events using data on 516 pa...

Using Natural Language Processing to Improve Discrete Data Capture From Interpretive Cervical Biopsy Diagnoses at a Large Health Care Organization.

Archives of pathology & laboratory medicine
CONTEXT.—: The terminology used by pathologists to describe and grade dysplasia and premalignant changes of the cervical epithelium has evolved over time. Unfortunately, coexistence of different classification systems combined with nonstandardized in...

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.

Revista espanola de enfermedades digestivas
BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN),...

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Briefings in bioinformatics
Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher sel...

Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network.

Briefings in bioinformatics
Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinit...

Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning.

Briefings in bioinformatics
With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein...

EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning.

Briefings in bioinformatics
Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs....

DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms.

Briefings in bioinformatics
Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms an...