Biomedical Informatics Graduate Rotation Projects

This page is updated annually. Some projects may already be taken, and new projects may be available. The projects below give an indication of the types of projects available in each lab, but please browse faculty web pages and contact professors directly to discuss current opportunities.

Labs with BMI Rotation Projects

Robert El-Kareh,

Yoav Freund,

Chun-Nan Hsu,

Trey Ideker,

Michael Rosenfeld,

Jonathan Sebat,

Gene Yeo,

  • Gene Yeo, Cellular and Molecular Medicine

Robert El-Kareh | School of Medicine

Robert El-Kareh | School of Medicine

Email Contact: relkareh [at] ucsd.edu

Project: Development of a Research Electronic Health Record for Clinical Decision Support Studies

When: Fall 2011, Winter 2012, Spring 2012

Creation of effective clinical decision support tools has the potential to significantly improve the quality of care delivered within our healthcare system. However, developing and testing prototypes of these tools requires access to realistic electronic health record (EHR) environments. This process has often involved prohibitively long turnaround times due to time and resource constraints of healthcare information systems groups. For research and educational purposes, these barriers could be avoided by creating an investigator-controlled research EHR and populating it with realistic clinical data. Such a system could enable researchers and students to develop a wide range of novel and innovative clinical decision support tools much more rapidly.

Aims:

  1. Install a sophisticated, open-source EHR (OpenMRS)
  2. Populate this EHR with deidentified data from a rich clinical database (MIMIC II)
  3. Develop one or more prototype clinical decision support tools within this environment

Yoav Freund | Computer Science and Engineering

Yoav Freund | Computer Science and Engineering

Email Contact: yfreund [at] ucsd.edu

Project: Digital Mouse Brain Atlas

When: Summer 2013, Any Quarter
Last updated: 08/26/2013

This Project is in collaboration with the Kleinfeld Lab (http://physics.ucsd.edu/neurophysics/) and the Mitra Lab (http://brainarchitecture.org/mouse/about).

The idea is to use a combination of machine learning and computer vision algorithms to create a digital atlas of the mouse brain.

This would require developing detectors of landmarks that exist in a majority of the brains. As the data size is large (tens of tera-bytes) the work will involve using a hadoop cluster.

Requirements: Python, computer vision, machine learning/statistics.
 

Olivier Harismendy | Pediatrics

Olivier Harismendy | Pediatrics

Email Contact: oharismendy [at] ucsd.edu

The Oncogenomics laboratory is located in the Moores Cancer Center. Its research program is focused on the identification of genetic and epigenetic markers for cancer prevention and progression as well as drug response. The laboratory is a humid laboratory, combining both wet-lab techniques and bioinformatics analysis to study cancer samples from patients and animal models of cancer. The laboratory is also an important partner for multiple principal investigators at the Moores Cancer Center, collaborating on the design, analysis and interpretation of their genomic experiments.

Project: Development of Genomics Virtual Machines in HIPAA compliant cloud

When: Any Quarter
Last updated: 06/09/2016

Genetic information is considered protected health information (PHI) and as a consequence the highest security standards need to be applied for its storage, analysis and sharing. The oncogenomics laboratory is using state of the art iDASH compute cloud for its main computation. As a consequence, we participate in the development of optimal workflows and virtual machines for the analysis of patient-derived genomic datasets such as whole exomes, whole genomes, RNA-seq or genotyping arrays. 

In this project we will develop robust provisioning methods to establish virtual machines capable of running popular human genomic analysis workflows. We will benchmark these machines and workflows and convert some of them into standard recipes for production-grade, reproducible genomic analysis.

Olivier Harismendy | Pediatrics

Email Contact: oharismendy [at] ucsd.edu

The Oncogenomics laboratory is located in the Moores Cancer Center. Its research program is focused on the identification of genetic and epigenetic markers for cancer prevention and progression as well as drug response. The laboratory is a humid laboratory, combining both wet-lab techniques and bioinformatics analysis to study cancer samples from patients and animal models of cancer. The laboratory is also an important partner for multiple principal investigators at the Moores Cancer Center, collaborating on the design, analysis and interpretation of their genomic experiments.

