In the broadest sense it is uncontroversial that computational biology is helping advance biomedicine because knowledge-processing is at its core. I will argue that this is an unnecessarily limiting definition that both slows progress and prevents individual computational biologists from maximizing their personal impact. Among the approaches with which computational biologists can overcome this artificial limitation:
1. Short but intensive apprenticeships in the biomedical application domain that is your passion.
2. Overcome the sociological barrier between the intellectually parallel computational challenges of clinical medicine and molecular biology. Lead by example in crossing that chasm.
3. Identification within biomedicine of subtasks that most researchers and clinicians do not realize can benefit from massive improvement through computational methods.
4. Pitch In, in individual cases where standard medicine has failed, and help patients directly in their patient care (in ways MDs are unable to).
5. Become a leader. Not only by asking the important questions but obtain funding and teams to answer them. I will illustrate these points with prismatic (and real) examples drawn from the domains of discovery, rare diseases, clinical care and health policy.
11:10 AM-11:20 AM
Prioritising cancer therapeutic targets through CRISPR-Cas9 screens and multi-omics data integration
Room: Delhi (Ground Floor)
Fiona Behan, Wellcome Sanger Institute, United Kingdom
Francesco Iorio, Wellcome Sanger Institute, United Kingdom
Gabriele Picco, Wellcome Sanger Institute, United Kingdom
Kosuke Yusa, Wellcome Sanger Institute, United Kingdom
Mathew Garnett, Wellcome Sanger Institute, United Kingdom
The molecular features of a patient’s tumour impact clinical responses and can be used to guide therapy, leading to more effective treatments and reduced toxicity. Most
patients however do not benefit from such targeted therapies in part due to limited knowledge of candidate targets. Lack of efficacy is a leading cause of the 90% attrition rate in oncology
drug development, and fewer molecular entities to new targets are being developed. Unbiased strategies that effectively identify and prioritise oncology therapeutic targets are needed to improve success rates in drug development and to accelerate the development of new therapies.
We performed genome-scale CRISPR-Cas9 dependency screens in 324 human cancer cell lines from 30 cancer types and developed a data-driven framework to prioritise cancer therapeutic candidates. To distinguish genes required for cell fitness in specific molecular or histological contexts from core-fitness genes (which might be involved in cell essential processes exerting a greater toxicity when inactivated), we developed a novel statistical method: the Adaptive Daisy Model (ADaM). Through this method, we identified a novel set of human core-fitness genes which showed greater recall of genes involved in prior known essential processes and similar false discovery rates for putative context-specific fitness genes when compared with state-of-the-art reference sets of human essential genes. Subsequently, by focusing on putative context-specific fitness genes only, we systematically identified genomic biomarkers of gene essentiality and integrated these with target tractability information, through a dedicated bioinformatic pipeline designed on purpose. This allowed to nominate and prioritise promising therapeutic targets at a genome-scale, and generated a catalog of ~600 promising hits for specific tissues and genotypes, ranked according to their predicted future therapeutic potential, based on multiple evidences.
As a proof of concept, we further experimentally verified one of the most promising targets predicted by our approach - WRN (Werner syndrome ATP-dependent helicase) - as a selective and potent therapeutic target for cancers with microsatellite instability (MSI) from multiple tissues. Additionally, we validated this finding in vivo and determined, through a gene essentiality rescuing experiment, that the helicase activity of WRN is selectively required in MSI cancer cells and an important domain for future therapeutic targeting. Our analysis provides a comprehensive resource of cancer dependencies, generates a framework to prioritise oncology targets, and nominates specific new targets. The principles underlying this study and the described experimental/computational framework can inform the initial stages of drug development by contributing a new, diverse and more effective portfolio of oncology targets. Confirmatory studies are necessary to further evaluate the priority targets we have identified. Nonetheless, even a modest improvement in drug development success rates, and an expanded repertoire of targets, through approaches such as ours could bring patient benefit. Our CRISPR-Cas9 screening results are also a rich resource with diverse applications in fundamental and evolutionary biology, genome engineering and disease genetics. The datasets produced in this new study lay the foundations for producing the Cancer Dependency Map ( https://depmap.sanger.ac.uk/ ), a detailed rulebook for the precision treatment of cancer. All data and results are also public available and queryable in an interactive and user friendly way through the Project Score web-portal ( https://score.depmap.sanger.ac.uk/ ).
