AIMC Topic: Software

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MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.

Bioinformatics (Oxford, England)
MOTIVATION: CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area...

Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning.

Bioinformatics (Oxford, England)
MOTIVATION: Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedic...

XGDAG: explainable gene-disease associations via graph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover u...

Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes.

Artificial life
This article tackles the topic of the special issue "Biology in AI: New Frontiers in Hardware, Software and Wetware Modeling of Cognition" in two ways. It addresses the problem of the relevance of hardware, software, and wetware models for the scient...

LegNet: a best-in-class deep learning model for short DNA regulatory regions.

Bioinformatics (Oxford, England)
MOTIVATION: The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar.

DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

Bioinformatics (Oxford, England)
MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells.

[Application Value of Artificial Intelligence-assisted Three-dimensional Reconstruction in Planning Thoracoscopic Segmentectomy].

Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND: The three-dimensional (3D) can assist in planning lung segmentectomy. 3D reconstruction software based on artificial intelligence algorithm is gradually applied in clinic. The aim of this study was to evaluate the accuracy and safety of 3...

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Briefings in bioinformatics
Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of pr...

DDMut: predicting effects of mutations on protein stability using deep learning.

Nucleic acids research
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongo...

The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions.

Nucleic acids research
Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitous...