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Francisco M. Couto - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets
    2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Abstract The large, and increasing, number of Chemical Compounds poses challenges to the exploration of such datasets. In this work, we propose the usage of Recommender Systems to identify Compounds of interest to scientific researchers. Our approach consists of a hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, using the semantic similarity between the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The hybrid model was able to improve the results of the collaborative-filtering algorithms, by more than ten percentage points in most of the assessed evaluation metrics.

  • Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets
    2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Abstract The large, and increasing, number of Chemical Compounds poses challenges to the exploration of such datasets. In this work, we propose the use of Recommender Systems in the selection of Compounds of interest to scientific researchers. Our approach consists of a Hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than ten percentage points in most of the assessed evaluation metrics.

  • Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets
    2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Abstract The increasing number of Chemical Compounds is a challenge for the researchers to explore such datasets. In this work, we propose the use of Recommender Systems in the exploration of new Chemical Compounds of interest to scientific researchers. Our approach consists in a Hybrid recommender model suitable for implicit feedback datasets and focused in retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than 10 percentage points in most of the assessed evaluation metrics.

  • ECIR (2) - Hybrid Semantic Recommender System for Chemical Compounds.
    Lecture Notes in Computer Science, 2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking (BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.

  • Hybrid Semantic Recommender System for Chemical Compounds
    arXiv: Information Retrieval, 2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.

Tatsuya Akutsu - One of the best experts on this subject based on the ideXlab platform.

  • Enumeration method for tree-like Chemical Compounds with benzene rings and naphthalene rings by breadth-first search order
    BMC Bioinformatics, 2016
    Co-Authors: Jira Jindalertudomdee, Morihiro Hayashida, Yang Zhao, Tatsuya Akutsu
    Abstract:

    Background Drug discovery and design are important research fields in bioinformatics. Enumeration of Chemical Compounds is essential not only for the purpose, but also for analysis of Chemical space and structure elucidation. In our previous study, we developed enumeration methods BfsSimEnum and BfsMulEnum for tree-like Chemical Compounds using a tree-structure to represent a Chemical compound, which is limited to acyclic Chemical Compounds only.

  • Enumeration method for tree-like Chemical Compounds with benzene rings and naphthalene rings by breadth-first search order
    BMC Bioinformatics, 2016
    Co-Authors: Jira Jindalertudomdee, Morihiro Hayashida, Yang Zhao, Tatsuya Akutsu
    Abstract:

    Background Drug discovery and design are important research fields in bioinformatics. Enumeration of Chemical Compounds is essential not only for the purpose, but also for analysis of Chemical space and structure elucidation. In our previous study, we developed enumeration methods BfsSimEnum and BfsMulEnum for tree-like Chemical Compounds using a tree-structure to represent a Chemical compound, which is limited to acyclic Chemical Compounds only. Results In this paper, we extend the methods, and develop BfsBenNaphEnum that can enumerate tree-like Chemical Compounds containing benzene rings and naphthalene rings, which include benzene isomers and naphthalene isomers such as ortho, meta, and para, by treating a benzene ring as an atom with valence six, instead of a ring of six carbon atoms, and treating a naphthalene ring as two benzene rings having a special bond. We compare our method with MOLGEN 5.0, which is a well-known general purpose structure generator, to enumerate Chemical structures from a set of Chemical formulas in terms of the number of enumerated structures and the computational time. The result suggests that our proposed method can reduce the computational time efficiently. Conclusions We propose the enumeration method BfsBenNaphEnum for tree-like Chemical Compounds containing benzene rings and naphthalene rings as cyclic structures. BfsBenNaphEnum was from 50 times to 5,000,000 times faster than MOLGEN 5.0 for instances with 8 to 14 carbon atoms in our experiments.

