Víctor A. Bucheli

Bio: Víctor A. Bucheli is an academic researcher from University of Valle. The author has contributed to research in topics: Relationship extraction & Complex network. The author has an hindex of 5, co-authored 20 publications receiving 114 citations. Previous affiliations of Víctor A. Bucheli include University of Los Andes.

Prediction of virus-host protein-protein interactions mediated by short linear motifs.

Abstract: Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them – with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

Growth of scientific production in Colombian universities: an intellectual capital-based approach

Abstract: The aim of this paper is to study the knowledge production of Colombian universities in terms of their accumulation of intellectual capital (IC). We observe Colombian universities' publications between 1958 and 2008, categorizing each university according to growth trends in its scientific publications: early exponential growth, late exponential growth, and linear and irregular growth. This work describes the relationships between these growth trends and IC accumulation. It presents an historical description of some institutional changes in Colombian universities that improved the research activity. In addition, we present an empirical study of IC accumulation in universities from the three growth trend categories between 2003 and 2009. We suggest that the adapting capacity, the accumulation time, and the strategies of IC accumulation related to feedback structures are key factors in explaining the differences in knowledge production between growth categories of Colombian universities. The results show critical differences--on orders of magnitude--in IC accumulation across the three categories. Therefore, it would be possible to define a roadmap to improve the knowledge production in Colombian universities.

Game Development for Computer Science Education

Abstract: Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren't typically found in homework: they often situate problems within a compelling alternate reality that unfolds through intriguing narrative, they often draw more upon a player's intrinsic motivations than extrinsic ones, they can facilitate deliberate low intensity practice, and they often emphasize a spirit of play instead of work. At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013. While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016. We extend this working group to a second year---with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals.

Biotea: semantics for Pubmed Central

Abstract: A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http://biotea.github.io.

Computational Thinking 計算論的思考

Abstract: It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

Introductory programming: a systematic literature review

Abstract: As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

PubMed Central

Abstract: PubMed Central（PMC）是美国国立卫生研究院国立医学图书馆生物技术与信息中心开发和维护的生物医学与生命科学期刊文献免费数字文档库。其宗旨是承担起数字时代世界级图书馆的作用。它不是期刊出版商。出版商自愿加入PMC，并需满足一定的科研水平和编辑质量标准。

An analytical study of information extraction from unstructured and multidimensional big data

Abstract: Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive.