PhD Program

The QBS academic curriculum has been designed to provide students with a strong foundation in the quantitative sciences: bioinformatics, biostatistics, and epidemiology, while also offering a wide multi-disciplinary menu of electives.

All QBS students must register for research credit (QBS 197-199, 297-298) for the duration of their PhD candidacy. The first year, students will conduct a research rotation in at least 3 labs before determining a dissertation mentor.

Satisfactory completion of the required core courses include two terms of Integrative Biomedical Sciences (QBS 110 & 111), two terms of bioinformatics (QBS 146 & 175), two terms of biostatistics (QBS 120 & 121), two terms of epidemiology (QBS 130 & 131), and unless having taken a graduate level biology prior to matriculation, a biology course (QBS 100 or BIOC 101). All students are also required to enroll in a Journal Club for their first 3 years in the program (QBS 270 for first years) and one quarter of supervised teaching (QBS 196).


QBS 100: Molecular Basis of Human Health and Disease

Course Director: Kristine Giffin

This course is designed to solidify key cellular, molecular, and genetic concepts in the biology of human health and disease. Students in this course will develop a fundamental understanding of the molecular pathogenesis and genetic predisposition to disease, be familiar with the modern tools and technologies to study molecular processes and disease in model systems and human populations. Topics include the basics of cell structure and function, DNA structure and function, normal and pathologic cellular processes, genetic and epigenetic mechanisms, and examples of major disease outcomes such as cancer.


QBS 110: Integrative Biomedical Sciences I

Course Director: Kristine Giffin

This is the first of a two-part course designed to introduce students to the diversity of biomedical research at Dartmouth across the quantitative, experimental, observational and clinical sciences. In addition to hearing about specific research topics, students will be introduced to the core facilities and shared resources that are available for use by Dartmouth faculty, staff and students. At the culmination of the term, students present a class project that consists of a short grant proposal that summarizes a hypothetical collaboration among three Dartmouth faculty and one or more core facilities or shared resources as described.


QBS 111: Integrative Biomedical Sciences II

Course Directors: TBD

Prerequisite: QBS 100, QBS 110, QBS 120, QBS 121, QBS 130, QBS 131, QBS 146, QBS 175


QBS 146: Foundations of Bioinformatics I

Course Director: Chao Cheng & Mike Whitfield

The sequencing of the complete genomes of many organisms is transforming biology into an information science. This means the modern biologist must possess both molecular and computational skills to adequately mine this data for gaining biological insights and creating new hypotheses. Taught mainly from the primary literature, topics will include genome sequencing and annotation, genome variation, gene mapping, genetic association studies, gene expression, functional genomics, proteomics, single-cell genomics, and systems biology. The course will meet for 3 hours per week.


QBS 175: Foundations of Bioinformatics II

Course Director: TBD

Computation is vital for modern molecular biology, helping scientists to model, predict the behaviors of, and control the molecular machinery of the cell. This course will study algorithmic challenges in analyzing biomolecular sequences (what genes encode an organism, and how are genes related across organisms?), structures (what do the proteins constructed for these genes look like, and what does that tell us about their mechanisms?), and functions (what do these things do, and how do they interact with each other in doing it?). The course is application-driven, but focused on the underlying algorithms and information processing techniques, employing approaches from search, optimization, pattern recognition, and so forth. The course will meet for 3 hours per week.


QBS 120: Foundations of Biostatistics I: Statistical Theory for the Quantitative Biomedical Science

Course Directors: Robert Frost

This is a graduate level course in statistics designed to teach the fundamental knowledge required to read and, with further study, contribute to the statistical methodology literature. An in depth overview of statistical estimation and hypothesis testing will be provided, including the method of least squares, maximum likelihood methods, asymptotic methods, and correction for multiple comparisons. The basic elements of statistical design and sample size calculations will be introduced. Resampling strategies will be discussed in the context of the bootstrap and cross validation, as well as simulation as a tool for statistical research. The emphasis will be on theory used in modern applications in biomedical sciences, including genomics, epidemiology, and clinical and health services research. The statistical package R will be leveraged for computational examples, problem sets and exams. The course will meet for two 1.5 hour sessions per week.


