Matrices, Genomics, Transcriptomics, Probability Quiz

Study Notes

Parts of a gene include exons, introns, untranslated regions (UTRs), and regulatory elements such as promoters.
Genomics can be determined using next-generation sequencing or SNP arrays.

PCR and qPCR are both techniques for amplifying DNA, but qPCR quantifies the amount of DNA present.
qPCR, microarrays, and RNAseq are all techniques for measuring gene expression, but they differ in their sensitivity, cost, and resolution.

Sample space refers to all possible outcomes of a random event.
Types of probability distributions include the normal distribution, binomial distribution, and Poisson distribution, each used for different scenarios.
A statistic that quantifies the strength of the association between two events is the correlation coefficient.
Multiple testing correction helps to control the number of false positives in a statistical analysis.

The probability of two events occurring together is equal to the product of their individual probabilities: P(EF) = P(E)P(F)

Conditional probability represents the probability of an event E occurring given that event F has already occurred.
The formula for conditional probability is: P(E|F) = P(F|E)P(E) / P(F)

Multilayer networks are composed of different networks connected by edges between subnetworks.
Multiplex networks have the same nodes but different edges in subnetworks.

Directed graphs have edges with a direction, indicating a relationship from one node to another. Undirected graphs have edges without a direction, representing a mutual relationship between nodes.
A node's degree represents the number of connections it has with other nodes in the network.
A network path is a sequence of connected nodes.
Networks can be used to model and understand biological concepts, such as protein interactions and gene regulation.

Classification is used to categorize data into predefined groups, while prediction aims to estimate a continuous value.
Feature selection can be done at three stages: preprocessing, during model training, and after model training.
Decision trees use a hierarchical structure to classify data based on a series of rules or conditions.
Supervised learning involves training a model on labeled data, while unsupervised learning uses unlabeled data to identify patterns.