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Current trends and future perspectives in hadron therapy: radiobiology
PurposeThe purpose of this article was to highlight current and future trends in radiobiology in an effort to move hadron therapy forward through the...
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Enhancing SNV identification in whole-genome sequencing data through the incorporation of known genetic variants into the minimap2 index
MotivationAlignment of reads to a reference genome sequence is one of the key steps in the analysis of human whole-genome sequencing data obtained...
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DQMMBSC: design of an augmented deep Q-learning model for mining optimisation in IIoT via hybrid-bioinspired blockchain shards and contextual consensus
Single-chained blockchains are highly secure but cannot be scaled to larger IIoT (Internet of Industrial Things) network scenarios due to storage...
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Enhancing OCT patch-based segmentation with improved GAN data augmentation and semi-supervised learning
For optimum performance, deep learning methods, such as those applied for retinal and choroidal layer segmentation in optical coherence tomography...
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Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations
There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene...
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Evolution of hadron therapy from 1935 to 2005: a personal view
IntroductionHadron therapy is today an established modality in cancer radiation therapy that, world-wide, is available in about hundred centres....
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Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling
Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the...
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METASEED: a novel approach to full-length 16S rRNA gene reconstruction from short read data
BackgroundWith the emergence of Oxford Nanopore technology, now the on-site sequencing of 16S rRNA from environments is available. Due to the error...
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expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer
BackgroundHomologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy...
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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database
BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of...
