Our observation of multiple, independent selective sweeps at the CYP6 locus in different regions of Africa suggests that resistance is associated with different haplotypes in different regions of Africa. There is a risk that resistance haplotypes could spread among regions and even combine to create multiple- and super-resistant mosquitoes. Therefore, it is important to understand the factors defining population structure of An. funestus across its range. Currently, the relative contributions of isolation by geographical distance, the presence of physical geographical barriers such as the Rift Valley and genomic barriers to recombination, such as chromosomal inversions, are not clearly defined. Each of these has different consequences for the evolution of resistance. For instance, if populations are simply separated by geographical distance then one may expect resistance haplotypes to spread over time and to mix, creating multiple- and super-resistant mosquitoes that will seriously jeopardise future vector control. Conversely, barriers to gene flow, whether geographical (e.g rift valley) [30] or evolutionary adaptive factors (chromosomal inversions) [31], may more effectively limit the continental spread of resistance haplotypes. However, autochthonous selection of resistance haplotypes in different regions makes it more difficult to develop molecular markers for monitoring resistance across Africa and complicates the application of novel malaria control approaches such as gene drives or symbiont-based control that rely on the natural spread through a population of the genotype or symbiont.
Analysis of genome-wide polymorphism in the major malaria vector Anopheles funestus elucidates the population history and structure of the species and identifies signatures of recent positive selection driven by insecticide use. The identification of multiple, independent selective sweeps at the same locus highlights the evolutionary plasticity of the species and its ability to evolve in response to vector control efforts. This strengthens the case for active resistance management strategies and the development of novel insecticides and alternative control strategies.
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Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
As one of the cutting-edge cancer treatments, targeted drug therapy has the advantages of high efficiency, few side effects, and low drug resistance for patients1. However, there are several drawbacks to the existing targeted therapies, such as a few druggable targets2, ineffective coverage of the patient population, and the lack of alternative responses to drug resistance in patients1. Therefore, identifying novel therapeutic targets and evaluating their druggability3,4 becomes the current cancer research focus of targeted drug therapy.
To elucidate the molecular mechanisms underlying cancer genesis, interactome data can be comprised and modelled in network structures in which components are biological entities (e.g., genes, proteins, mRNAs, and metabolites) and edges are associations/interactions between them (e.g., gene co-expression, signalling transduction, gene regulation, and physical interaction between proteins9,10,11,12,13,14). Artificial intelligence biology analysis algorithms are effective method to process the biological network data, which build machines or programs to simulate human intelligence, so as to implement classification, clustering and prediction tasks in biological network15. Therefore, artificial intelligence algorithms can effectively tackle the complexity of cancer that arises from interactions between genes and their products16,17 in biological network structures, so as to improve our understanding of carcinogenesis11,12,18,19,20,21,22 and explore novel anticancer targets23,24,25,26,27,28,29.
Although artificial intelligence biology analysis has been widely used to improve our understanding of carcinogenesis, to the best of our knowledge, there is no systematic review that introduces the scope of related research and explains the network-based and the ML-based biology analysis algorithms to identify novel anticancer targets and discover drugs. Therefore, in the next section, we will describe the scope of artificial intelligence biology analysis for novel anticancer targets investigation. In the third section, we will introduce the basic principles and theory of commonly used artificial intelligence biology analysis algorithms. Then, we will briefly review and discuss studies that utilize network-based and ML-based biology analysis for cancer target identification and drug discovery. Finally, we will summarize the content of the article, discuss the limitations and challenges faced by the community, and point out the potential of artificial intelligence biology analysis to identify the therapeutic targets and discover drugs for cancer.
Recently, the rapid development of cancer-related multiomics technologies34,35,36 has been one of the most important factors for artificial intelligence biology analysis to explore novel anticancer targets37,38,39. Figure 2 classifies these technologies into five aspects: epigenetics, genomics, proteomics, metabolomics, and multiomics integration analysis. Furthermore, Table 1 lists the related major diseases, drug targets, genomics, and network databases commonly used in multiomics integration analysis for these five aspects. Next, we will detail these five aspects.
Furthermore, analysing data from 1,547 cancer patients revealed 56 indispensable genes in nine cancers. 46 of these genes were associated with cancer for the first time, demonstrating the ability of intelligent network controllability analysis to identify novel disease genes and potential drug targets77. Moreover, Valle et al.78 developed a network-based biology analysis framework to compute the proximity between polyphenol targets and disease proteins. The calculated results indicated that the diseases whose proteins are proximal to polyphenol targets have significant gene expression changes, while the diseases whose proteins are distal to polyphenol targets have no such change. The network relationship between disease proteins and polyphenol targets provides not only a computing method to reveal the effect of polyphenols on diseases but also a basis to identify novel anticancer targets.
For example, Mallik et al.139 first identified differentially expressed and methylated genes in uterine leiomyoma tumours and then found TFs and miRNAs that regulate the expression of these genes. Subsequently, they reconstructed a network that comprised the genes, TFs, and miRNAs and then used eigenvector centrality to identify potential biomarkers. They specified that PTGS2 and TACSTD2 are potential novel biomarkers, since both genes are downregulated and hypermethylated in the tumour.
Because the wide and easy accessibility of high-throughput data in oncology has provided the basis for developing novel artificial intelligence methods and validating their capability to identify therapeutic targets, this section will focus on reviewing the biomedical applications from four perspectives. First, we present the artificial intelligence applications to identify novel anticancer targets. Second, we present the artificial intelligence applications to evaluate the druggability of potential target genes. Third, we show the artificial intelligence applications for drug discovery. Fourth, we show the artificial intelligence applications for drug property prediction.
ML-based biology network analysis applications are applied to interrogate the large, complex data and thus identifying reliable potential novel targets as effective treatments of human diseases200. These ML-based biology analysis applications for novel anticancer targets identification consist of classification201, clustering202, neural networks203,204, and so on205. Here, due to the limit space of the review, we only focus on the ML-based biology network analysis applications for classifications and graph-based neural networks.
In addition, Xuan et al.204 proposed a novel method based on the graph convolutional network and convolutional neural network (GCNLDA) to infer disease-related lncRNA candidates. First, they developed a network that is comprised of lncRNA, disease, and miRNA nodes. Then, they developed an embedding matrix of lncRNA-disease node pairs with respect to the biological premises. Then, they employed a convolutional neural network to explore various connections related to lncRNA-disease on node pair embedding. Finally, they learned the local network representations of lncRNA-disease pairs by deeply integrating the graph convolution autoencoder into topological lncRNA-disease-miRNA heterogeneous networks. Cross-validation confirmed that GCNLDA outperforms other state-of-the-art methods in terms of both AUC and AUPR161. Case studies204 on stomach cancer, osteosarcoma and lung cancer confirmed that GCNLDA effectively discovered potential lncRNA-disease associations. Therefore, GCNLDA is becoming an effective tool to screen reliable candidates for lncRNA-disease association validation with the help of biological experiments. be457b7860
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