Synthetic data in biomedicine via generative artificial intelligence
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The transcriptomic architecture of common cancers reflects synthetic lethal interactions
To maintain cell fitness, deleterious genetic alterations are buffered by compensatory changes in additional genes. In cancer, buffering processes could be targeted by synthetic lethality. However, despite the large-scale identification of synthetic lethal effects in preclinical models, evidence that these operate clinically is limited. This impedes the application of synthetic lethal approaches. By integrating molecular profiling data from >9,000 cancers with synthetic lethal screens, we show that transcriptomic buffering of tumor suppressor gene (TSG) loss by hyperexpression of synthetic lethal partners is a common phenomenon, extending to multiple TSGs and histotypes. Transcriptomic buffering is also notable in cancers that phenocopy TSG loss, such as BRCAness cancers, where expression of BRCA1/2 synthetic lethal genes correlates with clinical outcome. Synthetic lethal genes that exhibit transcriptomic buffering also represent more robust synthetic lethal effects. These observations have implications for understanding how tumor cells tolerate TSG loss, in part explain transcriptomic architectures in cancer and provide insight into target selection.
Modelling and design of transcriptional enhancers
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Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach—SYNTA—for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.
Photovoltaic bioelectronics merging biology with new generation semiconductors and light in biophotovoltaics photobiomodulation and biosensing
This review covers advancements in biosensing, biophotovoltaics, and photobiomodulation, focusing on the synergistic use of light, biomaterials, cells or tissues, interfaced with photosensitive dye-sensitized, perovskite, and conjugated polymer organic semiconductors or nanoparticles. Integration of semiconductor and biological systems, using non-invasive light-probes or -stimuli for both sensing and controlling biological behavior, has led to groundbreaking applications like artificial retinas. From fusion of photovoltaics and biology, a new research field emerges: photovoltaic bioelectronics.
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