Pub Date : 2021-11-06DOI: 10.3390/mol2net-07-11601
Ngan Vu, U. Bui
{"title":"Using TCGAbiolinks package in ranking breast cancer genes from The Cancer Genome Atlas (TCGA) to predict disease-associated genes","authors":"Ngan Vu, U. Bui","doi":"10.3390/mol2net-07-11601","DOIUrl":"https://doi.org/10.3390/mol2net-07-11601","url":null,"abstract":"","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131129887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-05DOI: 10.3390/mol2net-07-11597
N. Nguyen, N. Tran, Quang Le, O. Nguyen, Hai Pham
{"title":"Multi-objective screening of non-small cell lung cancer drug candidates","authors":"N. Nguyen, N. Tran, Quang Le, O. Nguyen, Hai Pham","doi":"10.3390/mol2net-07-11597","DOIUrl":"https://doi.org/10.3390/mol2net-07-11597","url":null,"abstract":"","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121583673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-29DOI: 10.3390/mol2net-07-11238
Santiago Aguiar, C. Fernández, Mishel Cujilema
. The cost of feeding represents the largest item in the production of animals of zootechnical interest, in this sense, recent research has focused on the use of alternative foods that can compete with conventional raw materials in quantity, quality and price. The objective of this work was to analyze scientific information on the current situation of the use of hydroponic green forage for feeding animals of zootechnical interest on a small and medium scale. The present investigation was exploratory and was based on an updated bibliographic compilation. The introduction of hydroponic green forage of sorghum, corn, wheat and barley in the diet of pigs, poultry, rabbits, guinea pigs, allows to reduce the cost of feeding, it is easy to produce, it is grown all year round, it requires little space, it presents high nutrient utilization coefficients. In addition, it allows to achieve a significant increase in the increase of weight, consumption, weight gain and feed conversion of the animals. The production of hydroponic green forage is an easy technique to apply and does not require a high initial investment for its implementation in the feeding systems for animals in small and medium scale farms.
{"title":"Hydroponic green forage an alternative for feeding animals of zootechnical interest on a small and medium scale","authors":"Santiago Aguiar, C. Fernández, Mishel Cujilema","doi":"10.3390/mol2net-07-11238","DOIUrl":"https://doi.org/10.3390/mol2net-07-11238","url":null,"abstract":". The cost of feeding represents the largest item in the production of animals of zootechnical interest, in this sense, recent research has focused on the use of alternative foods that can compete with conventional raw materials in quantity, quality and price. The objective of this work was to analyze scientific information on the current situation of the use of hydroponic green forage for feeding animals of zootechnical interest on a small and medium scale. The present investigation was exploratory and was based on an updated bibliographic compilation. The introduction of hydroponic green forage of sorghum, corn, wheat and barley in the diet of pigs, poultry, rabbits, guinea pigs, allows to reduce the cost of feeding, it is easy to produce, it is grown all year round, it requires little space, it presents high nutrient utilization coefficients. In addition, it allows to achieve a significant increase in the increase of weight, consumption, weight gain and feed conversion of the animals. The production of hydroponic green forage is an easy technique to apply and does not require a high initial investment for its implementation in the feeding systems for animals in small and medium scale farms.","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116788267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-28DOI: 10.3390/mol2net-07-11236
Santiago Aguiar, Diego Ramos, Victor Zhunaula
{"title":"Management of the deep bedding system in pig farming: An alternative to improve production and animal welfare in the Ecuadorian Amazon","authors":"Santiago Aguiar, Diego Ramos, Victor Zhunaula","doi":"10.3390/mol2net-07-11236","DOIUrl":"https://doi.org/10.3390/mol2net-07-11236","url":null,"abstract":"","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-28DOI: 10.3390/mol2net-07-11237
Zakaria Rguibi, A. Hajami, Dya Zitouni
{"title":"Self-explanatory neural models, part 2","authors":"Zakaria Rguibi, A. Hajami, Dya Zitouni","doi":"10.3390/mol2net-07-11237","DOIUrl":"https://doi.org/10.3390/mol2net-07-11237","url":null,"abstract":"","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123587680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-27DOI: 10.3390/mol2net-07-11234
Ane Ibáñez Antolín
{"title":"Applications of Machine Learning in drug discovery and development","authors":"Ane Ibáñez Antolín","doi":"10.3390/mol2net-07-11234","DOIUrl":"https://doi.org/10.3390/mol2net-07-11234","url":null,"abstract":"","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128998637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-26DOI: 10.3390/mol2net-07-11228
M. Paumier, D. Verdecia, H. Uvidia, Jorge Ramirez, R. Herrera, Jhoeel Uvidia, Edgar Chicaiza, Á. Santana
.