QBS 121: Foundations of Biostatistics II: Regression

Course Directors: Tor Tosteson & Todd MacKenzie

This course covers generalized regression theory and applications as practiced in biostatistics and the quantitative biomedical sciences. The basics of linear model theory are presented, and extended to generalized linear models for binary, counted, and categorical data; regression models for censored survival data; and multivariate regression and mixed fixed and random effects regression models for longitudinal and repeated measures data.. Special topics include measurement error in regression, instrumental variables, causal inference, propensity scores and inverse propensity weighted estimation, methods for missing data. Current statistical methodologies for model selection and classification are introduced in the context of applications in genomics and the biomedical sciences. The course features computational examples using the statistical package R, with references as necessary to other statistical packages.. The course meets 3 hours per week. Most course meetings will consist of presentations and demonstrations of analytic methods using datasets from QBS projects and R or other statistical software. The final meeting will feature presentation of class projects consisting of the explanation and application of a novel regression methodology in a QBS case study.


QBS 130: Foundations of Epidemiology I: Theory and Methods

Course Director: Diane Gilbert-Diamond

This is the first of a two course sequence of graduate level epidemiology (Foundations of Epidemiology I and II). The two courses are designed to teach the underlying theory of epidemiologic study designs and analysis and prepare students for conduct of epidemiology research. Design of investigations seeking to understand the cause of human disease, disease progression, treatment and screening methods include clinical trials, cohort studies, case-cohort, case-case, nested case-control and case-control designs. Concepts of incidence rates, attributable rate and relative rate, induction and latent periods of disease occurrence, confounding, effect modification, misclassification, and causal inference will be covered in depth.


QBS 131: Foundations of Epidemiology II: Theory and Methods

Course Director: Megan Romano

Epidemiology is the science of studying and understanding the patterns of disease occurrence in human populations with the ultimate goal of preventing human disease. This graduate-level course is the second in a two-part sequence. Building off of concepts covered in the Foundations of Epidemiology I, it aims to develop an in-depth understanding of population characteristics and disease frequencies, epidemiological study designs, measures of excess risk associated with specific exposures, and inferring causality in exposure-disease relationships.


QBS 149: Mathematics and Probability for Statistics and Data Mining

Course Directors: TBD

Optional if student tests out.

This course will cover the fundamental concepts and methods in mathematics and probability necessary to study statistical theory. Topics will include univariate and multivariate probability distributions with emphasis on the normal distribution, conditional distributions, mathematical expectation, convergence in probability and distribution, and the central limit theorem. Relevant concepts and methods from univariate and multivariate calculus will be introduced as necessary, along with related topics in linear and matrix algebra. Computational methods for statistics, including nonlinear optimization and Monte Carlo simulation will be introduced. Special attention will be given to students' active learning by programming in a statistical software package. The course will meet for 3 hours per week.


QBS 196: Supervised Teaching in QBS

Course Directors: Arranged

This course is required for all QBS graduate students, based on the assertion that an essential element of graduate education is the experience gained in teaching other students. Such teaching experience is of particular relevance to students interested in academic careers. Students will conduct discussion sessions and provide assistance to the instructor as required in QBS courses under the supervision of the course instructor(s). The faculty and student teaching assistant work very closely to develop assignments. In some cases, the students are encouraged to present lectures for which they receive detailed feedback on their teaching style. The instructor will guide them on how to teach the material if required, how to run a discussion, how to evaluate student responses, and grading. Performance will be monitored throughout the term and appropriate evaluation, coupled with detailed suggestions for improvement, will be provided.