  • Parallelization of enumerating tree-like Chemical Compounds by breadth-first search order.
    BMC Medical Genomics, 2015
    Co-Authors: Morihiro Hayashida, Jira Jindalertudomdee, Yang Zhao, Tatsuya Akutsu
    Abstract:

    Enumeration of Chemical Compounds greatly assists designing and finding new drugs, and determining Chemical structures from mass spectrometry. In our previous study, we developed efficient algorithms, BfsSimEnum and BfsMulEnum for enumerating tree-like Chemical Compounds without and with multiple bonds, respectively. For many instances, our previously proposed algorithms were able to enumerate Chemical structures faster than other existing methods.

  • Kernel Methods for Chemical Compounds: From Classification to Design
    IEICE Transactions on Information and Systems, 2011
    Co-Authors: Tatsuya Akutsu, Hiroshi Nagamochi
    Abstract:

    In this paper, we briefly review kernel methods for analysis of Chemical Compounds with focusing on the authors' works. We begin with a brief review of existing kernel functions that are used for classification of Chemical Compounds and prediction of their activities. Then, we focus on the pre-image problem for Chemical Compounds, which is to infer a Chemical structure that is mapped to a given feature vector, and has a potential application to design of novel Chemical Compounds. In particular, we consider the pre-image problem for feature vectors consisting of frequencies of labeled paths of length at most K. We present several time complexity results that include: NP-hardness result for a general case, polynomial time algorithm for tree structured Compounds with fixed K, and polynomial time algorithm for K=1 based on graph detachment. Then we review practical algorithms for the pre-image problem, which are based on enumeration of Chemical structures satisfying given constraints. We also briefly review related results which include efficient enumeration of stereoisomers of tree-like Chemical Compounds and efficient enumeration of outerplanar graphs.

Jira Jindalertudomdee - One of the best experts on this subject based on the ideXlab platform.

  • Enumeration method for tree-like Chemical Compounds with benzene rings and naphthalene rings by breadth-first search order
    BMC Bioinformatics, 2016
    Co-Authors: Jira Jindalertudomdee, Morihiro Hayashida, Yang Zhao, Tatsuya Akutsu
    Abstract:

    Background Drug discovery and design are important research fields in bioinformatics. Enumeration of Chemical Compounds is essential not only for the purpose, but also for analysis of Chemical space and structure elucidation. In our previous study, we developed enumeration methods BfsSimEnum and BfsMulEnum for tree-like Chemical Compounds using a tree-structure to represent a Chemical compound, which is limited to acyclic Chemical Compounds only.

  • Enumeration method for tree-like Chemical Compounds with benzene rings and naphthalene rings by breadth-first search order
    BMC Bioinformatics, 2016
    Co-Authors: Jira Jindalertudomdee, Morihiro Hayashida, Yang Zhao, Tatsuya Akutsu
    Abstract:

    Background Drug discovery and design are important research fields in bioinformatics. Enumeration of Chemical Compounds is essential not only for the purpose, but also for analysis of Chemical space and structure elucidation. In our previous study, we developed enumeration methods BfsSimEnum and BfsMulEnum for tree-like Chemical Compounds using a tree-structure to represent a Chemical compound, which is limited to acyclic Chemical Compounds only. Results In this paper, we extend the methods, and develop BfsBenNaphEnum that can enumerate tree-like Chemical Compounds containing benzene rings and naphthalene rings, which include benzene isomers and naphthalene isomers such as ortho, meta, and para, by treating a benzene ring as an atom with valence six, instead of a ring of six carbon atoms, and treating a naphthalene ring as two benzene rings having a special bond. We compare our method with MOLGEN 5.0, which is a well-known general purpose structure generator, to enumerate Chemical structures from a set of Chemical formulas in terms of the number of enumerated structures and the computational time. The result suggests that our proposed method can reduce the computational time efficiently. Conclusions We propose the enumeration method BfsBenNaphEnum for tree-like Chemical Compounds containing benzene rings and naphthalene rings as cyclic structures. BfsBenNaphEnum was from 50 times to 5,000,000 times faster than MOLGEN 5.0 for instances with 8 to 14 carbon atoms in our experiments.