11:20 AM-11:40 AM
Proceedings Presentation: Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm
Room: Delhi (Ground Floor)
Michio Iwata, Kyushu Institute of Technology, Japan
Longhao Yuan, RIKEN Center for Advanced Intelligence Project, Saitama Institute of Technology, Japan
Qibin Zhao, RIKEN Center for Advanced Intelligence Project, Japan
Yasuo Tabei, RIKEN Center for Advanced Intelligence Project, Japan
Francois Berenger, Kyushu Institute of Technology, Japan
Ryusuke Sawada, Kyushu Institute of Technology, Japan
Sayaka Akiyoshi, Kyushu University, Japan
Momoko Hamano, Kyushu Institute of Technology, Japan
Yoshihiro Yamanishi, Kyushu Institute of Technology, Japan
Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications. Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches.
11:40 AM-12:00 PM
Proceedings Presentation: Identifying and ranking potential driver genes of Alzheimer's Disease using multi-view evidence aggregation
Room: Delhi (Ground Floor)
Sumit Mukherjee, Sage Bionetworks, United States
Thanneer Malai Perumal, Sage Bionetworks, United States
Kenneth Daily, Sage Bionetworks, United States
Solveig Sieberts, Sage Bionetworks, United States
Larsson Omberg, Sage Bionetworks, United States
Christoph Preuss, The Jackson Labortory, United States
Gregory Carter, The Jackson Laboratory, United States
Motivation: Late onset Alzheimer’s disease (LOAD) is currently a disease with no known effective treatment options. To address this, there have been a recent surge in the generation of multi-modality data (Hodes and Buckholtz, 2016; Muelleret al., 2005) to understand the biology of the disease and potential drivers that causally regulate it. However, most analytic studies using these data-sets focus on uni-modal analysis of the data. Here we propose a data-driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our paper are: i) A general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature-sets and identifying other potential driver genes which have similar feature representations, and ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study (GWAS) summary statistics.
While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types.
Results: We demonstrate the utility of our machine learning algorithm on two benchmark multi-view datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimers. We show that our ranked genes show a significant enrichment for SNPs associated with Alzheimers, and are enriched in pathways that have been previously associated with the disease.
12:00 PM-12:20 PM
Proceedings Presentation: Drug repositioning based on bounded nuclear norm regularization
Room: Delhi (Ground Floor)
Mengyun Yang, Central South University, China
Huimin Luo, Central South University, China
Yaohang Li, Old Dominion University, United States
Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug-disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug-disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug-disease associations are highly correlated. In other words, the drug-disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug-disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug-disease associations.
Results: In this article, we propose to use a Bounded Nuclear Norm Regularization (BNNR) method to complete the drug-disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug-drug and disease-disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug-disease network, which integrates the drug-drug, drug-disease, and disease-disease networks. It not only makes full use of available drugs, diseases, and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug-disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirms the accuracy and reliability of BNNR.
Availability: The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR
12:20 PM-12:40 PM
Proceedings Presentation: Enhancing the Drug Discovery Process: Bayesian Inference for the Analysis and Comparison of Dose-Response Experiments
Motivation: The efficacy of a chemical compound is often tested through dose-response experiments from which efficacy metrics, such as the IC50 , can be derived. The Marquardt-Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose-response experiments.
Results: Our results well demonstrate the informativeness gain of our Bayesian approach in comparison to the commonly used Marquardt-Levenberg algorithm. It is capable to characterize the noise of dataset while inferring probable values distributions for the efficacy metrics. It can also evaluate the difference between the metrics of two datasets and compute the probability that one value is greater than the other. The conclusions that can be drawn from such analyzes are more precise.
Availability: We implemented a simple web interface that allows the users to analyze a single dose-response dataset, as well as to statistically compare the metrics of two datasets.
