Gene pathogenicity prediction of Mendelian diseases via the random forest algorithm
Sijie He1,2,3,4, Weiwei Chen1,2, Hankui Liu2,3,4
1BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China.
Human Genetics
|May 10, 2019
Summary
Researchers developed a gene pathogenicity prediction (GPP) score using machine learning to identify Mendelian disease genes. This approach estimated 10,384 such genes and showed a correlation between GPP score and disease severity.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Mendelian diseases are significant genetic disorders.
- Identifying causative genes and assessing their pathogenicity are crucial but underexplored areas.
Purpose of the Study:
- To develop a method for evaluating gene pathogenicity.
- To estimate the total number of Mendelian disease genes.
- To correlate gene pathogenicity with disease severity.
Main Methods:
- Utilized a machine learning approach, specifically the random forest algorithm.
- Calculated a gene pathogenicity prediction (GPP) score.
- Applied the GPP score to a testing gene set.
Main Results:
- Achieved 80% accuracy, 93% recall, and an AUC of 0.87 in gene pathogenicity prediction.
- Estimated that 10,384 protein-coding genes are Mendelian disease genes.
- Found a positive correlation between the GPP score and disease severity.
Conclusions:
- The GPP score offers a reliable method for predicting gene pathogenicity.
- This study provides the first estimation of the total number of Mendelian disease genes.
- Gene pathogenicity is linked to disease severity.
Related Concept Videos
02:27Predicting Molecular Geometry
45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
01:04Random Error
9.2K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.2K
01:09Random Variables
17.6K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.6K
01:13Randomized Experiments
8.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
8.9K
01:12Trial and Error and Algorithm
403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403
01:20Random and Systematic Errors
14.7K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.7K