NanoString Technologies, Inc.’s Post

Breast Cancer Prediction Model Designed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Tools Used: Scikit-Learn and its dataset of breast cancer, also Gaussian Naive Bayes. https://lnkd.in/g6H7ts9Y Ciao!

In just one month we'll be joined by Andres Nevarez, Postdoctoral Fellow at MD Anderson Cancer Center, who will shed light on the existing limitations of researching triple-negative breast cancer (TNBC) metastasis and the role of morphological analysis in guiding new detection, diagnostic, and treatment strategies. 📅 Mar. 21st at 12 pm (ET)/9 am (PT) Webinar Highlights: - Cancer cell morphology across genomically distinct clonal TNBC cell lines - How quantitative imaging reveals the morpholome of metastatic TNBC - Sequencing-based morpholomic profiling that exposes hidden drivers of metastasis Register + add it to your calendar: https://lnkd.in/gY5PSZ6F #BreastCancerResearch #SingleCellAnalysis #AIinScience

Are you ready for AACR 2024? Scintica will be at booth 712, bringing you the M-Series MRI systems and more! Don't miss this opportunity to learn about these systems that are advancing cancer research in real-time. Read our article: Development & Characterization Of Mammary Intraductal (MIND) Spontaneous Metastasis Models For Triple-Negative Breast Cancer In Syngeneic Mice: https://lnkd.in/gPgtXnNd #AACR2024 American Association for Cancer Research

📢 Exciting Discovery: Unveiling the Role of Fibroblasts in Breast Cancer! 📢 A groundbreaking study recently published in Nature Communications sheds light on the intricate world of cancer-associated fibroblasts (CAFs) within breast cancer tumors. Led by Hugo Croizer, Rana Mhaidly, and their team, the research delved into the spatial landscape and plasticity of immunosuppressive fibroblasts, offering new insights into tumor behavior. #BreastCancerResearch #TumorMicroenvironment #Immunosuppression #NatureCommunications #ResearchBreakthrough #Genomics #Informatics https://lnkd.in/esJi2Xcj

🔬🧬 Published in Nature: Human Tumor Atlas Network — A collection of research articles, methods and datasets from a collaborative initative tracking human tumor evolution in space and time. “Since 2018, the network of HTAN researchers has compiled multiomics data and applied novel analytical tools to expand our understanding of the complexity of tumor ecosystems across many organs and tumor types. The scope of the project exemplifies how to build a mosaic of understanding of cancer progression from spatial and molecular data. The tools and datasets generated provide a resource for the cancer research community to explore and discover new mechanisms of tumorigenesis.” https://lnkd.in/e2FBdNET #cancer #BreastCancer #ColonCancer #research

Another edition of our throwback to AACR series ☀ In collaboration with Canopy Biosciences, this poster, presented by Dr Arne Christians, demonstrates how high resolution imaging of immune checkpoint activation using Navinci’s in situ PLA technology and precise spatial multiplexing on the Canopy CellScape Platform uncovers differential tumor-immune cell regulation in cancer tissues. Watch Arne’s presentation below, and follow the link to read more and view the poster: https://lnkd.in/e-qY2_rt American Association for Cancer Research

🌟 Day 24 Update: Implementing Decision Tree on Breast Cancer Dataset! 🎗️ Today, I implemented the Decision Tree algorithm on the breast cancer dataset and visualized the results. It was insightful to see how the model splits the data to make predictions. GitHub:https://lnkd.in/eMx6YkUr Excited to share the visualizations and insights from this important analysis! 💡 #MachineLearning #DecisionTree #BreastCancer #100DaysOfCode

Recently published my research paper through IEEE titled- “Optimizing Breast Cancer Detection: Machine Learning for Pectoral Muscle Segmentation in Mammograms” Breast cancer detection is a widely researched field, and I’m honored to contribute to this important area. My focus was on the differentiation and segmentation of the pectoral muscle in mammograms. By refining this process, we aim to improve the accuracy of models in predicting breast cancer and assist doctors in identifying it more effectively. I’m grateful for the support and collaboration that made this possible. You can access my paper here: https://lnkd.in/gBYjFtuT #Research #BreastCancerDetection #MachineLearning #HealthcareInnovation

Alhamdulillah!!! Our paper, "Predictive modeling for breast cancer classification in Bangladeshi patients using machine learning and explainable AI," has been published in Scientific Reports, a Q1 Journal with an Impact Factor of 4.996! DOI: https://lnkd.in/gedGXNkS Jajakallah to the team for their dedication and innovative work in advancing breast cancer research. Let's keep pushing boundaries! #breastcancerresearch #machinelearning #ExplainableAI

Discover the insights from our latest scientific blog: "The Vital Role of Highly Characterized Glioblastoma PDX Models in Preclinical Cancer Research." During brain cancer awareness month, explore how TD2's study showcases over 100 GBM PDX models, and download the poster to understand the significant potential of these models in bridging the gap between lab discoveries and clinical breakthroughs. https://hubs.li/Q02wKKRk0 #TD2 #CancerResearch #GBM #PDXModels #AACR2024