2:00 PM-2:40 PM
TransMed Keynote: SNAP variation between people + Deep Learning - lessons for protein structure prediction
Motivation: Human genomic datasets often contain sensitive
information that limits use and sharing of the data. In particular,
simple anonymisation strategies fail to provide sufficient level of
protection for genomic data, because the data are inherently
identifiable. Differentially private machine learning
can help by guaranteeing that the published results
do not leak too much information about any individual data point.
Recent research has reached promising results on differentially
private drug sensitivity prediction using gene expression data.
Differentially private learning with genomic data is challenging
because it is more difficult to guarantee privacy in high
dimensions. Dimensionality reduction can help, but if the dimension
reduction mapping is learned from the data, then it needs to be
differentially private too, which can carry a significant privacy
cost. Furthermore, the selection of any hyperparameters (such as the
target dimensionality) needs to also avoid leaking private
Results: We study an approach that uses a large public
dataset of similar type to learn a compact representation for
differentially private learning. We compare three representation
learning methods: variational autoencoders, PCA and random
projection. We solve two machine learning tasks on gene expression
of cancer cell lines: cancer type classification, and drug sensitivity
prediction. The experiments demonstrate significant benefit from all
representation learning methods with variational autoencoders
providing the most accurate predictions most often. Our results
significantly improve over previous state-of-the-art in accuracy of
differentially private drug sensitivity prediction.
Availability: Code used in the experiments is available at https://github.com/DPBayes/dp-representation-transfer
3:00 PM-3:20 PM
Proceedings Presentation: Deep Learning with Multimodal Representation for Pancancer Prognosis Prediction
Room: Delhi (Ground Floor)
Anika Cheerla, Stanford University, United States
Olivier Gevaert, Stanford University, United States
Motivation: Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multi-modal neural network based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type - using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs.
Results: We used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs, and represents patient multi-modal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients.
3:20 PM-3:30 PM
Rare Disease Gene Prioritization Using MEDLINE Derived Association Network
Rare disease gene prioritization approaches rely on high quality curated resources containing disease, gene and phenotype annotations. However, effectiveness of such approaches is constrained by the limited recall and high curation cost of annotated data.
We develop a tool PRIORI-T for rare disease gene prioritization that takes an input set of phenotypes describing a clinical case. PRIORI-T makes use of rare disease correlation pairs extracted from MEDLINE involving human rare diseases, phenotypes and genes. Further, the correlation pairs are augmented using novel associations inferred using the information propagation algorithm GCAS (Graph Convolution-based Association Scoring) and an association network is constructed. The gene prioritization performance of PRIORI-T was validated using the phenotype descriptions of 230 real-world rare disease clinical cases collated from recent publications.
PRIORI-T achieved an overall AUC score of 97% on the Orphanet disease gene associations curated from literature. For the clinical cases, the causal genes were captured within Top-50 and Top-300 for more than 40% and 72% of the cases respectively. PRIORI-T outperformed other competing approaches for gene prioritization that rely primarily on curated resources. Combining PRIORI-T with variant prioritization tools could further improve the accuracy of identifying causal genes.
3:30 PM-3:40 PM
Identifying Biomarkers for Precision Cancer Medicine using Patient-Derived Xenografts
Room: Delhi (Ground Floor)
Arvind Singh Mer, University of Toronto, Canada
Wail Ba-Alawi, University of Toronto, Canada
Petr Smirnov, Univerisity Health Network, Canada
Yi Wang, University of Toronto, Canada
Ben Brew, Hospital for Sick Children, Toronto, Ontario, Canada
Janosch Ortmann, Centre de recherches mathématiques (CRM), University of Montreal, Canada
Ming-Sound Tsao, University Health Network, Toronto, Canada, Canada
David Cescon, University Health Network, University of Toronto, Canada
Anna Goldenberg, SickKids, Canada
Benjamin Haibe-Kains, University of Toronto, Canada
Identifying robust biomarkers of drug response constitutes one of the key challenges in precision medicine. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze PDXs' high-throughput molecular and pharmacological profiles. We have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for in vivo pharmacogenomic datasets that allows for the quantification of the variability in gene expression and pathway activity across passages. By using our package, we showed that gene and pathway activity is consistent across different PDX passages. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR<0.05). We found novel biomarkers based on gene expressions, copy number aberrations and mutations predictive of drug response (concordance index>0.60; FDR<0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.