QBS 270: QBS Journal Club

Course Directors

  • Fall: Epidemiology - Jennifer Emond

In this applied course, students will learn how to critically evaluate epidemiological research within public health and the biomedical sciences. Each week we will review a series of peer-reviewed journal articles (approximately 4-6 articles each week) related to one theme. Themes in previous years have included evaluating the health consequences of combustible cigarettes and electronic (“e”) cigarettes, the health benefits and risks of hormone replacement therapy, and the health consequences of sugar-sweetened beverage intake. Articles central to each week’s theme will be selected by the instructor and supplemented with student selected articles. Students are expected to read and critically review each set of articles before class, prepare thought questions based on the readings, and participate in class discussions as we evaluate the body of evidence across studies. For each weekly theme, one set of students will present a summary of the week’s readings to the class. Students are also required to submit a brief summary of the week’s theme after the class discussion.

Learning objectives:

  • To critically evaluate epidemiological research studies within public health and biomedical research.
  • To effectively summarize the findings from such studies orally and in writing.
  • To critically compare different epidemiological research designs that address similar research questions.
  • To identify classical epidemiological research studies within public health and biomedical research.

Students will be evaluated on class attendance, completion of pre-class assignments, participation in class discussions, quality and comprehension of presentations, and completion and quality of weekly summaries.

  • Winter: Biostatistics(194) - Jiang Gui

This is a journal club course that discusses new findings and applications in biostatistics and data science. The goal of the course is to develop critical thinking in biostatistical methodology. Starting the second week of the term, students will present two related paper with an emphasis on biostatical method and the rest of the class will submit a short written summary (1-2 pages) that covers the paper motivation, approach, results, strengths and weaknesses. During class, student will give 50-minute presentation on their papers with 40 min class discussion. In addition to reading and summarizing their selected paper for the week, all students are expected review the two presented papers prior to class in order to participant
in the discussion.

  • Spring: Computational Biology/Bioinformatics - TBD

The critical analysis and communication of experimental research in an oral format is an essential element of scientific training. Students in the QBS journal club will take turns selecting and presenting recently published journal papers related to their research interests. The presentation should include a brief discussion of the significance of the paper as well as a description of the methods used. While the presenter should be prepared to lead the discussion, members of the journal club are expected to come with questions about the paper. These questions can focus on methods, discussion, and interpretation of the results and their implications. This course will meet for a 1.5-hour discussion every week

 


QBS 197, 198, & 199: Graduate Research in Quantitative Biomedical Sciences I , II, & III

Course Directors: Arranged

An original individual, experimental, or theoretical investigation beyond the undergraduate level in quantitative biomedical sciences. This course is open only to graduate students, prior to passing their qualifying exam; it may be elected for credit more than once. This course carries one course credit. Advanced research is to be registered for post-qualifier examination.


QBS 297, 298, & 299: Advanced Graduate Research in Quantitative Biomedical Sciences I, II, & III

Course Directors: Arranged

An original individual, experimental, or theoretical investigation beyond the undergraduate level in quantitative biomedical sciences. This course is open only to graduate students, prior to passing their qualifying exam; it may be elected for credit more than once. This course carries one course credit. Advanced research is to be registered for post-qualifier examination.

Satisfactory completion of two approved graduate level elective courses is required of all students. Below is the list of QBS electives, however, students are allowed to take electives offered through other graduate departments.


QBS 108: Machine Learning

Course Director: Saeed Hassanpour

This course provides a comprehensive introduction to machine learning methods and techniques. Various machine learning concepts and topics, including natural language processing and deep learning, will be described and discussed. The emphasis of this course will be providing the required background and working knowledge of the machine learning methodology to apply these techniques to new or existing data science problems. Through multiple projects/assignments, this course will provide students with the experience on the application of machine learning techniques to solve complex real-world problems, such as those in the biomedical domain.


