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Biomedical Data and AI-Driven Decision Sciences

The study of biostatistics, bioinformatics, systems biology and genomics focuses on developing and applying statistical and mathematical models in close collaboration with biomedical researchers. The mission of the Program in Biomedical Data and AI-Driven Decision Sciences is to train researchers who will contribute to biomedical research by developing new methods for the design and analysis of research studies and by formulating mathematical models of biologic systems, thereby contributing to our understanding of cancer biology and disease processes. The BDADS Program currently has around 50 students and 70 faculty.

In addition to the general GSBS course requirements, the BDADS Program requires the following courses:

  • Introduction to Bioinformatics GS01 1143
  • Biostatistics for Life Sciences GS14 1612 (except for Biostatistics Track)
  • Quantitative Sciences Student Seminar Series GS01 1031 (enroll every fall semester unless student has a direct course conflict)

BDADS students are also expected to take courses required by one of the Program's four tracks as listed in the section below.

Program course requirements for MS students:

  • GS01 1143 Introduction to Bioinformatics
  • GS14 1612 Biostatistics for Life Sciences
  • GS01 1031 Quantitative Sciences Student Seminar

Students who declare a Secondary Area of Concentration in Quantitative Sciences are required to take:

All of the BDADS program required courses or equivalent quantitative courses on a case-by case basis as approved by the Program Director

  • You will be required to add a BDADS faculty to your advisory committee
  • Please reach out to the BDADS Director for any further questions or concerns

Quantitative Sciences Program Prerequisites:

  • Biostatistics Track – background requirements: 1) college-level calculus 2) linear algebra 3) and statistics.  It is highly recommended that students have an MS degree in Statistics or Biostatistics.
  • Bioinformatics/Systems Biology/Quantitative Genomics Tracks – experience with programming in R or Python is highly recommended.

For questions regarding the curriculum for the Quantitative Sciences program, please email [email protected]

Biomedical Data and AI-Driven Decision Sciences Tracks

  • Bioinformatics Track

    In addition to the general BDADS Program requirements, the Bioinformatics Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. BMI 5007 (UT-BMI) – Methods in Health Data Science
    2. PH1976 (UT-SPH) – Fundamentals of Data Analytics & Predictions
      Note: PH1976 has the following prerequisites: PH1700 or equivalent; PH1975; and calculus, linear algebra, basic statistical theory and convex optimization methods at the introductory level.
      1. alternate course option: BMI 6323 (UT-BMI) – Machine Learning in Biomedical Informatics
    3. GS11 1113 – Introduction to Statistical Genetics
    4. Bioinformatics students must also select 1 bio-related elective course

    *Bioinformatics Track – experience with programming in R or Python is highly recommended.

    Electives:

    1. BMI 5304 (UT-BMI) - Advanced Database Concepts in Biomedical Informatics
    2. COMP 557 (Rice) - Artificial Intelligence
    3. COMP 602 (Rice) - Neural Machine Learning II
    4. STAT 615 (Rice) – Regression and Linear Models
      1. Alternate course option: PH1915 (UT-SPH) – Linear Regression
    5. STAT 519 (Rice) - Statistical Inference
    6. STAT 648 (Rice) - GRAPH Models & Networks

    Bioinformatics Track Timeline

    Year 1, Fall

    BDADS Student Seminar Series - GS01 1031

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Biostatistics for Life Sciences - GS14 1612

    Fundamentals of Data Analytics & Predictions - PH1976 (UT-SPH) or BMI 6323 (UT-BMI)

    Year 2, Fall

    Introduction to Statistical Genetics - GS11 1113

    Methods in Health Data Science - BMI 5007 (UT-BMI)

    BDADS Student Seminar Series - GS01 1031

    1 Bio-related elective course

    Year 2, Spring

    Scientific Writing - GS21 1152


    Course Requirements for PhD students who matriculated prior to 2019

    1. BMI 5007 (UT-BMI) Methods in Health Data Science
    2. SPH 1976 (UT-SPH) Fundamentals of Data Analytics & Predictions – Or BMI 6323 (UT-BMI)
    3. BIOC 571 (Rice) Sequence Analysis
    4. 1 Bio-related Elective course
  • Biostatistics Track

    In addition to the general BDADS Program requirements, the Biostatistics Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. GS01 1033 - Introduction to Biostatistics and Clinical Trials
    2. GS01 1023 – Survival Analysis
      1. Alternate course option: PH1831 (UT-SPH) – Survival Analysis

    *Biostatistics Track – background requirements: 1) college-level calculus 2) linear algebra 3) and statistics.  It is highly recommended that students have an MS degree in Statistics or Biostatistics.

