Complementing a Strand of DNA

Problem
In DNA strings, symbols 'A' and 'T' are complements of each other, as are 'C' and 'G'.
The reverse complement of a DNA string s is the string sc formed by reversing the symbols of s, then taking the complement of each symbol (e.g., the reverse complement of "GTCA" is "TGAC").
Given: A DNA string s of length at most 1000 bp.
Return: The reverse complement sc of s.
Sample Dataset
AAAACCCGGT
Sample Output

ACCGGGTTTT


#include <stdio.h>
void main ()
{
	char s[9999];
	int i=0;
	int cnt  =0;
	int cnt_a=0;
	int cnt_c=0;
	int cnt_g=0;
	int cnt_t=0;
	puts("输入:\n");
	gets(s);
	for(i=0;s[i]!='\0';i++)
	{
	
		if (s[i] == 'C')
		{
			s[i]  = 'G';
			continue;
		}

		if (s[i] == 'G')
		{
			s[i]  = 'C';
			continue;
		}

		if (s[i] == 'T')
		{
			s[i]  = 'A';
			continue;
		}

		if (s[i] == 'A')
		{
			s[i]  = 'T';
			continue;			
		}

	}
	cnt = i;
	for(;cnt>0;cnt--)
	{
		printf("%c",s[cnt-1]);
	}
}


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Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思
06-11
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