UCLA has established an interdepartmental Institute for Quantitative and Computational Biosciences (QCBio) through a partnership between the UCLA College, Health Sciences and Engineering. We are seeking candidates for faculty positions.
Please view the announcement and find more details here.
For UCLA undergraduate students who are currently enrolled in pre-medical, biological science or related majors, we have open positions for innovative Big Data Science research projects in Gene Wiki, using crowdsourcing and community intelligence approaches.
For UCLA PhD students who are currently in the UCLA MCIP/ACCESS programs, we have two open positions for rotation students in the Winter, Spring, or Summer. There is currently no open slot for a permanent PhD student.
Welcome students from Bioinformatics and Bioinformatics IDPs. For UCLA PhD students who are currently in the UCLA Bioinformatics/ Bioengineering programs, we have two open positions for rotation students in the Winter, Spring, or Summer, and two open positions for PhD studies.
Advances in bioinformatics, especially in the field of genomics, have been greatly accelerated by the progress in more powerful computational systems that enable larger and larger amounts of data to be quickly processed. This has resulted in a rapid increase in the number of software tools, databases, and knowledge bases for biology publicly available. Unfortunately, the lack of systems for assisting users to search and find those most suited for their needs is becoming a significant obstacle. Our lab aims to develop a computational platform (https://aztec.bio) that will aggregate, index, and integrate all biomedical research software. We are developing methods for classifying biomedical software and extracting relevant metadata from scientific publications.
Currently, over 2.2 million cardiovascular-related scientific articles are available online, but are largely unstructured, making it a formidable challenge to identify datasets and to comprehend information. We aim to address this big data challenge by developing text-mining and machine learning methods to discover new insights from clinical data and scientific literature. One subset of this work concerns developing systems to analyse and parse clinical case reports. As a source of biomedical evidence, case reports offer observations of cardiovascular symptoms, disease, and prognoses not seen in other resources, though extracting relevant features from these documents requires development of new medical language processing methods. Such methods may then be used as part of machine learning pipelines to discover properties common to cardiovascular disease as it appears in clinical environments. Additionally, the resulting models of text features may be applied to tools for parsing other medical text, including electronic health records. The results of this project will therefore enable both researchers and clinicians to more rapidly interpret medical text relevant to cardiovascular disease.
The mitochondrion produces ATP and is the energy source of all cells. A mitochondrial proteome may contain up to 2000 distinct proteins with various abundance and they form up to 100 networks/pathways. We have obtained four large protein datasets on four types of mitochondria: the human heart mitochondria; the mouse heart mitochondria; the mouse liver mitochondria; and the fly muscle mitochondria. The analyses of these four datasets will inform what are the core proteins essential to all mitochondrial proteomes, which proteins are unique and contribute to the specificities in function for heart, liver, and muscle, and which proteins are fundamental to the human heart mitochondrial proteome. These information will be essential for our understanding of human cardiac mitochondrial function.
TOPMed database contains several high value human cohorts which are quite diverse in nature, ranging from cardiovascular disease to chronic lung disease cohorts. The bioinformatics investigators may wish to identify molecular signatures that are predictive of clinical outcomes and determine phenotype-genotype associations using machine learning algorithms. Moreover, disease phenotypes among different human cohorts may be interrelated. For example, a high fraction of patients from chronic lung disease cohort can also suffer from Congestive Heart Failure. The integration of datasets across cohorts will allow the quantification of lung and cardiovascular disease clinical data under different environmental conditions. Therefore, TOPMed investigators require a platform to query and integrate the datasets available in these longitudinal cohorts in order to link progression and outcomes with omics signatures. We want to define metadata standards and standardize datasets among human cohorts within TOPMed. Furthermore, we want to develop tools for automatic metadata extraction from different types of datasets, supported by consistent ontology.
Protein-protein interactions (PPI) can reveal protein functions, especially when viewed in the context of protein interaction networks. Within a network, changes to a target of interest can reveal impacts relevant to its interactions: loss of a protein within a cell due to mutation, for example, may impact all potentially interacting proteins, as well as the proteins those protein interact with, and so on, resulting in network perturbations. Examining the complex nature of changes within protein interaction networks often requires comprehensive experimental data sets such as proteomes. Our lab has produced proteomes of the mammalian heart, including experimental quantification of proteins under conditions mimicking heart disease and measurements of changing amounts of these proteins over time. We are now developing methods to combine these and other proteome data sets with protein interaction networks. Integration of these data will likely reveal the protein interactions most likely to be impacted by heart disease, providing evidence for further studies or for the development of novel therapies.