Prediction of Plant-Disease Relations Based on Unani Formulas by Network Analysis

Hossain, Shaikh Farhad; Wijaya, Sony Hartono; Huang, Ming; Batubara, Irmanida; Kanaya, Shigehiko; Farhad, Md. Altaf-Ul-Amin

doi:10.1109/bibe.2018.00075

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…In the preliminary step, we collected the metabolite information of medicinal plants used as the composition of the Unani formulas. The Unani data we utilized in this work are the same as the data utilized in previous work [ 3 ]. Actually, these data were collected from the following book: BANGLADESH: National Formulary of Unani Medicine (ISBN 978-984-33-3253-0).…”

Section: Methodsmentioning

confidence: 99%

“…The main reasons why people tend to choose herbal medicines are because herbal medicines provide better efficacy and relatively lower side effects compared to conventional drugs [ 1 ]. The use of herbal medicines throughout the world reached USD 60 billion in 2010 and is expected to reach USD 5 trillion by 2050 [ 2 , 3 ]. This information shows that the use of herbal medicines is prevalent throughout the world.…”

Section: Introductionmentioning

confidence: 99%

“…This is needed to provide a foundation and knowledge as to why an Unani formula is useful for a particular disease. Unani medicines are made by the extraction of medicinal plants that are used as drugs against various diseases [ 3 ]. Based on [ 5 ], the Unani System of Medicine was invented in Greece and refined by Arabs into a sophisticated medical discipline using the framework of Hippocrates’ and Jalinoos’ teachings (Galen).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

Wijaya

Nasution

Batubara

et al. 2023

Life

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The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

Wijaya

Nasution

Batubara

et al. 2023

Life

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Implementation of BiClusO and its comparison with other biclustering algorithms

2019

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This paper describes the implementation of biclustering algorithm BiClusO using graphical user interface and different parameters to generate overlapping biclusters from a binary sparse matrix. We compare our algorithm with several other biclustering algorithms in the context of two different types of biological datasets and four synthetic datasets with known embedded biclusters. Biclustering technique is widely used in different fields of studies for analyzing bipartite relationship dataset. Over the past decade, different biclustering algorithms have been proposed by researchers which are mainly used for biological data analysis. The performance of these algorithms differs depending on dataset size, pattern, and property. These issues create difficulties for a researcher to take the right decision for selecting a good biclustering algorithm. Two different scoring methods along with Gene Ontology(GO) term enrichment analysis have been used to measure and compare the performance of our algorithm. Our algorithm shows the best performance over some other well-known biclustering algorithms.

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Investigating Potential Natural Antibiotics Plants Based on Unani Formula Using Supervised Network Analysis and Machine Learning Approach

Nasution,

Ono,

Kanaya

et al. 2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Prediction of Plant-Disease Relations Based on Unani Formulas by Network Analysis

Cited by 4 publications

References 12 publications

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

Implementation of BiClusO and its comparison with other biclustering algorithms

Investigating Potential Natural Antibiotics Plants Based on Unani Formula Using Supervised Network Analysis and Machine Learning Approach

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