Refine gene functional similarity network based on interaction networks

Tian, Zhen; Guo, Maozu; Wang, Chunyu; Liu, Xiaoyan; Wang, Shiming

doi:10.1186/s12859-017-1969-1

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…Moreover, different algorithms and tools have been developed for LBVS such as SwissSimilarity ( http://www.swisssimilarity.ch/ ) [ 198 ], METADOCK [ 199 ], Open-source platform [ 200 ], HybridSim-VS ( http://www.rcidm.org/HybridSim-VS/ ) [ 201 ], PKRank [ 202 ], PyGOLD ( http://www.agkoch.de/ ) [ 203 ], BRUSELAS ( http://bio-hpc.eu/software/Bruselas ) [ 204 ], RADER ( http://rcidm.org/rader/ ) [ 205 ], QEX [ 206 ], IVS2vec ( https://github.com/haiping1010/IVS2Vec ) [ 207 ], AutoDock Bias ( http://autodockbias.wordpress.com/ ) [ 208 ], Ligity [ 209 ], D3Similarity ( https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php ) [ 210 ], and GCAC ( http://ccbb.jnu.ac.in/gcac ) [ 211 ]. Emerging evidence suggests the potential implementation of AI algorithms in LBVS such as identification of aurora kinase A inhibitors [ 212 ], G-quadruplex-targeting chemotypes [ 213 ], PI3Kα inhibitors [ 214 ], targeting dengue virus non-structural protein 3 helicases [ 215 ], potential selective histone deacetylase 8 inhibitors [ 216 ], and novel p-Hydroxyphenylpyruvate dioxygenase inhibitors [ 217 ].…”

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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“…Predictions of functional similarity between genes could be generated using methods based on functional annotations of genes within Gene Ontology Database [3]. Networks of genes with associated functions could also be employed to predict the function of a gene of interest [4]. In addition, gene function could be predicted by analyzing multiplex gene expression maps while considering the spatial relationship among the sampled locations [5].…”

Section: Introductionmentioning

confidence: 99%

Genetic Functional Similarity Clustering Using CRISPR-Cas9 Knockout Data

Lee

Cho²,

韩红

et al. 2023

Preprint

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Background: Machine learning models have become a powerful tool in the field of genetics, allowing scientists to make more accurate predictions about the functions of genes using currently available information. Utilizing both pre-existing annotations from previous studies and multiple genome-wide experimental data would provide us with the potential to construct a more comprehensive model about the functional similarity between genes. Results: In this paper, we used knockout phenotype information obtained from CRISPR-cas9 knockout experiments performed under various conditions and using various cells to improve gene functional similarity prediction. We applied Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and Agglomerative Hierarchical Clustering algorithms to find functionally linked gene groups from knockout data. Subsequent gene enrichment analysis revealed that gene groups defined with knockout data could be associated with specific biological functionality with a high degree of statistical significance. Furthermore, we were able to identify possible functional similarities between an undescribed gene and previously researched genes by using HDBSCAN labels. As a case study, we manually investigated KCNA1/SCN9A pair, which showed highly similar HDBSCAN label profiles, and identified that they were both associated with Oncogene-Induced Senescence (OIS), information that was not found in available databases. Conclusion: We found that previously unaddressed functional similarities between genes could be identified from genome-wide CRISPR-Cas9 phenotype datasets. This approach might help to identify novel biomarkers or potential drug targets for diseases with few therapeutic options.

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“…GAIN [16] constructs bipartite networks of biological entities, where the interaction-profile similarities are calculated and compared, and the modules of genes with similar profiles are defined. RGFSN [17] is an integrated gene functional similarity network established by six different methods and is further refined by the PPI networks to perform the protein complex prediction. However, such methods are limited in giving explanations to the results, as they only conduct the computation of similarity and directly compare the results, lacking reasonability.…”

Section: Introductionmentioning

confidence: 99%

Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network

Zhang

Duan

Zheng

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful.

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Refine gene functional similarity network based on interaction networks

Cited by 8 publications

References 50 publications

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Genetic Functional Similarity Clustering Using CRISPR-Cas9 Knockout Data

Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network

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