AIMC Topic: Software

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Regulatory Aspects of the Use of Artificial Intelligence Medical Software.

Seminars in radiation oncology
The rapidly evolving scenario of Artificial intelligence (AI) in medicine comes with new regulatory challenges, including certification, ownership, and control of data sharing, privacy protection, and accountability. The Medical Physicists (MPs) are ...

Uncertainty Assessment for Deep Learning Radiotherapy Applications.

Seminars in radiation oncology
In the last 5 years, deep learning applications for radiotherapy have undergone great development. An advantage of radiotherapy over radiological applications is that data in radiotherapy are well structured, standardized, and annotated. Furthermore,...

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...

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, ...

DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins.

Briefings in bioinformatics
Protein S-sulfinylation is an important posttranslational modification that regulates a variety of cell and protein functions. This modification has been linked to signal transduction, redox homeostasis and neuronal transmission in studies. Therefore...

MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach.

Briefings in bioinformatics
Structural variations (SVs) play important roles in human genetic diversity; deletions and insertions are two common types of SVs that have been proven to be associated with genetic diseases. Hence, accurately detecting and genotyping SVs is signific...

LanceOtron: a deep learning peak caller for genome sequencing experiments.

Bioinformatics (Oxford, England)
MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay's ...

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.

Bioinformatics (Oxford, England)
MOTIVATION: Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict...

Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.

Bioinformatics (Oxford, England)
MOTIVATION: In this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear metho...

DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based...