QBS 122/PH 271: Biostatistics III: Modeling Complex Data

Course Directors: Todd MacKenzie & James O'Malley

The first component of the course introduces Bayesian statistical methods, which is featured due to its affinity for solving challenging problems and its ubiquity across modern statistical and artificial intelligence applications. In an extension of QBS 120, Bayesian methodology is carefully developed and compared to the classical (frequentist) approach. A variety of applications in which the Bayesian approach is naturally suited are considered (e.g., non-inferiority testing, missing outcome imputation, two-part models and selective topics in structural equation modeling). Bayesian computation via Markov-chain Monte-Carlo (MCMC) is also developed and illustrated. The remainder of the course follows QBS 121 by extending regression and other methods for analyzing data when standard statistical assumptions fail. There are two main areas of focus: analysis of statistically dependent data and analysis of social network data. The dependent data section encompasses clustered, multi-level, longitudinal and other forms of structured data and will focus on hierarchical (mixed-effect) modeling approaches under both a frequentist and a Bayesian perspective. The network analysis section includes representation, visualization, and summarization of networks; models of networks; and models of peer effects and social influence processes. Graph partitioning methods will be included if time permits.


QBS 123: Biostatistics Consulting Lab

Course Director: Tor Tosteson and Todd MacKenzie

The goal of this course is to have students gain experience contributing to the statistical aspects of health sciences research. Students will be mentored by Biostatistics faculty members while interacting with investigators from the Geisel School of Medicine and Dartmouth-Hitchcock Medical Center who seeksupport from the Synergy Biostatistics Consulting Core (BCC). Course requirements will include participation in the bi-weekly BCC walk in consulting clinics, shadowing BCC staff and faculty in other statistical collaborative meetings, preparing statistical analyses, sample size calculations, reports and analytic tables and figures. Student performance will be evaluated review of student summaries of their consulting activities and by feedback surveys from BCC collaborators, faculty, and staff.


QBS 132: Molecular Biologic Markers in Human Health Studies

Course Director: Angeline Andrew

This course covers the use of human tissue samples in the context of translational research, including observational epidemiology studies and clinical trials. Lectures focus on study design, bio-specimen collection, biomarker types, kinetics and validation. Discussion will focus on examples of biomarker utilization including identifying susceptible populations, exposure assessment, molecular-genetic characterization of disease phenotype, evaluating drug compliance, monitoring dose response, testing molecularly targeted therapy. The computer-laboratory based component of this course accompanies provides students with “hands on” experience with modern analytic approaches to data generated from state-of-the-art molecular studies of human tissues including many of the “omics” technologies (e.g. DNA methylation array data), and integrated analysis. Students will apply techniques for identifying and evaluating clusters and interactions. Includes application of study design principles, statistical modeling, and bioinformatical approaches.


QBS 133: Clinical Epidemiology

Course Director: Michael Passarelli

This course focuses on the study of medical interventions and the outcomes of disease, expanding on selected concepts covered in Foundations of Epidemiology I & II. Lectures will emphasize study design, statistical methods, collection and interpretation of data, and will be supplemented with readings from the medical literature. Topics include assessment of the performance of diagnostic and screening tests, design of studies for evaluating the efficacy of screening programs for early detection of chronic disease, as well as randomized clinical trials and nonrandomized studies of disease prognosis, therapeutic efficacy, and therapeutic safety. Also covered will be the construction and validation of clinical risk prediction models and statistical approaches for assessing the performance of risk prediction models including discrimination, calibration, and reclassification. Additional topics include the study of the natural history of disease, pharmacoepidemiology, pharmacogenomics, quality of life measurement, and synthesis of quantitative data for medical decision making such as meta-analysis and cost-effectiveness analysis.


QBS 136: Applied Epidemiological Methods I

Course Director: Anne Hoen

Computer laboratory-based course designed to provide hands-on experience performing epidemiological data analyses relevant to the theoretical/conceptual material presented in Foundations of Epidemiology I.  Students will complete laboratory exercises using epidemiological study data sets that guide them through descriptive data analyses, hypothesis testing within the context of a range of epidemiological study designs, causal inference methods, addressing confounding and effect modification, and power and sample size calculations. Analyses will be performed in the open-access programming language R. Course will meet once per week for 90 minutes. Note that this is a half-credit course designed to be taken at the same time as Foundations of Epidemiology I.


