Consort Partners’ Post

The potential of Artificial Intelligence to accelerate medical discovery is immense, particularly within biology, writes CEO and Co-Founder, Anand Parikh. Unlike the more predictable fields of chemistry and physics, biology presents unique challenges due to its complexity and constant adaptation, areas where AI can play a transformative role but has been limited to date due to this dynamism. Embracing AI in biology means tackling some of the most complex and variable aspects of science for the very first time. By focusing AI’s power on the untapped potential of the metabolic genome in cancer research, for example, we are not just making incremental improvements but are positioned to make quantum leaps in understanding, managing and treating cancer. This is key to unlocking novel strategies that could serve as the fourth pillar of cancer treatment, alongside surgery, chemotherapy, and radiation. This innovative approach could fundamentally transform cancer care, providing personalized, effective treatments based on the metabolic profiling of individual tumors and a patient’s likely reactions to multiple therapeutic approaches. By doing so, we are setting the stage for discoveries that could fundamentally alter our approach to health and disease, making biology the next great frontier for AI. Read more here via Drug Discovery & Development:  https://lnkd.in/gA3A9sgS #CancerResearch #PrecisionMedicine #AIinBiology

Resistance, #biomarkers, and #heterogeneity are the arch-nemeses of immuno-oncology. Solving these mysteries could unlock a new era of effective cancer treatments. Learn more in our article: https://ow.ly/NVnF50QoWmH #CASInsights #emergingtrends #2024trends

🧬🚀 Exciting Breakthrough Alert! Researchers from MIT & Harvard have just unveiled an AI-driven method that's revolutionizing cellular reprogramming! By focusing on causal relationships in genome regulation, this innovative technique promises to rapidly accelerate our path to discovering groundbreaking immunotherapy and regenerative therapies! 💡 Imagine a world where reprogramming a patient’s T-cells to become potent cancer killers is as routine as getting a flu shot! 🎯 With fewer trials and reduced experimental costs, we’re not just pushing boundaries; we’re erasing them altogether! Get ready for a future where optimal genetic interventions are identified efficiently, making life-saving treatments a reality for many! 💪🌟 #Innovation #AI #GenomeEngineering #HealthTech

Resistance, #biomarkers, and #heterogeneity are the arch-nemeses of immuno-oncology. Solving these mysteries could unlock a new era of effective cancer treatments. Learn more in our article: https://ow.ly/4fk150QoWcS #CASInsights #emergingtrends #2024trends

For this work, the researchers pointed their strategy towards identifying broad classes of antibiotics, such as methicillin-resistant Staphylococcus aureus (MRSA) and other gram-positive bacteria. Wong and colleagues determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Wong predicts that Phare Bio, a non-profit social enterprise Collins co-founded that uses cutting-edge AI and deep learning to address the most urgent global threats, would use the research article’s findings. The real success of this research is that this approach is widely applicable for many drug classes, including small molecules that selectively kill senescent cells, which have been implicated in cancer and aging. This concept inspired Wong to co-found Integrated Biosciences. This start-up is harnessing this approach to drug discovery to create a pipeline of clinical candidates targeting age-related diseases. #ai

📢 Original Article Sharing-Vol. 29 No. 2 💕 Title: Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning 🤵 Authors: Alex Kumar, Valentina L. Kouznetsova, Santosh Kesari, Igor F. Tsigelny* 🔔 Full Text: https://lnkd.in/gAgVMQ7H 🔑 Keywords: machine learning; Parkinson's disease; miRNA biomarkers; neural networks; deep learning 😎Welcome to your reading! #Bioscience #biomedicalscience #biochem #medicalscience #ScienceCommunication #Biochemistry #StemCell #Virology #CancerResearch #immunology #Genetics #MolecularBiology #Microbiology #medicine #health #CellularHealth #CellCancer

