Increasing online shop revenues with web scraping: a case study for the wine sector

Purpose – Wine has been produced for thousands of years and nowadays we have seen a spread in the wine culture. E-commerce sales of wine have increased considerably and online customer’s satisfaction is influenced by quality and price. This paper presents a case study of the company “QuieroVinos, S.L.”, an online wine shop founded in 2015 that sells Spanish wines in two main marketplaces. Design/methodology/approach – With the final target of increasing the company profits it has been designed and developed an application to track the prices of competitors for a set of products. This information will be used to set the product prices in order to offer the products both competitively and profitably in each Marketplace. This application must check, by tacking into account information such as the product cost or the minimum product margin, if it is possible to decrease the price in order to reach the top cheapest position and as a consequence, increase the sales. Findings – The application improved in a notorious way the company’s results in terms of sales and shipping costs. It must be said that without the use of the presented application, performing the price comparison process within each one of the marketplaces would have taken a long time. Moreover, as prices change very frequently, the obtained information has a very limited time value, and the competitors prices should be analyzed daily in order to take accurate decisions regarding the company’s price policy. Originality/value – Although the application has been designed for the wine sector and the two named marketplace, it could be exported to other sectors. For that, it should be implemented new modules to collect information regarding the competitor’s price of the products selling on each corresponding marketplaceThis work was supported by the Ministerio de Economía y Competitividad under contract TIN2017- 84553-C2-2-R. Also, the authors are members of the research group 2017-SGR363, funded by the Generalitat de Catalunya

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

On the characterization of protein-DNA interactions using statistical potentials and protein-protein interactions

Protein-DNA interactions are indispensable players in the daily activities of cells. DNA-binding proteins regulate gene expression and are responsible of DNA replication, packaging, repair and recombination. Among them, transcription factors activate/repress gene transcription by binding to specific genomic sites. Hence, the characterization of transcription factor binding sites turns out to be crucial in order to understand gene regulation. In this context, the development of computational tools is foremost. Here, I show the prediction of redundant transcription factors in yeast using a combination of homology-based tools and protein-protein interactions. The approach was automated and incorporated into ModLink+, an online and user-friendly tool to infer the fold of remote homologs. Moreover, I describe split-statistical potentials for protein-DNA interactions. Finally, I present SHAITAN, a statistical/homology-based approach that can be used to both predict transcription factor binding sites and infer the more likely transcription factors to bind a DNA sequence of interest.Les interaccions proteïna-ADN són indispensables en l’activitat diària de les cèl•lules. Les proteïnes que participen en aquestes interaccions s’encarreguen de la regulació de l'expressió gènica i són responsables de la replicació, l'empaquetament, la reparació i la recombinació de l’ADN. Entre aquestes proteïnes, els factors de transcripció activen/reprimeixen la transcripció de gens mitjançant la unió a llocs específics dins el genoma. Per tant, la caracterització dels llocs d'unió dels diferents factors de transcripció és crucial per tal d’entendre com funciona la regulació gènica. En aquest context, desenvolupar eines computacionals és importantíssim. En aquesta tesi predict redundància entre factors de transcripció de llevat eines fent servir eines basades en homologia i interaccions proteïna-proteïna. Aquesta aproximació va ser automatitzada i incorporada a ModLink+, una eina accessible des d’internet i fàcil d'usar per a inferir el plegament de proteïnes a partir d’homòlegs remots. D'altra banda, descric potencials estadístics fraccionats per a interaccions proteïna-ADN. Finalment presento SHAITAN, una aproximació basada en homologia i potencials estadistics que pot ser utilitzada per a predir els llocs d'unió de factors de transcripció així com per saber quins factors de transcripció són més probables que s’uneixin a una determinada seqüència d'ADN

Biologically relevant transfer learning improves transcription factor binding prediction

Background Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall model performance, compared to training a separate model for each new task. Results We assess a transfer learning strategy for TF binding prediction consisting of a pre-training step, wherein we train a multi-task model with multiple TFs, and a fine-tuning step, wherein we initialize single-task models for individual TFs with the weights learned by the multi-task model, after which the single-task models are trained at a lower learning rate. We corroborate that transfer learning improves model performance, especially if in the pre-training step the multi-task model is trained with biologically relevant TFs. We show the effectiveness of transfer learning for TFs with ~ 500 ChIP-seq peak regions. Using model interpretation techniques, we demonstrate that the features learned in the pre-training step are refined in the fine-tuning step to resemble the binding motif of the target TF (i.e., the recipient of transfer learning in the fine-tuning step). Moreover, pre-training with biologically relevant TFs allows single-task models in the fine-tuning step to learn useful features other than the motif of the target TF. Conclusions Our results confirm that transfer learning is a powerful technique for TF binding prediction.Medicine, Faculty ofScience, Faculty ofNon UBCStatistics, Department ofReviewedFacultyResearche

The Barcelona historical marriage database and the Baix Llobregat demographic database: from algorithms for handwriting recognition to individual-level demographic and socioeconomic data

Altres ajuts: CERCA Programme/Generalitat de CatalunyaThe Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451-1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography