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Biomedical Data Mining for Information Retrieval


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to classify compounds using relationship criteria between their chemical structure and toxicity profiles [111]. The comparative study of ML algorithms shows that non-linear/ensemble-based classification algorithms are more successful in classifying the compounds using ADMET properties. Random Forest algorithms can also be used in ligand pose prediction, finding receptor-ligand interactions and predicting the efficiency of docking simulations [112]. Nowadays, Deep Learning (DL) methods are achieving remarkable success in the area of pharmaceutical research starting from biological-image analysis, de novo molecule design, ligand– receptor interaction to biological activity prediction [113]. So the continuous improvements in machine learning and deep learning algorithms will help to achieve desired results with higher prediction accuracy in the drug designing field.

      Later on, several substructure mining algorithms have been developed to accommodate the needs of an ever-changing drug discovery process [117]. The subgraph mining approach is unique as it is free from any kind of arbitrary assumption, compared to other approaches. In other words, the current subgraph mining techniques are capable of retrieving all frequent occurring subgraphs from a given database of chemical compounds in significantly less time with minimum support [118]. Furthermore, as described above, the idea behind these techniques is to enable us to find the most significant subgraph out of all possible subgraphs. Shortly, the use of Artificial intelligence-based techniques in medicinal chemistry will become more complex, due to the increasing availability of huge repositories containing chemical, biological, genetic, and structural data. The implementation of the complex algorithm on ever-increasing data volume for searching a new, safer and more effective drug candidates leads to the use of quantum computing and high-performance computing. In summary, we believe that these techniques will become a much more significant part of drug discovery endeavours within a very short time.

      Machine learning has revolutionized the field of biology and medicine where researchers have employed machine learning to make gene chips more practical and useful. Data that might have taken years to collect, now takes a week. Biologist are aided greatly by the supervised and unsupervised learning methods that many are using to make sense of the large amount of data now available to them. As a result a rapid increase has occurred in the rate at which biologists are able to understand the molecular processes that underlie and govern the function of biological systems which can be used for a variety of important medical applications such as diagnosis, prognosis, and drug response. As our vast amount of genomic and similar types of data continues to grow, the role of computational techniques, especially machine learning, will grow with it. These algorithms will enable us to handle the task of analyzing this data to yield valuable insight into the biological systems that surround us and the diseases that affect us.

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