Softmax is a mathematical function commonly used in machine learning, particularly in the context of classification problems. It transforms a vector of raw scores, often called logits, from a model into a vector of probabilities that sum to one. The probabilities generated by the softmax function represent the likelihood of each class being the correct classification. $$\sigma(\mathbf{z})_i = \frac{e^{z_i}}{\sum_{j=1}^K e^{z_j}}$$

Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression. It finds the best hyperplane that separates the data into different classes with the largest possible margin. SVM can work well with high-dimensional data and use different kernel functions to transform data for better separation when it is not linearly separable.$$f(x) = sign(w^T x + b)$$

Random Forest is an ensemble machine learning algorithm that builds multiple decision trees during training and merges their outputs to improve accuracy and reduce overfitting. It is commonly used for both classification and regression tasks. By averaging the predictions of several decision trees, Random Forest reduces the variance and increases model robustness, making it less prone to errors from noisy data. $$\text{Entropy}_{\text{after}} = \frac{|S_l|}{|S|}\text{Entropy}(S_l) + \frac{|S_r|}{|S|}\text{Entropy}(S_r)$$

DLVO theory is named after Derjaguin, Landau, Verwey, and Overbeek, who developed it in the 1940s. It describes the forces between charged surfaces interacting through a liquid medium. The theory combines two main types of forces

The avian immune system exhibits a unique immunoglobulin (Ig) system characterized by distinct features absent in other vertebrates. Birds possess a specialized IgY, which serves as the functional equivalent to mammalian IgG and IgE, but with significant structural and functional differences. Unlike mammalian systems, birds utilize a limited number of germline gene segments and rely on gene conversion within the bursa of Fabricius to generate antibody diversity. This mechanism allows for a rapid and diverse immune response, showcasing the evolutionary adaptation of birds to their ecological niches and pathogen challenges.

The 12/23 rule is fundamental in the V(D)J recombination process, which assembles the variable (V), diversity (D), and joining (J) gene segments in the immune system to create diverse antibodies and T cell receptors. The rule stipulates that recombination can only occur between a gene segment flanked by a recombination signal sequence (RSS) with a 12-base pair (bp) spacer and one with a 23-bp spacer. This ensures proper alignment and prevents inappropriate recombination, thereby maintaining the integrity and functionality of the immune response​.

Multi-layer Neural Nets

Hidden Markov Model

Perceptron

Linear Regression

Learning Progress

Naive Bayes and Bayes NetWork

Artificial Intelligent

The flu NA (neuraminidase) protein is crucial because it plays a key role in the influenza virus's ability to infect host cells and spread within the body. It helps the virus to release newly formed viral particles from infected cells, facilitating the spread of the infection. As such, it's a key target for antiviral drugs and vaccine development, helping in controlling and preventing flu outbreaks.

This post provides an in-depth understanding of False Discovery Rate (FDR) and the Benjamini-Hochberg Method, crucial in statistical analysis with large datasets like genomics. It explains FDR's role in identifying false positives in multiple hypothesis testing and the Benjamini-Hochberg Method's effectiveness in controlling FDR. The post compares various p-value adjustment methods, discussing their advantages, limitations, and suitability for different data types. It emphasizes the BH method's balance in statistical power and error control, and its integration in software like R, highlighting its applicability across scientific fields.