  • Parallelization of enumerating tree-like Chemical Compounds by breadth-first search order.
    BMC Medical Genomics, 2015
    Co-Authors: Morihiro Hayashida, Jira Jindalertudomdee, Yang Zhao, Tatsuya Akutsu
    Abstract:

    Enumeration of Chemical Compounds greatly assists designing and finding new drugs, and determining Chemical structures from mass spectrometry. In our previous study, we developed efficient algorithms, BfsSimEnum and BfsMulEnum for enumerating tree-like Chemical Compounds without and with multiple bonds, respectively. For many instances, our previously proposed algorithms were able to enumerate Chemical structures faster than other existing methods.

Naoyuki Kataoka - One of the best experts on this subject based on the ideXlab platform.

  • Modulation of Abnormal Splicing of RNA Diseases by Small Chemical Compounds
    Applied RNA Bioscience, 2018
    Co-Authors: Naoyuki Kataoka
    Abstract:

    Pre-mRNA splicing is a critical step for protein gene expression in higher eukaryotes. Another mode of splicing, termed alternative splicing, contributes to diversity of the expressed proteins from the limited number of genes in chromosomes. Those steps are highly regulated and must be accurate. Therefore, disruption of splicing regulation often results in hereditary and sporadic diseases called as “RNA diseases” in human. Modulation of splicing by small Chemical Compounds and nucleic acids has been targeting aberrant splicing in those diseases. In this chapter, I will introduce several RNA diseases and splicing-target therapeutic approaches with Chemical Compounds. Unveiling molecular mechanism and correction of aberrant splicing by small Chemical Compounds are important for both RNA biologists and clinicians who desire therapies for those diseases.

  • Modulation of aberrant splicing in human RNA diseases by Chemical Compounds.
    Human Genetics, 2017
    Co-Authors: Naoyuki Kataoka
    Abstract:

    Pre-mRNA splicing is an essential step for gene expression in higher eukaryotes. Alternative splicing contributes to diversity of the expressed proteins from the limited number of genes. Disruption of splicing regulation often results in hereditary and sporadic diseases called as ‘RNA diseases’. Modulation of splicing by small Chemical Compounds and nucleic acids has been tried to target aberrant splicing in those diseases. Several RNA diseases and splicing-target therapeutic approaches will be briefly introduced in this review. Accumulating knowledge about molecular mechanism of aberrant splicing and their correction by Chemical Compounds is important not only for RNA biologists, but also for clinicians who desire therapies for those diseases.

M. Barros - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets
    2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Abstract The large, and increasing, number of Chemical Compounds poses challenges to the exploration of such datasets. In this work, we propose the usage of Recommender Systems to identify Compounds of interest to scientific researchers. Our approach consists of a hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, using the semantic similarity between the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The hybrid model was able to improve the results of the collaborative-filtering algorithms, by more than ten percentage points in most of the assessed evaluation metrics.

  • Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets
    2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Abstract The large, and increasing, number of Chemical Compounds poses challenges to the exploration of such datasets. In this work, we propose the use of Recommender Systems in the selection of Compounds of interest to scientific researchers. Our approach consists of a Hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than ten percentage points in most of the assessed evaluation metrics.

  • Hybrid Semantic Recommender System for Chemical Compounds in Large-Scale Datasets
    2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Abstract The increasing number of Chemical Compounds is a challenge for the researchers to explore such datasets. In this work, we propose the use of Recommender Systems in the exploration of new Chemical Compounds of interest to scientific researchers. Our approach consists in a Hybrid recommender model suitable for implicit feedback datasets and focused in retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than 10 percentage points in most of the assessed evaluation metrics.

  • ECIR (2) - Hybrid Semantic Recommender System for Chemical Compounds.
    Lecture Notes in Computer Science, 2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking (BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.

  • Hybrid Semantic Recommender System for Chemical Compounds
    arXiv: Information Retrieval, 2020
    Co-Authors: M. Barros, André Moitinho, Francisco M. Couto
    Abstract:

    Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.