Artificial intelligence

Andy Chen - By using GWAS (Genome-Wide Association Studies), he found specific genetic markers (pieces of DNA) that are linked to traits that make the wheat more resistant to cold temperatures ( winter hardiness ). These findings could help improve the ability of winter wheat to survive in colder conditions. 1. What is GWAS? Scientists use GWAS to scan the entire genotype (DNA) of many individuals to identify which genetic variations are linked to specific phenotypes (traits or diseases). For example, they might look for genetic markers that explain why a plant has a certain height or why a person is more resistant to a disease. 2. What is linkage analysis? Linkage analysis is a method used to find the location of genes on chromosomes that are responsible for specific traits or diseases. It’s based on the idea that genes that are physically close to each other on a chromosome tend to be inherited together. Linkage analysis helps scientists figure out which genes are passed down fr

DNA is the smallest part (like the letters). Genes are made up of DNA and are specific sections that tell your body to do certain things (like words). Genome is the entire set of instructions, including all the genes and DNA, that makes your body function (like a sentence or book). Plants, like all living things, have DNA, which is like their instruction manual. DNA tells the plant how to grow, how many beans to make, and other important things.  Scientists are trying to find out which parts of a plant’s instruction manual (DNA) are responsible for how many beans it makes, so they can help grow plants that are better at producing beans. This is known as GAWS.  By knowing which parts of the DNA affect bean production, scientists can help farmers grow plants that produce more beans or have other desirable traits. This is like figuring out how to make a better recipe. The old ways of figuring this out don't always work well, especially for plants that have a lot of similar genes. T

K-fold cross-validation is not an algorithm itself; it is a technique or method used for model evaluation. It helps assess how well a machine learning model performs by splitting the dataset into multiple parts (folds) and repeatedly training and testing the model on different subsets of the data.  For instance, suppose you want to test three algorithms—Algorithm A, Algorithm B, and Algorithm C using a dataset with 10 data points and 5-fold cross-validation. You start by dividing the dataset into 5 folds, each containing 2 data points: Fold 1 (data points 1 and 2), Fold 2 (data points 3 and 4), Fold 3 (data points 5 and 6), Fold 4 (data points 7 and 8), and Fold 5 (data points 9 and 10). For each algorithm, you train the model on 4 of the 5 folds and test it on the remaining fold. You repeat this process for all 5 folds, recording the performance score each time and then averaging these scores. For example, after applying this method: Algorithm A might have an average performance score