Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed.最新文献_第8页

Using TCGAbiolinks package in ranking breast cancer genes from The Cancer Genome Atlas (TCGA) to predict disease-associated genes 利用tcgabiollinks包对癌症基因组图谱(TCGA)中的乳腺癌基因进行排序，预测疾病相关基因

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-11-06 DOI: 10.3390/mol2net-07-11601

Ngan Vu, U. Bui

引用次数: 0

Multi-objective screening of non-small cell lung cancer drug candidates 非小细胞肺癌候选药物的多目标筛选

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-11-05 DOI: 10.3390/mol2net-07-11597

N. Nguyen, N. Tran, Quang Le, O. Nguyen, Hai Pham

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Hydroponic green forage an alternative for feeding animals of zootechnical interest on a small and medium scale 水培绿色草料是一种替代饲料动物技术的兴趣在中小型规模

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-29 DOI: 10.3390/mol2net-07-11238

Santiago Aguiar, C. Fernández, Mishel Cujilema

{"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}

引用次数: 0

Management of the deep bedding system in pig farming: An alternative to improve production and animal welfare in the Ecuadorian Amazon 养猪业中深层垫层系统的管理:厄瓜多尔亚马逊地区提高生产和动物福利的另一种选择

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-28 DOI: 10.3390/mol2net-07-11236

Santiago Aguiar, Diego Ramos, Victor Zhunaula

引用次数: 0

Self-explanatory neural models, part 2 自我解释的神经模型，第2部分

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-28 DOI: 10.3390/mol2net-07-11237

Zakaria Rguibi, A. Hajami, Dya Zitouni

引用次数: 0

Applications of Machine Learning in drug discovery and development 机器学习在药物发现和开发中的应用

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-27 DOI: 10.3390/mol2net-07-11234

Ane Ibáñez Antolín

引用次数: 0

Effect of the re-growth age on the primary metabolites of Tithonia diversifolia, part 2: Sugars metabolism. 再生年龄对山楂初级代谢产物的影响，第二部分:糖代谢。

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-26 DOI: 10.3390/mol2net-07-11228

M. Paumier, D. Verdecia, H. Uvidia, Jorge Ramirez, R. Herrera, Jhoeel Uvidia, Edgar Chicaiza, Á. Santana

引用次数: 0

Critical essay on predictive models for anti-sarcoma compounds 关于抗肉瘤化合物预测模型的评论文章

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-26 DOI: 10.3390/mol2net-07-11231

Bernabé Ortega-Tenezaca

{"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}

引用次数: 0

Effects of Serum 25-Hydroxyvitamin D concentration on Insulin Resistance and IVF-ET outcomes in PCOS 血清25-羟基维生素D浓度对PCOS患者胰岛素抵抗和IVF-ET结局的影响

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-26 DOI: 10.3390/mol2net-07-11226

Kun Liu, César Plagaro, Bairong Shen

引用次数: 0

Machine Learning Analysis of α-amylase Inhibitors α-淀粉酶抑制剂的机器学习分析

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed. Pub Date : 2021-10-26 DOI: 10.3390/mol2net-07-11229

K. Diéguez-Santana, B. Rasulev

引用次数: 0