Project: Genetic and epigenetic of cisplatin resistance

When: Any Quarter
Last updated: 06/09/2016

Cisplatin (cDDP) is the most commonly used chemotherapeutic drug, but most cancer eventually become resistant, leading to tumor recurrence. Several biological processes may modulate cDDP sensitivity: Drug import, export, detoxification, DNA repair, apoptosis. Drug resistance is transmitted to daughter cells, and one can build up resistant cell lines in vitro using sequential treatments. We are interested in identifying the genetic mutations that mediate this resistance. For this, we have derived resistant cell-lines from single clones of a cDDP sensitive ovarian cancer cell line. Using exome sequencing as well as target sequencing, we propose to determine mutations in genes and pathways that drive drug resistance. We will then expand the findings to the TCGA samples, using time to recurrence as an indicator of drug sensitivity.

Olivier Harismendy | Pediatrics

Email Contact: oharismendy [at] ucsd.edu

The Oncogenomics laboratory is located in the Moores Cancer Center. Its research program is focused on the identification of genetic and epigenetic markers for cancer prevention and progression as well as drug response. The laboratory is a humid laboratory, combining both wet-lab techniques and bioinformatics analysis to study cancer samples from patients and animal models of cancer. The laboratory is also an important partner for multiple principal investigators at the Moores Cancer Center, collaborating on the design, analysis and interpretation of their genomic experiments.

Project: The role of inherited variation in cancer somatic landscape

When: Any Quarter
Last updated: 06/09/2016

The role of germline or inherited variation in cancer has been studied in selected families and led to the identification of genetic variants that are dominant and responsible for cancer syndromes. Similarly, rare recessive variants with lower penetrance are responsible for the increase risk in breast and ovarian cancer (BRCA1/2). More common variants in the population have also been identified through GWAS, and have revealed multiple SNPs associated a modest increase in cancer risk. Despite these advances, multiple variants of intermediate allelic frequency in the population, or carried by patients with undocumented family history still remain variants of unknown significance (VUS) and can still play a role in tumor development. In addition, the contribution of variants located outside of the coding region has been underexplored and can now be reexamined in the light of recent maps of the regulatory landscape. The long-term goal of this research is to utilize germline genetics variation in cancer prevention and care to better stage patients or predict their response to treatment.

We propose to identify the germline variants in the UCSD Cancer center patients (targeted gene panel) as well as in the public TCGA/ICGC datasets (whole genomes). We will then test these variants, alone or in combination to identify the ones that impact cancer onset, the tumor somatic landscape or tissue specific regulatory network. The project will involve processing of high throughput sequencing data, population genetics and statistical analysis, in a HIPAA compliant cloud-computing environment.

Chun-Nan Hsu | Biomedical Informatics

Chun-Nan Hsu | Biomedical Informatics

Email Contact: chunnan [at] ucsd.edu

Project: Electronic phenotyping

When: Any Quarter
Last updated: 09/29/2014

As more medical record data are now in electronic format, how to re-use the data for clinical research and healthcare quality improvement becomes an important research topic. Selecting patients from electronic medical records satisfying certain phenotypic conditions may require understanding and disambiguating free texts given in narrative notes. The project will develop capabilities of algorithmic selection that can be used to enhance diagnostic decision-making. 

Lilia Iakoucheva | Psychiatry

Lilia Iakoucheva | Psychiatry

Email Contact: lilyak [at] ucsd.edu

The lab has a variety of bioinformatics projects aimed at improving understanding of the functional impact of autism mutations derived from exome and genome sequencing of the patients. We build spatio-temporal gene co-expression and protein interaction networks for psychiatric diseases and we use these networks to generate the testable hypothesis about the mechanisms of disease. We also test these hypothesis experimentally in the lab, thereby adding a translational aspect to our work. 

