Mahmood Amintoosi; Eisa Kohan-Baghkheirati
Abstract
Every year, extensive experimental analysis is conducted to evaluate the anti-cancer properties of plants. Developing a well-ranked list of potential anti-cancer plants based on verified anti-cancer metabolites can significantly reduce the time and cost required for plant evaluation. ...
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Every year, extensive experimental analysis is conducted to evaluate the anti-cancer properties of plants. Developing a well-ranked list of potential anti-cancer plants based on verified anti-cancer metabolites can significantly reduce the time and cost required for plant evaluation. This paper proposes a method for generating such a ranked list by analyzing biological graphs of plant-metabolite interactions. In this approach, graph nodes are ranked based on specific graph features. However, a challenge arises in selecting the most informative graph features that ensure the resulting ranked plant list is more relevant, prioritizing plants with greater anti-cancer properties at the top. To address this challenge, we propose the use of the Average Precision metric commonly used in information retrieval and recommender systems, to compare different ranked lists. By constructing a network that captures the similarities between plants based on their shared metabolites, and ranking plants using different combinations of graph features, we can identify the subset of features that yields a ranked list with a higher Average Precision score. This subset of features can then be considered the most suitable for recommending anti-cancer plants. The proposed method can be used to select the best graph features for screening unverified plant lists for anti-cancer properties, increasing the likelihood of identifying plants with higher scores in the list that possess anti-cancer properties.
Mostafa Boroumandzadeh; Elham Parvinnia; Reza Boostani; Sepideh Sefidbakht
Abstract
Medical decision support systems (MDSS) are designed to assist physicians in making accurate decisions. The required data by MDSS are collected from various resources such as physical examinations and electronic health records (EHR). In this paper, an MDSS framework has ...
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Medical decision support systems (MDSS) are designed to assist physicians in making accurate decisions. The required data by MDSS are collected from various resources such as physical examinations and electronic health records (EHR). In this paper, an MDSS framework has been proposed to diagnose and classify breast cancer patients (DSS-BC). Medical texts reports (MTR) were embedded, and essential feature vectors combined with EHR were extracted using principal component analysis (PCA). A new method based on a fuzzy min-max neural network with hyper box variable expansion coefficient (FMNN-HVEC) was used to determine the molecular subtypes, and the feature vectors were clustered using deep clustering. Also, a new decision fusion algorithm called weighted Yager was proposed based on the F1-Score for each class. This algorithm proposed a mathematical decision fusion technique to determine the Breast Imaging-Reporting and Data System (BI-RADS) and molecular subtypes values with the accuracy of 95.12% and 89.56%, respectively.