Powering Explainable AI in Transcriptomics! 🚀🧬 We were revisiting the intriguing study published in Genomics, Proteomics & Bioinformatics in August 2022 that revolutionizes our understanding of transcriptomic data through explainable AI. With a focus on interpretability, the study evaluated eight popular model explainers, including DeepLIFT, Integrated Gradients, and Guided Grad-CAM, applied to CNNs and multilayer perceptrons (MLPs). The results were impressive, with MLP-based models showing enhanced biological relevance and reproducibility, particularly for identifying tissue-specific genes and potential cancer biomarkers. Despite challenges such as the complexity of CNNs and variability in gene contribution scores, the optimized strategies proposed—such as model aggregation and simple repeats—provided robust solutions. The study utilized 27,417 RNA-seq samples from the GTEx and TCGA projects, representing a comprehensive dataset of normal and cancer tissues. This extensive dataset enabled the models to capture a wide range of gene expression profiles, ensuring that the findings are both reliable and applicable across various tissue types. The relevance of this study to transcriptomics lies in its ability to make ML models interpretable, thereby uncovering critical insights from gene expression data. By identifying the top contributing genes to tissue types and disease statuses, researchers can gain a deeper understanding of the underlying biological mechanisms. This has significant implications for advancing our knowledge in areas such as tissue heterogeneity, disease mechanisms, and cellular engineering. Kudos to Yongbing Zhao, Jinfeng Shao, and Yan W. Asmann for their innovative work in this domain! Read more here: https://lnkd.in/gTV7JzGT  Need support with implementing algorithms like the above? Visit us at https://lnkd.in/gsQuAi_P #Transcriptomics #AI #DeepLearning #Bioinformatics #HealthcareInnovation #MedicalResearch #GeneExpression #CancerBiomarkers #HealthTech #Genomics

CODEX: COunterfactual Deep learning for the in-silico EXploration of cancer cell line perturbations

Exciting new technologies in Spatial Transcriptomics! 🧬🔬 Spatial transcriptomics is an exceptional molecular profiling technique that allows us to measure gene expression profiles on tissue sections in high resolution. What can we get out of this analysis: Spatial Distribution of Gene Expression Cellular Heterogeneity Cell-Cell Interactions Tissue Architecture and Organization Disease Pathology and Biomarker Discovery What research themes can use spatial transcriptomics? ANY RESEARCH! What quickly comes to mind is tumor tissues because of their complex and heterogeneous nature. A recent trend for brain tissue spatial single-cell transcriptomics is on the rise as well! That being said, we are using spatial transcriptomics for different disease models, including cardiovascular and mitochondrial diseases, both in the heart and brain. Until a few years ago, the major commercial platform was 10X Genomics with their Xenium technology, and the most recent technology, Visium (higher resolution). Recently, however, I am starting to see many new platforms surfacing that offer some advantages over 10X Genomics. I attended a seminar for NanoString last Friday for their newest platform CosMX. It offers a larger analysis area on the slide compared to traditional methods. This means more spatial context for the transcriptomic data, with the same price range. One major standout feature of NanoString is its ability to perform two reactions on the same slide for different tissue sections. The resolution is also superior to Xenium, allowing for more precise localization of gene expression within tissues, enhanced by cell-cell segregation analysis that does not just depend on nuclear staining but also parameters of cells and gene expression intensity. Of course, not to forget about Stereo-seq (Chen et al., 2022, Cell 185, 1777–1792) technology that shows extraordinary resolution on a large field-of-view! Albeit still very expensive... The most exciting thing about this is the rise in competition. Certainly many researchers are seeking to set up their own startup that competes with the market, but the ghost of DOMINANT CORP is haunting. Constant competition fuels innovation. It's exciting to see how these advancements will shape the future of biomedical research. Did anyone use NanoString's platform? I'd love to hear your thoughts... #SpatialTranscriptomics #NanoString #10XGenomics #Innovation #BiomedicalResearch #Science Image taken from NanoString.com

Ten years ago, sequencing a human genome was expensive in terms of both money and computational time. The technology was developed enough to attempt and get a result, and the effort behind that result was impressive from many points of view. Today, the astonishing technological revolution concerning sequencing technologies, bioinformatic algorithms, biobanking, and storage information allows us to sequence a human genome with limited costs in a relatively small amount of time. We have fully entered the Genome Era. Early diagnosis (just a few hours after birth), personalized therapy, and precision oncology are now becoming a practical possibility and are no more spectral mirages on the horizon of the future. Personally, I think this is just the starting point because the parallel revolutions in artificial intelligence and sequencing technological development will boost genomic research and allow the implementation of clinical practice with such new approaches. I would not be surprised if, within a few years, large sequencing programs will be established by ordinary routine in Hospitals. This is a really good time for working in the field of Genetics and Genomics, and I hope I will still have the opportunity to take part in this amazing revolution in the future. If you are curious about and interested in this subject, then I suggest this reading from Nature Journal: https://lnkd.in/d9RqDVep