Project: Evaluating the effect of splicing mutations on isoform networks in autism

When: Any Quarter
Last updated: 07/12/2016

The project deals with constructing the isoform-level co-expression and protein interaction networks for predicting functional impact of the de novo splice site mutations from the patients with autism spectrum disorder (ASD). Hundreds of splice site de novo mutations are currently identified in the ASD patients, but not a single disease mechanism is established for any of these mutations. We will build and analyze isoform-level networks of brain co-expressed and physically interacting proteins; map de novo ASD mutations onto isoform-level networks to predict their functional impact; and validate the disrupted networks and pathways using CRISPR/Cas technology in neuronal and animal models. This project will discover and characterize cellular and molecular processes that are disrupted by the de novo splice site ASD mutations.

Lilia Iakoucheva | Psychiatry

Email Contact: lilyak [at] ucsd.edu

The lab has a variety of bioinformatics projects aimed at improving understanding of the functional impact of autism mutations derived from exome and genome sequencing of the patients. We build spatio-temporal gene co-expression and protein interaction networks for psychiatric diseases and we use these networks to generate the testable hypothesis about the mechanisms of disease. We also test these hypothesis experimentally in the lab, thereby adding a translational aspect to our work. 

Project: Integrative functional genomic study of pathways impacted by recurrent autism CNV

When: Any Quarter
Last updated: 07/12/2016

Copy number variants (CNVs) represent significant risk factors for Autism Spectrum Disorders (ASD). One of the most frequent CNVs involved in ASD is a deletion or duplication of the 16p11.2 CNV locus, spanning 29 protein-coding genes. Despite the progress in linking 16p11.2 genetic changes with the phenotypic (macrocephaly and microcephaly) abnormalities in the patients and model organisms, the specific molecular pathways impacted by this CNV remain unknown. To test the hypothesis that RhoA signaling is disrupted by this CNV, we will generate KCTD13 and CUL3 mouse models using CRISPR/Cas9 system and investigate dysregulated molecular pathways using RNAseq at various stages of the developing mouse fetal brain.

Trey Ideker | School of Medicine

Trey Ideker | School of Medicine

Email Contact: tideker [at] ucsd.edu

​The overall objective of the Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets:

For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks:

A second big challenge is to work out the functional logic by which these models process information, e.g., from genotype to phenotype. Here too, we have made recent progress

but much remains to be done before we have a cell model capable of making robust predictions about patients. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

Project: Computing a minimal set of genes required for life

When: Fall 2016, Spring 2017
Last updated: 07/13/2016

A long standing question in biology is how many (and which) genes are required for life. This essential core set of genes, or minimal genome, makes up the cell's “life support system” or “chassis and power supply” on which more complex functions and processes are built. This set of genes is of keen interest in the field Synthetic Biology, which aims to synthesize the complete minimal genome of an organism and add additional functions to this genome for biotechnological, pharmaceutical and agricultural ends. This project will attempt to use our whole-cell model of the networks and pathways in a cell to predict which genes and gene combinations are essential for life and, conversely, which genes and gene combinations can be removed. If successful, this project will be able to predict minimal genomes for synthesis and testing. It will also address whether there actually is a single “minimal genome” or whether there exist many different configurations all of which are near or at the global minimum.

Prerequisites: Computer programming or scripting skills.
Optional: Experimental laboratory skills, which would allow student to make tests of model predictions.

Trey Ideker | School of Medicine

Email Contact: tideker [at] ucsd.edu

​The overall objective of the Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets:

For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks:

A second big challenge is to work out the functional logic by which these models process information, e.g., from genotype to phenotype. Here too, we have made recent progress

but much remains to be done before we have a cell model capable of making robust predictions about patients. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

Project: Development of a software pipeline for generating cell function hierarchies from genomic data

When: Fall 2016, Spring 2017
Last updated: 07/13/2016

We have developed algorithms (NeXO and CliXO) by which systematic datasets are used to organize genes into a gene ontology, reflecting the hierarchical organization of cellular structures and molecular pathways in the cell. Currently these algorithms are coded in Python; however, a user-friendly and expandable interface would allow end-users to quickly build and update gene ontologies from new data sets. Coding of this interface is the main goal of this rotation; If successful, this tool could seed a thesis project to construct a gene ontology for a particular cellular process (e.g. DNA damage response) or disease (e.g. cancer) of interest.

