AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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AcrNET: predicting anti-CRISPR with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of an...

3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data.

Bioinformatics (Oxford, England)
MOTIVATION: Liquid chromatography coupled with high-resolution mass spectrometry is widely used in composition profiling in untargeted metabolomics research. While retaining complete sample information, mass spectrometry (MS) data naturally have the ...

The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships.

Bioinformatics (Oxford, England)
UNLABELLED: The DynaSig-ML ('Dynamical Signatures-Machine Learning') Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of s...

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-c...

MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.

Bioinformatics (Oxford, England)
MOTIVATION: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell li...

K-RET: knowledgeable biomedical relation extraction system.

Bioinformatics (Oxford, England)
MOTIVATION: Relation extraction (RE) is a crucial process to deal with the amount of text published daily, e.g. to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders,...

Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposom...

PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.

Bioinformatics (Oxford, England)
MOTIVATION: Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is ess...

NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities.

Bioinformatics (Oxford, England)
MOTIVATION: This article describes NEREL-BIO-an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL by introducing domain-specific entity types. NE...

De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment.

Bioinformatics (Oxford, England)
MOTIVATION: Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on diversity and novelty of molecules, but ignoring drug potential...