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Morris Aguilar's Projects

consensus_and_profile_of_homologous_strands_of_dna icon consensus_and_profile_of_homologous_strands_of_dna

This Python program analyzes multiple homologous strands with point mutations in order to produce the average-case--or consensus--DNA strand. The program can handle an input of a FASTA file containing multiple DNA strands.

counting_point_mutations_and_hamming_distance_in_dna icon counting_point_mutations_and_hamming_distance_in_dna

This program compares two strands of DNA in a FASTA file and detects point mutations between them. This data is then used to calculate the minimum number of nucleotide substitutions required to transform one string into the other, i.e, the Hamming distance.

finding_a_motif_in_dna icon finding_a_motif_in_dna

This program detects and outputs the nucleotide positions of a given DNA motif within a strand of DNA. A DNA motif is a commonly shared interval of DNA distributed within a genome.

genome_assembly_as_shortest_superstring icon genome_assembly_as_shortest_superstring

Current Project: This program aims to assemble individual strings of DNA into a contiguous superstring over a collection of DNA. Additionally, the program aims to construct the shortest superstring because of the biological principle that nature tends to take the shortest path in evolution.

mendel-s_first_law_and_phenotype_probability icon mendel-s_first_law_and_phenotype_probability

This program calculates probability that two randomly selected mating organisms will produce an individual possessing a dominant allele. The program can calculate the probability for any given number of homozygous recessive, homozygous dominant, and heterozygous organisms.

modeling_a_network_of_contiguous_dna_strands icon modeling_a_network_of_contiguous_dna_strands

This Python program can compare a large number of DNA strands from a FASTA input file and produce an adjacency list. The list represents which strands of DNA have contiguity between them. The program also represents an intermediate step towards genome assembly.

multiple_regression_gene_expression_predictions icon multiple_regression_gene_expression_predictions

The goal of this coding project is to generate a simple regression model (that takes a single input to make a prediction) and compare its performance to a multiple regression model (that takes more than one input to make a prediction.) The models are built in the context of the input, gene expression of a gene familiy, and how it affects the output, effector gene expression. I expect that a regression model built with multiple gene expression inputs would more precisely predict the gene expression of an effector gene. The goal is test this code with existing gene expression repositories such as the NIH Gene Expression Omnibus (GEO) repository.

predicting_house_prices_with_machine_learning icon predicting_house_prices_with_machine_learning

My iPython notebook visualizes more than 20 thousand housing data points from Seattle, WA and builds prediction models via multiple linear regression. In other words, a housing price prediction model was built using multiple house features such as square footage, zip code, year build, lot size, and more.

recurrence_relations_with_population_death_parameters icon recurrence_relations_with_population_death_parameters

This Python program revisits the simulation of a sequence of population growth over time that is defined as a function of the preceding population size. The program takes into account that newborns take time to reach reproductive age. This time I overhauled my code to incorporate added a lifespan parameter for a more realistic simulation.

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