Omer Yilmaz

Aug 18, 2024 | nature.com | Konstantine Tchourine |Keene L. Abbott |Florian Gourgue |Brian T. Do |Tenzin Kunchok |Allison Lau | +9 more

AbstractMetastases arise from subsets of cancer cells that disseminate from the primary tumour1,2. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize3,4. Specific cancer types metastasize to consistent subsets of tissues, suggesting that primary tumour-associated factors influence where cancers can grow.

Jun 7, 2024 | jcp.bmj.com | soo lee |Omer Yilmaz |Ömer H. Yilmaz |Nandan Padmanabha |Vikram Deshpande

AbstractAims Venous invasion (VI) in colorectal carcinoma influences treatment strategies, especially in early stages. Despite elastin staining effectiveness in detecting VI, guidelines for its routine application, including the optimal number of slides for staining, are limited. Methods Elastin staining was performed for VI assessment in patients with colorectal adenocarcinoma.

Mar 11, 2024 | medium.com | Omer Yilmaz |Ömer H. Yilmaz

XGBoost (Extreme Gradient Boosting) is a tree-based machine learning algorithm that has achieved significant success in various competitions and industrial applications in recent years. To understand its mathematical logic, let’s start with Gradient Boosting Machine (GBM), a more general algorithm, and then move on to XGBoost. GBM is an ensemble algorithm that combines weak learners (typically decision trees) to create a strong predictive model.

Feb 27, 2024 | medium.com | Omer Yilmaz |Ömer H. Yilmaz

Stemming is a fundamental technique in natural language processing (NLP) that aims to reduce words to their root or base form. This process involves removing affixes from words to normalize them and improve text analysis and retrieval tasks. Stemming algorithms play a crucial role in various NLP applications, including information retrieval, sentiment analysis, and text mining.

Feb 18, 2024 | medium.com | Omer Yilmaz |Ömer H. Yilmaz

Time series analysis has become a crucial tool in various fields. The SARIMA model combines seasonal, autoregressive, differencing, and moving average components to handle the complexity of time series and is a powerful tool for forecasting future values. In this article, we will explain the basic concepts of the SARIMA model and demonstrate how it is applied through a practical example. What is the SARIMA Model? SARIMA is a model used for the analysis of seasonal time series.