QBS 137: Applied Epidemiological Methods II

Course Director: Anne Hoen

Computer laboratory-based course designed to provide hands-on experience performing epidemiological data analyses relevant to the theoretical/conceptual material presented in Foundations of Epidemiology II.  Students will complete laboratory exercises using epidemiological study data sets that guide them through descriptive data analyses, hypothesis testing within the context of a range of epidemiological study designs, causal inference methods, addressing confounding and effect modification, and power and sample size calculations. Analyses will be performed in the open-access programming language R. Course will meet once per week for 90 minutes. Note that this is a half-credit course designed to be taken at the same time as Foundations of Epidemiology II.


QBS 147: Genomics: From Data to Analysis

Course Director: Olga Xhaxybayeva

Massive amounts of genomic data pervade 21st century life science. Physicians now
assess the risk and susceptibility of their patients to disease by sequencing the patient's
genome. Scientists design possible vaccines and treatments based on the genomic
sequences of viruses and bacterial pathogens. Better-yielding crop plants are assessed
by sequencing their transcriptomes. Moreover, we can more fully explore the roots of
humanity by comparing our genomes to those of our close ancestors (e.g., Neanderthals,
Denisovans). In this course, students will address real-world problems using the tools of
modern genomic analyses. Each week students will address a problem using different
types of genomic data, and use the latest analytical technologies to develop answers.
Topics will include pairwise genome comparisons, evolutionary patterns, gene expression
profiles, genome-wide associations for disease discovery, non-coding RNAs, natural
selection at the molecular level, and metagenomic analyses.


QBS 176: Methods in Statistical Genetics and Genomics

Course Director: Ivan Gorlov & Jinyoung Byun

This course will provide an introduction to statistical methods for the study of both simple and complex genetic traits. The emphasis of this course is on training in methods of statistical genetics, especially genetic epidemiology designed to identify genetic factors associated with human diseases. This course covers the key statistical and epidemiologic concepts and methods necessary for understanding genetic architecture of common human diseases.


QBS 177: Algorithms for Data Science

Course Director: Jiang Gui & Eugene Demidenko

This course provides an introduction to algorithms used in data science with applications to biomedical and health data science. The goal of this course is to present an overview of many of the approaches used for big data focusing on analytical methods and algorithms. The course assumes that students have some knowledge of R. Students will be provided with 2 large data sets. Lectures on data reduction, classification, and optimization will request students complete homework for these datasets.


QBS 180: Data Visualization and Statistical Graphics

Course Director: Ramesh Yapalparvi

This course will teach best practices for visualizing data, including exploratory statistics and effective communication of statistical analysis. Students will become competent in engaging diverse audiences in the process of analytic thinking and decision making. Topics include principles of graphic design, perceptual psychology, dashboards, dimensionality reduction, statistical smoothing and 3D graphics. Students will become competent users of Tableau, R graphics and R-Shiny.


QBS 181: Data Wrangling

Course Director: Ramesh Yapalparvi & Eugene Demidenko

This course is a survey of methods for extracting and processing data. It will cover data architectures (ontologies, metadata, pipeline and open source resources), database theory, data warehouses, the electronic medical record, various file formats including audio, and video, data security and cloud resources. Students will gain skills working with Big Data using software such as SQL, APACHE Hadoop and Python.