Prerequisites: Computer programming or scripting skills; some knowledge of genomic biology.

Trey Ideker | School of Medicine

Email Contact: tideker [at] ucsd.edu

​The overall objective of the Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets:

For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks:

A second big challenge is to work out the functional logic by which these models process information, e.g., from genotype to phenotype. Here too, we have made recent progress

but much remains to be done before we have a cell model capable of making robust predictions about patients. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

Project: Experimental mapping of the DNA damage response

When: Fall 2016, Spring 2017
Last updated: 07/13/2016

Cell colonies on agar grow in a near linear fashion with growth rates reflective of their "fitness". The laboratory has developed an experimental platform that can make continuous measurements of growth rates via time-lapse image capture of thousands of specific genetic mutant strains, enabling us to determine the relevance of every gene in the response to stimuli such as DNA damage via radiation or chemotherapy. During the rotation the student will grow ~50,000 cell colonies in parallel and capture their growth curves using digital images and intermittent radiation exposure. The project includes working in Matlab for the analysis of growth curves and the elucidation of DNA damage response pathways. If successful, the project could be developed into a thesis which uses these data to construct a hierarchical model of DNA damage responses.

Prerequisites: Prior experience in a genetics or biochemistry experimental laboratory.

Trey Ideker | School of Medicine

Email Contact: tideker [at] ucsd.edu

​The overall objective of the Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets:

For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks:

A second big challenge is to work out the functional logic by which these models process information, e.g., from genotype to phenotype. Here too, we have made recent progress

but much remains to be done before we have a cell model capable of making robust predictions about patients. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

Project: Improving the construction of gene ontologies from data

When: Fall 2016, Spring 2017
Last updated: 07/13/2016

While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recently, we have shown that GO can be inferred directly from molecular data. However, our previous methods use heuristic algorithms with problems such as:

  1. The parameters are application-dependent and must be adapted by hand.
  2. The methods are greedy and it is hard to prove or verify their correctness in theory.
  3. The memory consumption is large (10-15G memory footprint) resulting in slow run-times for large datasets.

The aim of this project is to replace the original heuristic objective function with a new mathematical one with an explicit form. It also includes developing a new efficient optimization algorithm (based on Integer Linear Programming) to solve the new objective function accurately.

Prerequisites: Prior coursework or research activity in computer algorithms; Computer programming or scripting skills.

Trey Ideker | School of Medicine

Email Contact: tideker [at] ucsd.edu

​The overall objective of the Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets:

For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks:

A second big challenge is to work out the functional logic by which these models process information, e.g., from genotype to phenotype. Here too, we have made recent progress

but much remains to be done before we have a cell model capable of making robust predictions about patients. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

Project: Using a hierarchical cellular model to analyze tumor genetic mutations

When: Fall 2016, Spring 2017
Last updated: 07/13/2016

The student will explore whether a hierarchical model we have recently constructed for predicting growth of simple cells can be translated to predict aggressiveness of human cancer. The model will be provided, along with access to tumor exomes from both public and internal sources. The goal is to determine, over a 10 week rotation, whether and to what extent the model can be used to analyze a patient's exome. If so, this project could be readily developed into a PhD thesis.

Prerequisites: Computer programming or scripting skills; some knowledge of genomic biology.

Lucila Ohno-Machado | School of Medicine

Lucila Ohno-Machado | School of Medicine

Email Contact: machado [at] ucsd.edu

Project: Bioinformatics Rotation Projects Available

When: Any Quarter
Last updated: 08/21/2012

Michael Rosenfeld | School of Medicine

Michael Rosenfeld | School of Medicine

Email Contact: mrosenfeld [at] ucsd.edu

Lab Location: CMM-West, Rm. 345

Lab Phone: 858-534-5858

Lab Composition and Activities: Five graduate students from several programs, and a talented group of enthusiastic (also helpful) postdoctoral fellows and a full time laboratory manager. We have one general laboratory meeting, one graduate student-only meeting, and one personal meeting each week. We also have joint lab meetings with two other labs weekly.

