The Wall Lab at Stanford University


About the Wall Lab

With my recruitment to the Center for Biomedical Informatics at Harvard Medical School, I began a campaign to translate my skills in computational genetics, genomics and systems biology for use in clinical understanding of human disease. That campaign enabled me to establish increasingly more clinical research goals and to turn the focus of my lab to questions in genomic medicine, informatics and clinical impact.

As healthcare has moved to use of finer resolutions of genomic scale, I have harnessed bioinformatic strategies that previously I had applied to understanding protein evolution, organismal diversification, and cellular network organization during my graduate work at Berkeley and postdoctoral research in computational genetics at Stanford, to the clinical interpretation of increasingly larger samples of next-generation genomic and phenotypic data. As an Associate Professor at Harvard Medical School with appointments in the Department of Pediatrics and Pathology at Beth Israel Deaconess Medical Center (BIDMC), I have worked to further bridge the gap between preclinical bioinformatics and medicine and refine my research goals to have finer focus on questions of direct impact on healthcare and the human condition. These research goals fall into three general categories:

  1. Translating the thinking of systems biology to the field of autism genetics with the intent to develop effective early-stage diagnostics and targets for therapeutic intervention. The work involves the generation and analysis of genomic and phenotypic databases using computational tools of systems biology, machine learning and network inference.

  2. Efforts to understand and characterize the clinical significance and utility of human genetic variation. This work involves clinical-grade annotation of human genetic variation, estimating the rates of both true and false positives in present day genetic testing and their likely impacts on the practice of personalized care, the construction of an authoritative knowledgebase for clinical decision support, and efforts in educating present and future doctors on the potentials of genomics in individualized healthcare.

  3. Redefining human diseases through computational and comparative network analysis. The work involves the integration and analysis of transcriptomic, genomic and bibliomic data to network all known human diseases. Deliverables include revealing disease connections, properly reshaping blurred boundaries of classification, and opportunities for drug treatment repositioning.

With respect to the first aim, I have launched a new program and paradigm in machine learning analysis of large-scale phenotypic and clinical databases of children with autism and related neurodevelopmental disorders. As a result of this work, I was awarded the coveted Slifka/Ritvo award for clinical innovation in autism research from the International Society for Autism Research. This competitive award is given to one researcher annually and was presented during a ceremony at the International Meeting on Autism Research in San Sebastian, Spain in May 2013. My work on this aim has also resulted in machine-learning classifiers that can reduce the time and complexity of an autism diagnosis from hours to minutes and that can be administered cheaply via mobile technologies for global, remote access. My intention is to deliver these technologies as a clinical solution that enables families to receive attention as early as possible in their child’s development, providing significant benefits from early therapeutic intervention (gaining an average of 17 IQ points). The work has been published [references 32,38,52 in publication list], patented [cf. patent list above], and I am now in the process of testing and commercializing the technology. My future research in this space will focus on expansion to other neurodevelopmental and neurodegenerative diseases, including expanded attention to semi-supervised analysis of home videos for mobilized detection and care.

The second aim has been leveraged by my positions in pediatrics and pathology at Harvard that provided me consistent access to clinicians and clinical data. With these interactions, my colleagues and I launched a genomic medicine initiative within Beth Israel Deaconess Medical Center that is focused on use of genomics data for clinical decision support. This effort has been funded in part through an ongoing collaboration with Affymetrix, which hopes to bring its OncoScan technology into the clinical market. We have worked on deriving clinical action from whole-genome analysis [35], construction of clinical annotation resources for use in genomic pathology for personalized healthcare [36], and on efforts to modernize pathology as nucleus for clinical genomics reporting and decision support [45,46]. Because arguably the biggest barriers to clinical genomics in healthcare is computational bottlenecks, I have focused my efforts on building new methods for genomic data processing that are fast, accurate, and cost effective. For example, my colleagues and I have built comprehensive solutions for real-time annotation of genomic data in the cloud [25,28,33,37]. I have been awarded several Amazon computing grants for access to the cloud and invited to speak at a variety of forums nationally and internationally, most recently as keynote speaker at the Amazon Reinvent conference in Las Vegas (; November 2013). Part of our cloud-based clinical genomics solutions are managed by technology transfer at Harvard and included in licensed solutions for rapid annotation of clinical genomes by 2 companies specializing in clinical sequencing and interpretation. This research had fostered several multi-investigator international collaborations, resulting in several publications on which I am a middle author [30,42,48] but responsible for the computational methods.

The third research aim, also a major thrust of the lab, involves systems medicine analysis of big data relevant to genetic connections to and within human diseases. This work has been recognized by a translational program grant from Boston Children’s Hospital, by NSF, NIH, and Autism Speaks. Enabled in part by these awards, I developed systems medicine methods for comparative network analysis, including approaches in game theory [24], that significantly improve signal detection for complex genetic disorders such as autism [29]. I went on to incorporate this method into a freely accessible research portal, Autworks ( that was initially focused on autism and related neurodevelopmental conditions, but has grown to include all named human conditions. Through this paradigm of comparative network analysis, we have discovered important connections between autism and certain autoimmune disorders [31], we have used haplotype structure and properties of the disease network to prioritize long and unruly lists of candidates for autism into an organized set of markers with high likelihood of clinical utility [49].

My research aims all include a strong commitment to education. The future of genomic medicine requires innovative training that prepares medical professionals to utilize genomic information for clinical decision support. As a step forward in this important mission, I actively co-develop and co-teach novel courses in translational medicine and interpretation of next-generation sequencing data tailored to Harvard’s medical students, residents, and fellows. Our genomics medicine course was the first of its kind at Harvard and has become a requirement for the biomedical informatics master’s program, the bioinformatics and integrative genomics PhD program, and the biomedical informatics fellowship program. Over the last 3 years, I have served as instructor for the resident training program and co-director of the molecular genetic pathology fellowship at BIDMC. At Stanford, I will continue to create and evolve curricula that integrate the practice of genomic and systems medicine into many levels of the healthcare enterprise. My goal is to help current and future doctors understand the potentials of genomics and to know when such information can and should be used in clinical decision support.

Going forward, I will continue to refine my research and teaching goals to the interpretation of “big data” including comprehensive phenotypes, medical records databases, and full genome sequences for development of biomarkers and agents for therapeutic intervention of human disease. As a specialist in genomic medicine and Associate Professor of Systems Medicine at Stanford, I plan to make significant positive impacts – through department and hospital-wide collaborations, funding, publication, patents, and teaching – on our understanding and treatment of neurodevelopmental disorders, chiefly including autism, as well as various cancers and other human conditions, particularly as we reshape the nosology of human disease through methods in systems biology.