BI7101 Critical Thinking and Scientific Communication
Developing critical thinking, scientific writing and presentation skills is critical in any scientific pursuit. Mastering these skills can be a life-long endeavor. Through this course, we aim to set students into the right trajectory by introducing them to the important elements of critical thinking, scientific writing and presentation. The primary objectives of this course are to impart to students a functional ability to reason well and make sound decisions, and provide a systematic guide to scientific writing and presentation. These learning outcomes will serve the students across all academic disciplines and in the workplace.
BI7102 Foundation in Biology
Students will be exposed to various study areas of biology, such as Macromolecules, Cell Physiology, Metabolism, Genetics, Pathobiology and Immunology, and Pharmacology. The goal of the course is to provide students with an integrated systems view of biology and consolidate fundamental concepts. Students will also be exposed to various scientific methods used in the study of biology. Additionally, impact and contributions made to biology through bioinformatics will be highlighted through exemplary papers. Importance of keeping abreast with the current developments will be highlighted and discussed. Expected learning outcome is that students will attain a strong foundation in biology. Although, this course is designed for students of biology, special attention will be given to computer science students and they will be encouraged to learn from their peers, the biology students.
BI7103 Data-warehousing and Programming 1
This hands-on course focuses on building the IT strength of the students, dealing with issues such as web-server, data-warehousing, and scripting in biology. Topics that will be covered include LAMP introduction (Linux, Apache, MySQL & PHP), an introduction to Linux for bioinformatics, apache for Web-server administation, MySQL to build and administer databases, PHP for Web development, and introduction to Shell scripting. Appropriate time slot are allocated for more difficult topics, such as Shell scripting, Apache, MySQL and PHP. Assessments are carefully designed with hands-on elements to help students with mastery of the topics. The expected outcome at the end of this course is that students will be able to setup web-based server workstations, implement database management system and create scripts to solve biological problems.
The course provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data, data types, statistical approaches to data analysis and test for significance, and elements of good experimental design. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types. Additionally, the course will highlight elements and pitfalls of interpretation of statistical results, such as statistical versus biological significance. Expected learning outcome is that students will attain a good understanding of biostatistics and its application in solving bioinformatics based problems.
BI7205 Essential topics in Bioinformatics
Students will be introduced to the concepts, tools and techniques of bioinformatics, a field of immense importance for understanding molecular evolution, individualized medicine, and data-intensive biology. The course includes a conceptual framework for modern bioinformatics, an introduction to key bioinformatics topics such as databases and software, sequence analysis, pairwise alignment, multiple sequence alignment, sequence database searches, and profile-based methods, molecular phylogenetics, visualization and basic homology modeling of molecular structure, proteomics, pathway analysis and personal genomics/NGS. Concepts emphasized in the lectures are complemented by hands-on inquiry using bioinformatics tools in the practicals. Students will achieve highly valued skills as researchers with basic competency in computational and bioinformatics techniques. The expected learning outcome is that students will have a good understanding of the essential topics in bioinformatics.
BI7206 Data-warehousing and Programming II
This course will build on students programming competency by focussing on algorithm design, data structure and computation theory. The course will introduce students to programming in biology, specifically the Python language syntax and methodologies, followed by basics of R, a strongly functional language and environment to explore data sets. The course also emphasizes on algorithm design in biology. This will help students establish good programming practices from the beginning that will go a long way in improving their efficiency in tweaking, debugging, scaling and regression-testing the programmes built. Moreover, the course touches on upcoming trends of high performance computing in biology and parallelism.
BI7207 Data Mining
This course will introduce students to the field of data mining and machine learning, which interfaces statistics and computer science. The course covers the many existing data mining methods available, with a special focus on machine learning approaches to automatically discover patterns, and also the many considerations such as data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, evaluation and online updating. This course prepares students for knowledge discovery from biological data.
BI7308 Advanced Bioinformatics
Students will be introduced to the advanced concepts, tools and techniques of bioinformatics herein. This course will expand on each of the topics covered in the course, BI7205, Essential Topics in Bioinformatics: advanced biological databases, advanced sequence comparisons, advanced biological patterns and profiles, advanced molecular evolution, advanced structural biology, advanced genomic and next-generation sequencing, advanced proteomics, and advanced network and pathway of bioinformatics. Additionally, most recent and current developments for each topic will be discussed. The course will be complemented with hands-on practicals. The expected learning outcome is that students would have further established their understanding and appreciation of bioinformatics topics and possess advanced and current knowledge or skills relevant to the field.
BI71/209 Research Seminar
This course will enrich students learning experience by broadening their views and ideas through participation in seminars presented by local and international bioinformatics researchers. This includes training the students to constructively critique the work of these researchers. In addition, students will be trained to critique published research papers and be able to present them in a way understandable to expert and non-experts of the field. Also, they are to present a critique of the work and synthesize the feedback from the audience for a refined critique. These skills are necessary as science is built by relying on the work of others.
BI72/310 Bioinformatics Research Mini-Project
Students get the opportunity to apply everything learnt into a research project of their choice, which they get to lead, under the supervision of a faculty from Perdana University and/or its partner organizations. The students will learn all about the research pipeline, starting from (i) inception of an idea to a specific problem, (ii) formulation of a rigorous hypothesis, (iii) testing of the hypothesis, to (iv) communication and defense of findings through various channels. This will help students develop basic bioinformatics labs skills (e.g. documentation, quality assurance/quality control, good dry laboratory practices) and understand concepts of experimental design and analysis. Deep learning happens when students construct knowledge, and this miniproject would put the students in a position to construct solutions to open-ended problems.