Research Interests: Our central laboratory focus this year is to continue to utilize global genomic approaches to uncover and investigate the “enhancer code” controlled by new, previously unappreciated pathways that integrate the genome-wide response to permit proper development and homeostasis, and that also functions in disease and senescence. We have investigated these events in differentiated cells, neuronal development, stem cells, and cancer. Our biological focus is on molecular mechanisms of the “enhancer code” regulating learning and memory; aggressive prostate and breast cancer, and they underlying events of senescence/aging. Epigenomic events studied include non-histone methylation events and non-coding RNAs. We are investigating these events in development, breast and prostate cancers, and in inflammation-based disease, including degenerative CNS disease and diabetes. The emerging importance of non-coding RNAs and regulation of nuclear architecture is rapidly altering our concepts of homeostasis and disease. Our laboratory is “Seq-ing” (RIP-seq, ChIP-seq, RNA-seq, GRO-seq, CLIP-seq, ChIRP-seq), and a new “FISH-seq”, for open-ended discovery of long-distance genome interactions to uncover new “rules” of regulated gene transcriptional programs and new roles for lncRNAs in biology of normal, cancer neuro-affective disorders and aging cells. Coupling this with chemical library screens, we hope to introduce new types of therapies based on targeting specific gene enhancers, histone protein readers and writers, and lncRNAs for cancers and other diseases. Recent surprising findings have been novel roles of lncRNAs prostate and breast cancer, connection between DNA damage repair/transcription and replication, and unexpected roles of enhancer RNAs.

Current interests include:

  • The “enhancer code,” Epigenomics and transcriptional regulatory mechanisms.
  • Roles of by ncRNAs in enhancer function in signal-dependent genomic relocation and in establishing subnuclear architecture.
  • Mechanisms of signal-induced tumor chromosomal translocations events and new chemical screens for inhibitors for breast and prostate cancer.
  • The “enhancer code” or regulation of learning and memory, including Reelin-regulated enhancers.
  • Linkage of DNA damage/repair and transcription.
  • Retinoic Acid regulation of Pol III-transcribed DNA repeats in maintenance of the stem cell state, in neuronal differentiation and in senescence.
  • Molecular mechanisms of prevelant disease associated sequence variations (GWAS) in disease susceptibility loci.
  • “Epigenomics” in neuronal differentiation, cancer, diabetes and degenerative brain disease.
  • Answering the question when and how enhancers arise and became functional (stem cells to mature cell types).

Project: Bioinformatics Rotation Projects

When: Any Quarter
Last updated: 08/12/2013

Potential projects include:

  • Projects employing use of genome-wide technologies, including ChIP-seq, GRO-seq, CLIPseq-, RNA-seq, and ChIRP-seq, to elucidate molecular mechanisms of regulated enhancer lncRNA actions in cancer and stem cells;
  • Roles and mechanisms of enhancer actions in prostate and breast cancers;
  • Enhancer-based model of neurodevelopment and CNS disorders;
  • New mechanisms of long non-coding RNAs dictating physiological gene regulation in cancer transcriptional programs;
  • Understanding subnuclear structures: Roles of relocation of transcription units between subnuclear architectural structures in regulated gene expression;
  • Chemical library screens to gene signature and translocation responses as an approach toward new cancer therapeutic reagents;
  • Roles of epigenomic regulators and expression of DNA repeats in stem cells, neuronal differentiation and in senescence.

Jonathan Sebat | Cellular and Molecular Medicine

Jonathan Sebat | Cellular and Molecular Medicine

Email Contact: jsebat [at] ucsd.edu

Our laboratory is interested in how rare and de novo mutations in the human genome contribute to patterns of genetic variation and risk for disease in humans. To this end, we are developing novel approaches to gene discovery that are based on advanced technologies for the detection of rare variants, including studies of copy number variation (CNV) and deep whole genome sequencing (WGS). Our goal is to identify genes related to psychiatric disorders and determine how genetic variants impact the function of genes and corresponding cellular pathways.

