Biometrics: Numerical and statistical methods for bioengineering applications

Course Code:

GBE222

Course Period:

Autumn

Course Type:

Core

Credits:

3

Theoric:

2

Practice:

2

Laboratory Hour:

0

ECTS:

6

Prerequisite Courses:

Calculus I

Course Language:

English

Course Coordinator:

Emrah Nikerel

Courses given by:

Emrah Nikerel

Course Objectives:

To provide an introduction to (bio)statistical and numerical methods, their theoretical foundations and applications in diverse areas in genetics and bioengineeing.

Course Content:

Introduction to fundamental concepts: application of linear algebra, numerical solutions of linear system of equations, eigenvalue problem, elementary statistics, obtaining experimental data from biological systems, fundamental concepts, hypothesis tests and analyses in biostatistics, model based experimental design, linear regression.

Course Methodology:

1: Lecture by instructor, 2: Lecture by instructor with class discussion, 3: Problem solving by instructor, 4: Use of simulations, 5: Problem solving assignment, 6: Reading assignment, 7: Laboratory work, 8: Term research paper, 9: Presentation by guest s

Course Evaluation Methods:

A: Written exam, B: Multiple-choice exam C: Take-home quiz, D: Experiment report, E: Homework, F: Project, G: Presentation by student, H: …

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Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
Define the fundamental concepts related to the linear algebra and statistics		1,3,4,5	A,E
Perform elementary matrix operations		1,3,4,5	A,E
Relate the solution methods to a given linear algebra problem		1,3,4,5	A,E
Solve an arbitrary size linear system of equations.		1,3,4,5	A,E
Apply statistical tests and compares differences in a given dataset.		1,3,4,5	A,E
Generate calibration lines using linear regression.		1,3,4,5	A,E

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction: General overview of Biostatistics and linear algebra applications in biological systems
2	Linear system of equations and solutions of (homogeneous) system of equations
3	Vectors and Matrices, Matrix Inverse, non-singular matrices, Linear independence, dependence, span
4	Gauss Jordan Elimination
5	Eigenvalues, eigenvectors and their properties
6	Applications in biological systems
7	Overview of biostatistics, normal distribution, confidence interval
8	Theory of hypothesis testing, z-test
9	t-test, t-test for two independent samples, paired t-test
10	ANOVA
11	Interpreting P-values, multiple comparison
12	Correlation analysis
13	Modeling continuous data; multiple (linear) regression
14	Applications, review of course content.

Recommended Sources

RECOMMENDED SOURCES
Textbook	No specific textbook is assigned. The following books is advised: Rossner, Fundamentals of Biostatistics Cherney, Denton, Thomas, Waldron, Linear Algebra
Additional Resources	Several online sources are available.

Material Sharing

MATERIAL SHARING
Documents	Problem sets will be distributed during lectures.
Assignments
Exams	Discussed at first lecture, tentatively 2 MT and 1 Final, several HWs

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid Terms	2	45
Assignments/HWs	8	15
Final	1	40
Total
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
No	Program Learning Outcomes	check √
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,	√
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.	√
2a	Ability to identify, formulate, and solve complex engineering problems,
2b	Ability to select and apply proper analysis and modeling methods for this purpose.
3a	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result,
3b	Ability to apply modern design methods for this purpose.
4a	Ability to devise, select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice.
4b	Ability to employ information technologies effectively.	√
5a	Ability to design experiments for investigating complex engineering problems or discipline specific research questions,
5b	Ability to conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.	√
6a	Ability to work efficiently in intra-disciplinary teams,
6b	Ability to work efficiently in multi-disciplinary teams,
6c	Ability to work individually.
7a	Ability to communicate effectively in Turkish, both orally and in writing,
7b	Knowledge of a minimum of one foreign language,
7c	Ability to write effective reports and comprehend written reports, prepare design and production reports,
7d	Ability to make effective presentations,
7e	Ability to give and receive clear and intelligible instructions.
8a	Recognition of the need for lifelong learning, ability to access information, ability to follow developments in science and technology,
8b	Ability to continue to educate him/herself.
9a	Consciousness to behave according to ethical principles and professional and ethical responsibility.
9b	Knowledge on standards used in engineering practice.
10a	Knowledge about business life practices such as project management, risk management, change management.
10b	Awareness in entrepreneurship and innovation.
10c	Knowledge about sustainable development.
11a	Knowledge about the global and social effects of engineering practices on health, environment, and safety,
11b	Knowledge about contemporary issues of the century reflected into the field of engineering.
11c	Awareness of the legal consequences of engineering solutions.

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration	14	4	56
Hours for off-the-classroom study (Pre-study, practice)	14	7	98
Mid term	2	2	4
Final Examination	1	3	3
Quiz
Total Work Load			162
Total Work Load / 25 (h)			6.4
ECTS Credit of the Course			6

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Biometrics: Numerical and statistical methods for bioengineering applications

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