for(x in lowest_mean_gene){ low_gene_exp<- as.numeric(AH_data[x,]) hist(low_gene_exp, main=x,breaks=40) }
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Concepts in Designing Database
A database design is the process of data organization based on a database model. The process deals with identifying what data should be stored in a database and how data elements relate to each other.
Entity Relationship Diagram
Complex real-world applications call for large volumes of data. Therefore, it is necessary to build a great database to store data safely and coherently. The ER data model aids in the process of database design. It helps outline the structure of an organization’s database by understanding the real-world interactions of objects related to the data. For example, if a school is tasked to store student information, then analyzing the correlation between the students, subjects, and teachers would help identify how the data needs to be stored.
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- 427387350304450293426468451389380370387308319335312412308358514444443324508424390426410417 Class Frequency 1. ___ -___ ____ 2. ___ -___ ____ 3. ___ -___ ____ 4. ___ -___ ____ 5. ___ -___ ____ 6. ___ -___ ____ 7.___ -___ ____ 8. ___ -___ ____ Construct a frequency histogram of the data. Descibe any patterns.What are The DNA Fragment Assembly Problem in machine learning?MIPS Assembly Complete gcd_cur function, which recursively calculates the GCD (Greatest Common Divisor) of two given positive integers input using the following Euclidean algorithm (Links to an external site.). def gcd_recur(a, b): if b = 0: return a; else: return gcd_recur( b, (a mod b) ); >> a0: the 1st input argument, a>> a1: the 2nd input argument, b ############################################################################################################################################################################################## PART 2 (gcd_recur)#a0: input number#a1: input number###############################################################gcd_recur:############################### Part 2: your code begins here ## ############################### Part 2: your code ends here ##jr $ra
- PYTHON/ Computational stoichiometry Make an organized output of the species and stoichiometric data for the reaction series : r0 : C(s) -> C(g)r1 : 2 C(g) + O2 -> 2 COr2 : CO + 0.5 O2 -> CO2r3 : C(g) + H2O -> CO + H2r4 : CH4 <=> C(g) + 2 H2 A sample code has been provided to guide your results, please follow as closely as you can to obtain the right answer. '''Create the species list''' species_tmp = list() # temporary list for species for r in reactions: left = r.split('<=>')[0].strip() # reactants side right = r.split('<=>')[1].strip() # products side left_terms = left.split('+') # reactant species w/ stoichiometric coeff. right_terms = right.split('+') # product species w/ stoichiometric coeff. terms = [t.strip() for t in left_terms] + [t.strip() for t in right_terms] # concatenate list comprehensions for i in terms: tmp = i.split(' ') # split stoichiometric coefficient from species name…MIPS Assembly Complete gcd_cur function, which recursively calculates the GCD (Greatest Common Divisor) of two given positive integers input using the following Euclidean algorithm (Links to an external site.). def gcd_recur(a, b): if b = 0: return a; else: return gcd_recur( b, (a mod b) ); >> a0: the 1st input argument, a>> a1: the 2nd input argument, b My Code: ############################################################################################################################################################################################## PART 2 (gcd_recur)#a0: input number#a1: input number###############################################################gcd_recur:############################### Part 2: your code begins here #### return value is v0move $t0, $a0 move $t1, $a1 loop: beq $t1, $0, done div $t0, $t1 move $t0, $t1 mfhi $t1 j loop done: move $v0, $t0…Write an R script to identify the differentially expressed genes at P < 0.01 of Saccharomyces Cerevisiae grown in YPD and Delft.
- Data Given : 5Belinda, 4CSCI212CSCI235CSCI222CSCI203Calvin, 3CSCI205CSCI204CSCI222Johnathan, 4CSCI212CSCI222CSCI204CSCI203Ann, 2CSCI204CSCI213Brandon, 4CSCI212CSCI203CSCI235CSCI213PLEASE SEND SCREENSHOT OF CODE AND OUTPUT WHICH EXECUTED AT ANY PYTHON COMPILER. Train an artificial neural network using CIFAR10 dataset at any python compiler. You can get the dataset from Keras similar to mnist dataset - try to find the best performing model for your dataset (CIFAR-10), use the splitting for train/val/test as 80/10/100 PLEASE SEND SCREENSHOT OF CODE AND OUTPUT WHICH EXECUTED AT ANY PYTHON COMPILER.For the data set {(ai, 6), (ml, 7), (ds, 3), (os, 1), (se, 2)} build a treap. Perform deletion of (ml, 7) over the treap and insert (ps, 7) into the deleted treap
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