Project: Determining the effect of autism mutations on development of the head and face

When: Any Quarter
Last updated: 06/09/2016

We have collected whole genome sequence data and 3D digital images of the head and face from a set of 300 autism families. This project will examine quantitative measurement of facial features in autism patients and sibling controls and determine the degree to which specific mutations affect craniofacial structure. We will apply unsupervised clustering of genetic and phenotype data to define diagnostic subgroups of patients.

Jonathan Sebat | Cellular and Molecular Medicine

Email Contact: jsebat [at] ucsd.edu

Our laboratory is interested in how rare and de novo mutations in the human genome contribute to patterns of genetic variation and risk for disease in humans. To this end, we are developing novel approaches to gene discovery that are based on advanced technologies for the detection of rare variants, including studies of copy number variation (CNV) and deep whole genome sequencing (WGS). Our goal is to identify genes related to psychiatric disorders and determine how genetic variants impact the function of genes and corresponding cellular pathways.

Project: Determining the frequency of spontaneous reversion in the human genome

When: Any Quarter
Last updated: 06/09/2016

Structural Variants (SVs) in the human genome are poorly ascertained in genome-wide association studies (GWAS).Tandem duplications in particular are not efficiently tagged by adjacent SNPs. The reasons for this are not known. We hypothesize that SVs, once formed, create local instability resulting in a high rate of spontaneous reversion. This project will directly determine the rates of spontaneous reversion in whole genomes of 300 trio families. In addition, we will examine the local patterns of genetic variation adjacent to SVs to infer the occurrence of reversion events.

Jonathan Sebat | Cellular and Molecular Medicine

Email Contact: jsebat [at] ucsd.edu

Our laboratory is interested in how rare and de novo mutations in the human genome contribute to patterns of genetic variation and risk for disease in humans. To this end, we are developing novel approaches to gene discovery that are based on advanced technologies for the detection of rare variants, including studies of copy number variation (CNV) and deep whole genome sequencing (WGS). Our goal is to identify genes related to psychiatric disorders and determine how genetic variants impact the function of genes and corresponding cellular pathways.

Project: Identifying human essential genes by deletion mapping of a large population

When: Any Quarter
Last updated: 06/09/2016

Studies of genetic variation in large populations makes it possible to determine the degree of natural selection acting on specific sequences. Our lab has mapped structural variation (SV, including deletions and duplications) in large samples (N>100,000). By generating a null model based on regional patterns of SV, we propose to identify sequences that deviate dramatically from expectations. Sequences that display extreme deviation are likely to be genes that are essential for life.

Yingxiao (Peter) Wang | Bioengineering

Yingxiao (Peter) Wang | Bioengineering

Email Contact: yiw015 [at] eng.ucsd.edu

Our research focuses on molecular engineering for cellular imaging and reprogramming, and image-based bioinformatics, with applications in stem cell differentiation and cancer treatment.

Project: Image-based reconstruction of biochemical networks in live cells

When: Any Quarter
Last updated: 06/02/2016

Fluorescence resonance energy transfer (FRET)-based biosensors have been widely used in live-cell imaging to accurately visualize specific biochemical activities. We have developed the Fluocell image analysis software package to efficiently and quantitatively evaluate the intracellular biochemical signals in real-time, and to provide statistical inference on the biological implications of the imaging results. However, important questions arise on how to use these results to reconstruct the quantitative parameters in the underlying biochemical networks, which determine cellular functions and ultimately their fates. In this rotation project, we will integrate optimization-based machine learning approaches with biochemical network models to seek answers to these questions, with applications in cancer treatment against drug resistance.

Gene Yeo | Cellular and Molecular Medicine

Gene Yeo | Cellular and Molecular Medicine

Email Contact: geneyeo [at] ucsd.edu

We have a wide scope of projects ranging from developing novel algorithms for studying RNA processing in diseases, development and personalized medicine, and for analyzing single-cell RNA-seq data.

Project: Single-cell RNA-seq analysis

When: Any Quarter
Last updated: 06/02/2016

We have projects that deal with developing new algorithms for single-cell RNA-seq analysis pertaining to studying heterogeneity in complex mixtures of cells upon environmental challenges.