AI Medical Compendium

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

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pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.

Bioinformatics (Oxford, England)
SUMMARY: Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption ...

No means 'No': a non-improper modeling approach, with embedded speculative context.

Bioinformatics (Oxford, England)
MOTIVATION: The medical data are complex in nature as terms that appear in records usually appear in different contexts. Through this article, we investigate various bio model's embeddings (BioBERT, BioELECTRA and PubMedBERT) on their understanding o...

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and funct...

Predicting cross-tissue hormone-gene relations using balanced word embeddings.

Bioinformatics (Oxford, England)
MOTIVATION: Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have foster...

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England)
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...

Guided interactive image segmentation using machine learning and color-based image set clustering.

Bioinformatics (Oxford, England)
MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understandi...

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of ove...

Structural analogue-based protein structure domain assembly assisted by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, ...

Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data.

Bioinformatics (Oxford, England)
MOTIVATION: In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary coll...

MICER: a pre-trained encoder-decoder architecture for molecular image captioning.

Bioinformatics (Oxford, England)
MOTIVATION: Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches that rely on expert knowledge and fail to consider all the stylistic varia...