．
{"title":"Effect of the re-growth age on the primary metabolites of Tithonia diversifolia, part 2: Sugars metabolism.","authors":"M. Paumier, D. Verdecia, H. Uvidia, Jorge Ramirez, R. Herrera, Jhoeel Uvidia, Edgar Chicaiza, Á. Santana","doi":"10.3390/mol2net-07-11228","DOIUrl":"https://doi.org/10.3390/mol2net-07-11228","url":null,"abstract":".","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131843590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-26DOI: 10.3390/mol2net-07-11231
Bernabé Ortega-Tenezaca
. Today, studies are performed from a dataset spanning multiple preclinical assays and different experimental conditions for sarcomas. PTML is a tool that combines Machine Learning (ML) algorithms and Perturbation Theory (PT) principles. With PTML, ML techniques can be used to predict antisarcoma compounds. At the same time, different PT techniques can be applied. One of the most widely used ML techniques is the neural network which showed high accuracy for both training and model validation. It is important to emphasize that the production of the most optimal model would save resources in the pharmaceutical industries. In a recent paper Cabrera et al . reported a new model for prediction of anti-sarcoma compounds. The model is very interesting because it can predict the biological activity vs multiple proteins, etc. The authors also explored multiple molecular descriptors of drugs as well as many assay conditions like protein target, cell line, etc. There are some suggestions we can make to improve future versions of this paper. For instance, the authors could calculate also sequence descriptor of target proteins to predict the results for new mutants. On my opinion, it could be very interesting developing a user-friendly software for use of non-expert medicinal chemists. This software could be a desktop or online server application increasing the use of the model worldwide. Another interesting step could be the fusion of the present pre-clinical data with clinical data including variables of patients or population groups. In all case, the paper is very interesting an opens new gates to the authors for future works including new features to the design of antisarcoma compounds.
{"title":"Critical essay on predictive models for anti-sarcoma compounds","authors":"Bernabé Ortega-Tenezaca","doi":"10.3390/mol2net-07-11231","DOIUrl":"https://doi.org/10.3390/mol2net-07-11231","url":null,"abstract":". Today, studies are performed from a dataset spanning multiple preclinical assays and different experimental conditions for sarcomas. PTML is a tool that combines Machine Learning (ML) algorithms and Perturbation Theory (PT) principles. With PTML, ML techniques can be used to predict antisarcoma compounds. At the same time, different PT techniques can be applied. One of the most widely used ML techniques is the neural network which showed high accuracy for both training and model validation. It is important to emphasize that the production of the most optimal model would save resources in the pharmaceutical industries. In a recent paper Cabrera et al . reported a new model for prediction of anti-sarcoma compounds. The model is very interesting because it can predict the biological activity vs multiple proteins, etc. The authors also explored multiple molecular descriptors of drugs as well as many assay conditions like protein target, cell line, etc. There are some suggestions we can make to improve future versions of this paper. For instance, the authors could calculate also sequence descriptor of target proteins to predict the results for new mutants. On my opinion, it could be very interesting developing a user-friendly software for use of non-expert medicinal chemists. This software could be a desktop or online server application increasing the use of the model worldwide. Another interesting step could be the fusion of the present pre-clinical data with clinical data including variables of patients or population groups. In all case, the paper is very interesting an opens new gates to the authors for future works including new features to the design of antisarcoma compounds.","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130851542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-26DOI: 10.3390/mol2net-07-11226
Kun Liu, César Plagaro, Bairong Shen
Abstract
摘要
{"title":"Effects of Serum 25-Hydroxyvitamin D concentration on Insulin Resistance and IVF-ET outcomes in PCOS","authors":"Kun Liu, César Plagaro, Bairong Shen","doi":"10.3390/mol2net-07-11226","DOIUrl":"https://doi.org/10.3390/mol2net-07-11226","url":null,"abstract":"Abstract","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115952925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-26DOI: 10.3390/mol2net-07-11229
K. Diéguez-Santana, B. Rasulev
{"title":"Machine Learning Analysis of α-amylase Inhibitors","authors":"K. Diéguez-Santana, B. Rasulev","doi":"10.3390/mol2net-07-11229","DOIUrl":"https://doi.org/10.3390/mol2net-07-11229","url":null,"abstract":"","PeriodicalId":136053,"journal":{"name":"Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126932076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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