MASTER OF SCIENCE PROGRAM IN MEDICAL BIOINFORMATICS (INTERNATIONAL PROGRAM), FACULTY OF MEDICINE SIRIRAJ HOSPITAL
| Date | Time | Room | Topic | Lecturer |
|---|---|---|---|---|
| T Sep 18 | 9 - 12 | SIMR301 | Overview of the course & Introduction to Medical Bioinformatics & the Central Dogma slides | Bhoom Suktitipat |
| 13 - 16 | SIMR301 | The RNA World & Systems Biology slides | Varodom Charoensawan | |
| T Sep 25 | 9 - 12 | SIMR301 | Proteomics & Application of Mass Spectrometry slides | Kessiri Kongmanas |
| 13 - 16 | SIMR301 | Molecular Biology Techniques & Bioinformatics Data slides | Wanna Thongnoppakhun | |
| T Oct 2 | 9 - 12 | SIMR301 | BLAST, Primer3, and Sequence Alignment slides | Prapat Suriyaphol |
| 13 - 16 | SIMR301 | Next-generation sequencing technologies slides | Bhoom Suktitipat | |
| T Oct 9 | 9 - 12 | SIMR301 | Metabolomics & its application slides | Sakda Khumroong |
| 13 - 16 | SIMR301 | Genome assembly & Reference Mapping slides | Harald Grove | |
| T Oct 16 | 9 - 12 | SIMR301 | Variant Calling & Annotation slides | Harald Grove |
| T Oct 30 | 9 - 12 | SIMR301 | Overview of Machine Learning slides | Bhoom Suktitipat |
| 13 - 16 | SIMR301 | Data mining & Text mining slides | Apirak Hoonlor | |
| T Nov 6 | 9 - 12 | SIMR301 | Applying genomic data into a real clinical practice: A dawn of the 'real' precision medicine slides | Vip Viprakasit |
| 13 - 16 | SIMR301 | Genetic Risk Profile slides | Bhoom Suktitipat | |
| T Nov 13 | 9 - 12 | SIMR301 | Molecular Evolution I slides | Pravech Ajawatanawong |
| 13 - 16 | SIMR301 | Molecular Evolution II slides | Pravech Ajawatanawong | |
| T Dec 4 | 9 - 12 | SIMR301 | Final Exam | Faculty |
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Attendance: 10%
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Homework: 40%
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Blast/Primer3
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Genome assembly
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Variant calling & annotation
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-
Term Paper: 30%
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Final Exam: 20%
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A > 90%
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B+ [80-90)
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B [70-80)
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C < 70%
Pick an original article from this list that is related to bioinformatics data analysis. Focus more on articles that use high-throughput data in their research, either genomics, transcriptomics, proteomics, metabolomics, or other big data related to biological questions and experiments. Stay away from papers that focus mainly on classical epidemiology, conventional risk factors, or biostatistics, that do not have much utilization of biological data.
* Other ground breaking publications not on this list. Please discuss with the course director. *
Answer the following questions (3 sections)
Section 1: What is already known on this topic
In less than three single-sentence bullet points, please summarize the state of scientific knowledge on this topic. Emphasize on “why” this study needed to be done.
Section 2: What this study adds
In one or two single sentence bullet points, give a simple answer to the questions “What do we now know as a result of this study that we did not know before?” “Is there any implications for practice, research, policy, or public health?”
Be brief, succinct, specific, and accurate.
Section 3: How the data were analyzed
Pick 3 figures and describe the followings
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What is question that this figure tried to answer?
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What data have been generated to answer the question?
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What analysis have been done to get to the conclusion?
Assistant professor Dr. Bhoom Suktitipat, MD, PhD
Graduate Program in Medical Bioinformatics
Department of Biochemistry, Faculty of Medicine Siriraj Hospital &
Integrative Computational BioScience Center, Mahidol University
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