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In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug - like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with...
Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously...
Action rules are built from atomic expressions called atomic action terms and they describe possible transitions of objects from one state to another. They involve changes of values within one decision attribute. Association action rule is similar to an action rule but it may refer to changes of values involving several attributes listed in its decision part. Action paths are defined as sequences...
In this paper, a drug knowledge platform based on intelligent information technology is constructed. Unlike the traditional virtual drug compound library which focus on the patents of drug only, the aim of the new platform is to design a system including information retrieval, information extraction, construction of drug compound and drug ontology, structure based virtual screening, and text classification...
Machine learning and statistical techniques applied to gene expression data have been used to address the questions of distinguishing tumor morphology, predicting post treatment outcome, and finding molecular markers for disease. Today the classification of different morphologies, lineages and cell histologies can be performed successfully in many instances. The performance in predicting treatment...
Oral anticoagulation therapy, largely performed by warfarin-based drugs, is commonly used for patients with a high risk of blood clotting which can lead to stroke or thrombosis. The state of the patient, with respect to anticoagulation, is captured by the index INR, which is to be kept within a therapeutic range. The patients' response is marked by high interindividual and inter-temporal variability,...
Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful...
Motivated by the numerous applications of analysing opinions in multi-domain scenarios, this paper studies the potential of a still rarely considered approach to the problem of multi-domain sentiment analysis based on Senti-WordNet as lexical resource. SentiWordNet scores are exploited together with additional features to assign a polarity to a text using machine learning. On the other hand, a rule-based...
In the viewpoint of sharing knowledge, users need to input keywords before the agent retrievals the related information from the Internet. At the same time, the traditional method ignored the true meaning of the terms. However, the semantic Web has been created to improve the disadvantage. Ontology is the fundamental element of the semantic Web that is a kind of knowledge presentation and can present...
Against the problem of medicine inventories accidental damage, a practical algorithm for balanced restoration is proposed. Thinking about the actual needs of the system, the method of medicine classification is presented too. The corresponding data structure is designed, which is realized in the system. The algorithm resolves the key issue of inventory maintenance and guaranteed the system's successful...
In this paper, we have proposed a fuzzy rule-based classifier for assigning amino acid sequences into different superfamilies of proteins. While the most popular methods for protein classification rely on sequence alignment, our approach is alignment-free and so more human readable. It accounts for the distribution of contiguous patterns of n amino acids ( n-grams) in the sequences as features, alike...
To discriminate the quality on traditional Chinese medicines Eucommia Bark real-time, according to the characters of Eucommia Bark finger printer, the basic concepts of rough set are introduced briefly. For rough sets can only deal with discrete data, the discretization of data is the key factor in the rough sets applied in quality assessment, we present a method of discretization based on cluster...
Graph data mining algorithms are increasingly applied to biological graph dataset. However, while existing graph mining algorithms can identify frequently occurring sub-graphs, these do not necessarily represent useful patterns. In this paper, we propose a novel graph mining algorithm, MIGDAC (Mining Graph DAta for Classification), that applies graph theory and an interestingness measure to discover...
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