QBS 187: QBS PhD Student Internship

Course Director: Arranged

PhD students pursuing an internship after the Spring term of their second year will need to register for this course. This course seeks to provide opportunities for field experience and additional training to strengthen understanding of core concepts of our academic curriculum and prepare students for career placement. Qualifying Exam completion is a prerequisite to enroll in this course. Must be in good academic standing to be eligible. Enrollment in this course should not impact the time towards completion of your PhD. Internship enrollment is not an appropriate reason to extend PhD completion beyond 5 years. Students can continue to engage in research during this internship but agreement to do so must be arranged with your primary PI(s). Enrollment in this course is limited to 2 times/2 terms. This course is worth 3 Units. This course is only available to QBS PhD students


QBS 194: Biostatistics Journal Club

Course Directors: Jiang Gui

This is a journal club course that discusses new findings and applications in biostatistics and data science. The goal of the course is to develop critical thinking in biostatistical methodology. Starting the second week of the term, students will present two related paper with an emphasis on biostatical method and the rest of the class will submit a short written summary (1-2 pages) that covers the paper motivation, approach, results, strengths and weaknesses. During class, student will give 50-minute presentation on their papers with 40 min class discussion. In addition to reading and summarizing their selected paper for the week, all students are expected review the two presented papers prior to class in order to participant
in the discussion.


QBS 195: Independent Study

Course Directors: Arranged

Independent study in QBS is structured to allow students to explore subject matter and enhance their knowledge in QBS related fields. This independent study for QBS students can count as an elective credit and is offered during each academic term. The arrangement and a course outline is to be developed between the student and a QBS faculty member prior to the start of the summer term as well as approved by QBS administration. The student and faculty will work together to structure the study program and set goals that are to be met by the end of the term. The course of study may include, but is not limited to, literature review, seminar attendance, online course material, small projects, and presentations related to the specific field being studied. This can also substitute for a journal club credit after the first year.


QBS 271: Epidemiology Graduate Seminar II: Current topics in epidemiology

Course Director: TBD

Student-led graduate level seminar. Students will identify and present two influential epidemiological or biomedical research studies that used different epidemiologic study designs to address a research question. Students will be encouraged to discuss and critically analyze the motivation for the studies, the research design, key findings, study limitations and study implications, and present aims for a future study which will address gaps in the research or be a clear extension of the research to date.

Requirements for a Doctoral Degree (PhD) in Quantitative Biomedical Sciences (QBS)

Modern biomedical research relies on both multidisciplinary and interdisciplinary approaches. Multidisciplinary approaches bring several different scientific disciplines such as bioinformatics and genetics to bear on a research question. Interdisciplinary approaches synthesize knowledge and methods from other disciplines to provide an integrated framework for solving complex biomedical problems in new ways.

The rapid advancement of high-throughput technologies such as DNA microarrays and mass spectrometry for measuring biological systems and their application as part of translational medicine has generated a significant demand for investigators doing cutting-edge research in quantitative disciplines such as bioinformatics, biostatistics and epidemiology. Those with the greatest impact are cross-trained in multiple disciplines giving them the ability to synthesize and integrate several disciplines to provide a truly interdisciplinary approach to solving complex biomedical problems.

The goal of the Graduate Program in Quantitative Biomedical Sciences (QBS) is to prepare PhD students for careers at the intersection of biomedical research and quantitative sciences such as bioinformatics, biostatistics and epidemiology.

The requirements for the PhD degree in Quantitative Biomedical Sciences are as follows:

  1. Satisfactory completion of a two-quarter course in Integrative Biomedical Sciences, two quarters of bioinformatics (Foundations to Bioinformatics I and II), two quarters of biostatistics (Foundations of Biostatistics I and II), two quarters of epidemiology (Foundations of Epidemiology I and II), and a course in biology if not having taken a graduate level biology course prior to matriculation.
  2. Three first year research rotations that will consist of three small research projects, conducted with different faculty members for periods of about three months each, and registered research until the completion of the PhD.
  3. Satisfactory completion of two approved graduate level elective courses.
  4. One quarter of supervised teaching in a QBS course.
  5. Participation in a weekly journal club for the first 3 years.
  6. Satisfactory completion of an oral qualifying examination.
  7. Satisfactory completion of a significant research project and preparation of a thesis describing this research.
  8. Successful defense of the thesis in an oral examination and presentation of the work in a public lecture.

For details of program rules and regulations please see: QBS Handbook 2017-18