Código Oficial: | 9687 |
Sigla: | BINF |
Descrição: | A Bioinformática surge no interface entre as disciplinas de Ciências Biológicas, Informática e Matemática e é uma área emergente que foi fortemente impulsionada pela sequenciação de genomas. A combinação de competências e conhecimentos obtidos fornecem uma base sólida para uma ampla gama de oportunidades de emprego ou estudo mais aprofundado para níveis mestrado ou doutoramento. Este curso permite o acesso à Ordem dos Engenheiros Técnicos. |
To acquire some calculus techniques which are widely used in other curricular units; among these techniques we highlight matrix techniques, representation of linear equation systems and their resolution, determinants and their applications as well as linear spaces and linear transformations.
The goal is to carry on developing the mathematical reasoning initiated in highschool education, in order to be able to meet the demands of other curricular units. On completing this curricular unit, students should have acquired the necessary skills in differential calculus and integration of functions of one variable, including the fundamental theorems of calculus.
This curricular unit is designed primarily to provide to the student an overview of boinformátics as an interdisciplinary science that allows the storage and analysis of large volumes of biological information, involving biochemists, biologists, mathematicians and experts in the latest informatic techniques applied to Biological and Chemical sciences. In addition, it is intended with this curricular unit to introduce the student to several fields of action and application of bioinformatics.
The goal is to carry on developing the mathematical reasoning initiated in Mathematical Analysis I and apply it, in this case, to functions of several variables, to be able to meet the demands of other curriculum units. On completing the curriculum unit, the students should have acquired the necessary skills in differential calculus and integration of functions of several variables, including the fundamental theorems of calculus. They should also be able to solve some differential equations that appear in several applications of engineering.
The student should be able to design, conceive and implement
databases using the relational database model.
Concepts, various models and existing database systems are
presented. It is intended that the student acquire the ability
to describe the architecture of a Database Management System
and use a database construction tool with this approach.
In this context, it should understand the transactional
operation, the mechanisms of competition, security and
fault tolerance and authorization/authentication in a DBMS.
It is also objective for the student to acquire skills in
analyzing and extracting information from the database.
Data definition and data manipulation languages will be
presented.
For the practical component, among the available tools,
a reference client/server architecture database on the
market will be used.
Learning objectives (knowledge, skills, and competencies to be developed by students):
Students should be able to apply statistical description methods, including both univariate and bivariate analysis, in common engineering applications.
The objectives for students are: to become familiar with formulas, structures, nomenclature and concepts in the field of organic chemistry; to recognize the importance of a given molecule, the role and distribution of electrons that can intervene in organic reactions; to classify the reactions of organic compounds; to understand the chemical reactions and justify mechanistically these reactions. Apply the knowledge of the reactivity of different functional groups in order to obtain new compounds; to acquire the concept of geometry of molecules in space associated with the study of stereochemistry.
It is intended that students acquire skills to access profession as chemical engineering professionals in the chemical or biological in general and, in particular, in the pharmaceutical, agrochemical, food and biochemistry, or related fields, and in public services.
It is intended in this course to convey concepts of probability and statistics so that students know and understand applying advanced statistical techniques for multivariate statistical description, whose purpose is to summarize and describe the most relevant aspects in a data set.
Fundamental notions will be addressed in sampling theory and discrete and continuous probabilistic models. The approach to statistical inference with reference to the hypothesis tests will be explored in more detail.
The theoretical approach will be accompanied by examples related to biology. It is also intended that the knowledge acquired in this curricular unit provides a solid basis for other curricular units in the syllabus.
This curricular unit is designed primarily to provide students with an integrated view of the structure and function of genomes, taking into account their methods of sequencing, annotation and analysis. In addition, at the end of the semester it is expected that students know the latest methods of genetic analysis at the RNA level and protein expression, in addition to understand the functioning of cells in an integrated and comprehensive manner.
Students should become familiar with the algorithmic foundations of machine learning, as well as techniques for solving the challenges presented by each dataset. They should be able to select appropriate algorithms for each problem and apply the algorithms to new datasets and understand and evaluate their results.
Learning outcomes and competences
- Understanding of the fundamentals of machine learning algorithms and methodologies presented
- Ability to justify the choice of a machine learning solution to a given problem
- Ability to apply the algorithms to new data sets
- Ability to evaluate the results
Bioethics
(OA.1) Understand the bioethical approach and the relevance of the relationship between science, ethics and society.
(OA.2) Framing biotechnological issues in an ethical approach.
(OA.3) Discuss cases and themes, properly applying bioethical thinking, prevailing theories and argumentative models to the assessment of concrete situations
Information security
(OA.4) Identify the main risks and threats in Cyberspace and the need for Information Security to find structured responses to them.
(OA.5) Understand and know how to apply the main concepts underlying Information Security, its limitations and interrelationships.
(OA.6) Understand and the main concepts related to the risk of information assets in organizations and develop analysis capabilities materialized through various elements, such as strategies, architectures, policies, standards and procedures, etc.
. Acquire a conceptual, deep and specialized
knowledge about Entrepreneurship.
. Critically understand the theories and principles
of Entrepreneurship.
. Conceive creative solutions and innovate both in
the context of creating new companies and in existing
companies.
. Solve complex and unpredictable problems related to
business environments.
. Apply strategic planning tools for the creation
and development of new businesses.
. Develop business plans for business creation.
. Take responsibility for individual and collective
development as an entrepreneur on their own or for others.
. Develop autonomy with regard to decision making and
problem solving in the context of creating the company
itself or existing companies in a proactive, innovative
way, generating sustainable value.
This curricular unit will provide knowledge on tools for storage, processing and visualization of large volumes of data, the development of skills in the construction and testing of efficient algorithms for Big Data, namely the study of paradigms, models, tools and parallel programming languages.
At the end of the course the student should be able to
- Determine the solution to be applied and the instruments to be used in the storage, exploration and analysis of a large volume of data
- Select appropriate visualization options to summarize and extract knowledge from a large volume of data
- Understand the concept of parallel and distributed processing as a way to increase performance in data management and analysis
- Develop algorithms and models to solve problems that explore the management of concurrency, distribution and parallelism
- Recognize the different hardware architectures that support the operation of these algorithms
The degree in Bioinformatics results from the joint efforts of 4 IPS schools. Thus, it makes sense that in the
curricular plan exists one unit with multidisciplinary character, aggregating different knowledge. The UC with 5
ECTS, occurs in the 5th semester with both TP and tutorial (OT) components, being implemented through a tutoring
system that monitors and supports the student's pathway.
The general objective is to allow the student to build a specific learning pathway, ensuring the formal recognition of
learning and skills acquired in different contexts and situations - scientific, academic, professional and social –
ensuing its recognization as significant, relevant and in the scope of the desired competencies for the graduates.
These activities can be carried out throughout the course (extracurricular internships or participation in research
projects or others of the IPS) or be structured only in this 5th semester.