scholarly journals FDMine: a graph mining approach to predict and evaluate food-drug interactions

2022 ◽  
Author(s):  
Md Mostafizur Rahman ◽  
Srinivas Mukund Vadrev ◽  
Arturo Magana-Mora ◽  
Jacob Levman ◽  
Othman Soufan
Keyword(s):  
Drug Interactions ◽  
Graph Mining ◽  
Characteristic Curve ◽  
Ground Truth ◽  
Food Composition ◽  
Accuracy Evaluation ◽  
Chemical Structures ◽  
Food Items ◽  
Related Drug ◽  
Drug Similarity

Abstract Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

scholarly journals FDMine: a graph mining approach to predict and evaluate food-drug interactions

2021 ◽  
Author(s):  
Md Mostafizur Rahman ◽  
Srinivas Mukund Vadrev ◽  
Arturo Magana-Mora ◽  
Jacob Levman ◽  
Othman Soufan
Keyword(s):  
Drug Interactions ◽  
Graph Mining ◽  
Characteristic Curve ◽  
Ground Truth ◽  
Recovery Process ◽  
Food Composition ◽  
Accuracy Evaluation ◽  
Chemical Structures ◽  
Food Items ◽  
Drug Similarity

Abstract Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. These interactions can create unexpected adverse pharmacological effects. By contrast, particular foods can aid in the recovery process of a patient. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

Food and drugs

2002 ◽  
Vol 55 (1-2) ◽  
pp. 5-12 ◽  
Author(s):  
Kornelija Djakovic-Svajcer
Keyword(s):  
Plasma Level ◽  
Drug Metabolism ◽  
Drug Interactions ◽  
Therapeutic Effect ◽  
Significant Influence ◽  
Plasma Concentrations ◽  
Chemical Properties ◽  
Therapeutic Index ◽  
Food Composition ◽  
Drug Bioavailability

Food can exert a significant influence on the effects of certain drugs. The interactions between food and drugs can be pharmacokinetic and pharmacodynamic. Pharmacokinetic interactions most often take place on absorption and drug metabolism levels. Absorption can be either accelerated or delayed, increased or decreased, while drug metabolism can be either stimulated or inhibited. The factors which influence food-drug interactions are as follows: composition and physic-chemical properties of drugs, the interval between a meal and drug intake and food composition. Food consistency is of lesser influence on drug bioavailability than food composition (proteins, fats, carbohydrates, cereals). Important interactions can occur during application of drugs with low therapeutic index, whereby the plasma level significantly varies due to changes in resorption or metabolism (e.g. digoxin, theophyllin, cyclosporin) and drugs such as antibiotics, whose proper therapeutic effect requires precise plasma concentrations.

scholarly journals The Design and Development of a Food Composition Database for an Electronic Tool to Assess Food Intake in New Caledonian Families

Nutrients ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1668
Author(s):  
Juliana Chen ◽  
Solène Bertrand ◽  
Olivier Galy ◽  
David Raubenheimer ◽  
Margaret Allman-Farinelli ◽  
...  
Keyword(s):  
New Caledonia ◽  
Phase 1 ◽  
Saturated Fat ◽  
Food Composition ◽  
Mobile App ◽  
Portion Size ◽  
Cross Sectional ◽  
Food Items ◽  
Specific Food ◽  
Country Specific

The food environment in New Caledonia is undergoing a transition, with movement away from traditional diets towards processed and discretionary foods and beverages. This study aimed to develop an up-to-date food composition database that could be used to analyze food and nutritional intake data of New Caledonian children and adults. Development of this database occurred in three phases: Phase 1, updating and expanding the number of food items to represent current food supply; Phase 2, refining the database items and naming and assigning portion size images for food items; Phase 3, ensuring comprehensive nutrient values for all foods, including saturated fat and total sugar. The final New Caledonian database comprised a total of 972 food items, with 40 associated food categories and 25 nutrient values and 615 items with portion size images. To improve the searchability of the database, the names of 593 food items were shortened and synonyms or alternate spelling were included for 462 foods. Once integrated into a mobile app-based multiple-pass 24-h recall tool, named iRecall.24, this country-specific food composition database would support the assessment of food and nutritional intakes of families in New Caledonia, in a cross-sectional and longitudinal manner, and with translational opportunities for use across the wider Pacific region.

Can Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces

2022 ◽  
Vol 29 (2) ◽  
pp. 1-33
Author(s):  
Nigel Bosch ◽  
Sidney K. D'Mello
Keyword(s):  
Operating Characteristic ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Ground Truth ◽  
Mind Wandering ◽  
High Stakes ◽  
Machine Learning Model ◽  
Improve Accuracy ◽  
Comparable Accuracy ◽  
Operating Characteristic Curve

The ability to identify whether a user is “zoning out” (mind wandering) from video has many HCI (e.g., distance learning, high-stakes vigilance tasks). However, it remains unknown how well humans can perform this task, how they compare to automatic computerized approaches, and how a fusion of the two might improve accuracy. We analyzed videos of users’ faces and upper bodies recorded 10s prior to self-reported mind wandering (i.e., ground truth) while they engaged in a computerized reading task. We found that a state-of-the-art machine learning model had comparable accuracy to aggregated judgments of nine untrained human observers (area under receiver operating characteristic curve [AUC] = .598 versus .589). A fusion of the two (AUC = .644) outperformed each, presumably because each focused on complementary cues. Furthermore, adding more humans beyond 3–4 observers yielded diminishing returns. We discuss implications of human–computer fusion as a means to improve accuracy in complex tasks.

Review of Cimetidine Drug Interactions

1983 ◽  
Vol 17 (2) ◽  
pp. 110-120 ◽  
Author(s):  
Eugene M. Sorkin ◽  
Diane L. Darvey
Keyword(s):  
Blood Flow ◽  
Drug Interactions ◽  
Hepatic Blood Flow ◽  
Hepatic Clearance ◽  
Hepatic Metabolism ◽  
Rapid Onset ◽  
Related Drug ◽  
Cytochrome P 450 ◽  
Acid Labile ◽  
Hepatic Cytochrome

The literature on cimetidine drug interactions has been thoroughly reviewed. Several different mechanisms have been proposed for cimetidine-related drug interactions. These mechanisms include: (1) impaired hepatic drug metabolism due to inhibition of hepatic microsomal enzymes, (2) reduced hepatic blood flow, resulting in decreased clearance of drugs that are highly extracted by the liver, (3) increased potential for myelosuppression when administered concurrently with other drugs capable of causing myelosuppression, and (4) altered bioavailability of acid-labile drugs. Cimetidine binds reversibly to the hepatic cytochrome P-450 and P-448 systems, resulting in decreased metabolism of drugs that undergo Phase I reactions (e.g., dealkylation and hydroxylation). In contrast, glucuronidation pathways are unaffected. The rapid onset and reversal of cimetidine's inhibition of hepatic metabolism indicates an effect on hepatic enzyme systems. Cimetidine also has been reported to decrease hepatic blood flow. Drugs that are highly extracted by the liver, such as propranolol, lidocaine, and morphine, may be postulated to have a decreased hepatic clearance. Cimetidine, through its effect on gastric pH, may increase the absorption of acid-labile drugs or may decrease the absorption of drugs. There have been reports of increased potential for myelosuppression when cimetidine is administered concurrently with drugs capable of causing bone marrow suppression. An understanding of the mechanisms involved in cimetidine drug interactions allows the clinician to prevent and predict these interactions.

Potential Aspirin-Related Drug Interactions in the Elderly

DICP ◽  
1989 ◽  
Vol 23 (4) ◽  
pp. 340-340
Author(s):  
Arthur Schwartz ◽  
Sybil N.E. Seoka
Keyword(s):  
Drug Interactions ◽  
The Elderly ◽  
Related Drug

scholarly journals Differences in nutrient and energy contents of commonly consumed dishes prepared in restaurants v. at home in Hunan Province, China

2018 ◽  
Vol 21 (7) ◽  
pp. 1307-1318 ◽  
Author(s):  
Xiaofang Jia ◽  
Jiawu Liu ◽  
Bo Chen ◽  
Donghui Jin ◽  
Zhongxi Fu ◽  
...  
Keyword(s):  
Fast Food ◽  
Regression Models ◽  
Food Choices ◽  
Food Composition ◽  
Hunan Province ◽  
Additional Contribution ◽  
Food Items ◽  
Total Fat ◽  
Composite Samples ◽  
At Home

AbstractObjectiveEating away from home is associated with poor diet quality, in part due to less healthy food choices and larger portions. However, few studies account for the potential additional contribution of differences in food composition between restaurant- and home-prepared dishes. The present study aimed to investigate differences in nutrients of dishes prepared in restaurants v. at home.DesignEight commonly consumed dishes were collected in twenty of each of the following types of locations: small and large restaurants, and urban and rural households. In addition, two fast-food items were collected from ten KFC, McDonald’s and food stalls. Five samples per dish were randomly pooled from every location. Nutrients were analysed and energy was calculated in composite samples. Differences in nutrients of dishes by preparation location were determined.SettingHunan Province, China.SubjectsNa, K, protein, total fat, fatty acids, carbohydrate and energy in dishes.ResultsOn average, both the absolute and relative fat contents, SFA and Na:K ratio were higher in dishes prepared in restaurants than households (P < 0·05). Protein was 15 % higher in animal food-based dishes prepared in households than restaurants (P<0·05). Quantile regression models found that, at the 90th quantile, restaurant preparation was consistently negatively associated with protein and positively associated with the percentage of energy from fat in all dishes. Moreover, restaurant preparation also positively influenced the SFA content in dishes, except at the highest quantiles.ConclusionsThese findings suggest that compared with home preparation, dishes prepared in restaurants in China may differ in concentrations of total fat, SFA, protein and Na:K ratio, which may further contribute, beyond food choices, to less healthy nutrient intakes linked to eating away from home.

Automatic detection of various patterns of calcification distribution on mammograms: a graph convolution network approach (Preprint)

2021 ◽  
Author(s):  
Melissa Min-Szu Yao ◽  
Hao Du ◽  
Mikael Hartman ◽  
Wing P. Chan ◽  
Mengling Feng
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Automatic Detection ◽  
Ground Truth ◽  
Architectural Distortion ◽  
Breast Cancers ◽  
Convolutional Network ◽  
Mammographic Images

UNSTRUCTURED Purpose: To develop a novel artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 200 patients classified as Category 4 or 5 according to the American College of Radiology Breast Imaging Reporting and Database System, which showed calcifications according to the mammographic reports and diagnosed breast cancers. The calcification distributions were classified as either diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer as a single or combined characterization such as a mass, asymmetry, or architectural distortion with or without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph convolutional network-based model was developed. 401 mammographic images from 200 cases of breast cancer were divided based on calcification distribution pattern: diffuse (n = 24), regional (n = 111), group (n = 201), linear (n = 8) or segmental (n = 57). The classification performances were measured using metrics including precision, recall, F1 score, accuracy and multi-class area under receiver operating characteristic curve. The proposed achieved precision of 0.483 ± 0.015, sensitivity of 0.606 (0.030), specificity of 0.862 ± 0.018, F1 score of 0.527 ± 0.035, accuracy of 60.642% ± 3.040% and area under the curve of 0.754 ± 0.019, finding method to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. Conclusion: The proposed deep neural network framework is an AI solution to automatically detect and classify calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.

MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization

2020 ◽  
Vol 36 (20) ◽  
pp. 5061-5067
Author(s):  
Ali Akbar Jamali ◽  
Anthony Kusalik ◽  
Fang-Xiang Wu
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Matrix Factorization ◽  
Structural Information ◽  
Superior Performance ◽  
Supplementary Information ◽  
Similarity Matrix ◽  
Interaction Prediction ◽  
Prediction Approach ◽  
Drug Similarity

Abstract Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.

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