    Electives:

    1. STAT 518 (Rice)- Probability (Required for students with no prior background)
      1. Alternate course option: PH1910 (UT-SPH) Theory of Biostatistics I
    2. STAT 519 (Rice) - Statistical Inference (Highly encouraged for students with no prior background)
      1. Alternate course option – PH1911 (UT-SPH) Theory of Biostatistics II
    3. STAT 532 (Rice) - Foundations of Statistical Inference I
    4. PH1915 (UT-SPH) - Linear Models I (highly recommended for students with no prior background)
    5. STAT 605 (Rice) - R for Data Science (highly recommended for students with no prior background)
      1. Alternate course option – PH1975 (UT-SPH) Introduction to Data Science
    6. PH1918 (UT-SPH) - Statistical Methods in Correlated Outcomes
    7. PH1835 (UT-SPH) - Statistical Methods in Clinical Trials
    8. GS01 1233 - GLM and Categorical Data Analysis
      1. Alternate course option – PH1830 (UT-SPH) Categorical Data Analysis
      2. Alternate course option – PH1916 (UT-SPH) Generalized Linear Models
    9. GS01 1273 - Modern Nonparametrics
    10. GS01 1283 - Foundations of Statistical Inference II (Rice STAT 533)
    11. GS01 1813 – Topics in Clinical Trials (Rice STAT 630)

    Biostatistics Track Timeline

    Year 1, Fall

    BDADS Student Seminar Series - GS01 1031

    (STAT 518 (Rice) - Probability - required for students with no prior background)
    (Strongly encourage to take PH1915 (UT-SPH) or STAT 615 (Rice) - Linear Regression for students with no background)
    (Strongly encourage to take PH1975 (UT-SPH) or STAT 605 (Rice) - R for Data Science for students with no background)

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Introduction to Biostatistics and Clinical Trials - GS01 1033

    Survival Analysis - GS01 1023

    (Strongly encourage to take STAT 519 (Rice) Statistical Inference for students with no background)

    Year 2, Fall

    BDADS Student Seminar Series - GS01 1031

    Year 2, Spring

    Scientific Writing - GS21 1152

    Course Requirements for PhD students who matriculated prior to 2019

    1. STAT 518 (Rice) - Probability
      1. Alternate course option: PH1910 (UT-SPH) Theory of Biostatistics I
    2. PH1915 (UT-SPH) - Linear Models I
    3. STAT 605 (Rice) - R for Data Science
      1. Alternate course option – PH1975 (UT-SPH) Introduction to Data Science
    4. GS01 1233 - GLM and Categorical Data Analysis
      1. Alternate course option – PH1830 (UT-SPH) Categorical Data Analysis
      2. Alternate course option – PH1916 (UT-SPH) Generalized Linear Models
    5. GS01 1023 – Survival Analysis
      1. Alternate course option: PH1831 (UT-SPH) – Survival Analysis
  • Systems Biology Track

    In addition to the general BDADS Program requirements, the Systems Biology Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. GS04 1235 Basic & Translational Cancer Biology
    2. PH1976 (UT-SPH) Fundamentals of Data Analytics & Predictions
      Note: PH1976 has the following prerequisites: PH1700 or equivalent; PH1975; and calculus, linear algebra, basic statistical theory and convex optimization methods at the introductory level.
      1. alternate course option: BMI 6323 (UT-BMI) – Machine Learning in Biomedical Informatics
    3. GS11 1113 Introduction to Statistical Genetics     

    *Systems Biology Track – experience with programming in R or Python is highly recommended.

    Electives:

    1. GS01 1133 Statistical Methods in Bioinformatics
    2. STAT 519 (Rice) Statistical Inference
    3. GS04 1183 Molecular Methods and Bioinformatics
    4. BIOC 570 (Rice) Computation with Biological Data
    5. BMI 5007 (UT-BMI) Methods in Health Data Science
    6. BMI 5304 (UT-BMI) Advanced Database Concepts in Biomedical Informatics
    7. GS11 1013 Genetics & Human Disease
    8. GS11 1033 Quantitative Methods in Genetic Epidemiology
    9. Other graduate level courses offered by Statistics at Rice and Biostatistics at UT-SPH, guided by mentor and program director

                                                                                     

     Systems Biology Track Timeline

    Year 1, Fall

    BDADS Student Seminar Series - GS01 1031                                                                                                                                                                         

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Biostatistics for Life Sciences - GS14 1612

    Basic & Translational Cancer Biology - GS04 1235

    Fundamentals of Data Analytics & Predictions - PH1976 (UT-SPH)              

    Year 2, Fall

    Introduction to Statistical Genetics - GS11 1113

    BDADS Student Seminar Series - GS01 1031

    Year 2, Spring

    Scientific Writing - GS21 1152

  • Quantitative Genomics Track

    In addition to the general BDADS Program requirements, the Quantitative Genomics Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. PH1830 (UT-SPH) Categorical Data Analysis
             1. alternate course option: STAT 545 (Rice) GLM & Categorical Data Analysis
             2. alternate course option: PH1916 (UT-SPH) Generalized Linear Models
    2. PH1976 (UT-SPH) Fundamentals of Data Analytics & Predictions
      Note: PH1976 has the following prerequisites: PH1700 or equivalent; PH1975; and calculus, linear algebra, basic statistical theory and convex optimization methods at the introductory level.
      1. alternate course option: BMI 6323 (UT-BMI) – Machine Learning in Biomedical Informatics
    3. GS11 1113 Introduction to Statistical Genetics
    4. GS11 1013 Genetics of Human Disease          

    *Quantitative Genomics Track – experience with programming in R or Python is highly recommended.

    Electives:

    1. GS11 1092 Genetic Epidemiology of Chronic Disease
    2. GS11 1033 Quantitative Methods in Genetic Epidemiology
    3. GS11 1711 Seminar in Genetics & Population Biology
    4. GS11 1103 Evolution of DNA and Protein Sequences
    5. PH2780 (UT-SPH) Applied Genetic Methods in Public Health
    6. Other graduate level courses offered by Statistics at Rice and Biostatistics at UT-SPH, guided by mentor and program director

            

    Quantitative Genomics Track Timeline

    Year 1, Fall

    BDADS Student Seminar Series - GS01 1031

    Categorical Data Analysis - PH1830 (UT-SPH)

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Biostatistics for Life Sciences - GS14 1612

    Fundamentals of Data Analytics & Predictions - PH1976 (UT-SPH)

    Year 2, Fall

    Introduction to Statistical Genetics - GS11 1113

    Genetics of Human Disease - GS11 1013

    BDADS Student Seminar Series - GS01 1031

    Year 2, Spring

    Scientific Writing - GS21 1152

Candidacy Exam

Biomedical Data and AI-Driven Decision Sciences (BDADS) Program students are required to take an on-topic candidacy exam in which the research proposal is based on the student's project. The format follows the GSBS on-topic format and also includes a breadth of knowledge component.  Time at the end of the exam will be used for each examiner to ask one breadth of knowledge question, plus follow-up questions as warranted. 

The question guidelines are as follows:

  • topics covered in class the student has already taken
  • topics not already contained in the students grant proposal
  • at least one biology or clinical practice-based question, as appropriate
  • should be general questions about concepts that lead to follow-up that examines the boundaries of the student’s knowledge and ensures student has a firm understanding of  basic principles of the field
  • one question per examiner; 20 minutes total at the end of the exam

Please see the list of the Biomedical Data and AI-Driven Decision Sciences Program Candidacy Exam Committee Members below.

  • Students should select 2 members from the list below to be on their exam committee (can select any 2 from the list below)
  • Students should select their exam committee chair from this list

Candidacy Exam Committee Members:

Jeff Chang, PhD
Goo Jun, PhD
Peng Wei, PhD
Linghua Wang, PhD 
Ruitao Lin, PhD
James Long, PhD
Arjun Bhattacharya, PhD
Ye Zheng, PhD
Liang Li, PhD
Jian Wang, PhD
Sheng Pan, PhD
Suprateek Kundu, PhD

Program Requirements

Course Descriptions