3:40 PM-3:50 PM
Harnessing genetic interactions to advance precision cancer medicine
Room: Delhi (Ground Floor)
Joo Sang Lee, Cancer Data Science Lab, NCI/NIH, United States
Avinash Das, Department of Biostatistics and Computational Biology, Harvard School of Public Health, United States
Eytan Ruppin, Cancer Data Science Lab, NCI/NIH, United States
Precision cancer medicine approaches are typically focused on searching for ‘actionable’ mutations in these genes, aiming at their therapeutic targeting. However, identifying novel genetic interactions between cancer genes may open new drug treatment opportunities. We studied two fundamental types of genetic interactions: The well-known synthetic lethal interactions, describing the relationship between two genes whose combined inactivation is lethal to the cell; and the less-known synthetic rescues interactions, where a change in the activity of one gene is lethal to the cell but an alteration of its SR partner gene rescues cell viability. We shall describe a new approach for the data-driven identification of these genetic interactions by directly mining patients’ tumor data. Applying it to analyze the Cancer Genome Atlas (TCGA) data, we have identified the first pan-cancer genetic interaction networks shared across many types of cancer, which we then validated via existing and new experimental in vitro and in vivo screens. We find that: (a) synthetic lethal interactions offer an exciting venue for personalized selective anticancer treatments enabling the prediction of patients’ drug response and providing new selective drug target candidates, and (b) targeting synthetic rescue genes can mitigate resistance to primary cancer therapy, including both targeted and immunotherapy.
3:50 PM-4:00 PM
Combining Machine Learning with Single-cell Analysis for Individualized Precision Medicine
Room: Delhi (Ground Floor)
Benedict Anchang, Stanford University, United States
Loukia Karacosta, Stanford University, United States
Sylvia Plevritis, Stanford University, United States
Cancer cells interact with their microenvironment during tumor progression changing their phenotypic states. This challenges the field of precision medicine which is currently not optimized for the individual patient. We now have the ability to obtain highly resolved molecular phenotypes from individual cells from patient samples that can be used to define cell states and study cellular responses to drugs. We present 2 Network-based computational frameworks referred to as STAMP and DRUGNEM with the potential to precisely determine the dynamic state of a disease and individualize combination therapy respectively for a given patient with applications in lung cancer and leukemia. STAMP combines mass cytometry time-series data with machine learning to predict the states of tumor cells from 5 lung cancer patients using a reference Epithelial Mesenchymal Transition (EMT) map trained with a Neural Network. DRUGNEM is used to individualize therapy for 30 ALL patients. Instead of trying to identify a mutation in the DNA and then try to find a drug that addresses that mutation, DRUGNEM isolates single cells from the patient. Then test those cells against a set of drugs to see which drug combinations are effective against the tumor by optimizing early intracellular responses using nested effects models.
4:40 PM-5:10 PM
Harnessing Big, Multidisciplinary Data to Inform Cancer Medicine
Ageing is the major risk factor for many diseases. With the rise in life expectancy, overall burden of ageing-related diseases increases. The molecular link between ageing and age-related diseases, however, has not been explored in a systematic manner. In this study, we test whether diseases with similar age-of-onset share a genetic component that is also implicated in ageing. We perform GWAS on UK Biobank data, which includes genomic, medical and lifestyle measures for almost 500k participants. Our preliminary analysis comparing more than 100 diseases based on their age of onset profiles suggest late-life diseases do share a genetic component that is not prevalent in other diseases. Moreover, these results cannot be explained only by disease categories (e.g. cardiovascular, endocrine) or comorbidities. In order to explore the link between ageing and these diseases, we are now combining our results with publicly available datasets for ageing such as age-series gene expression profiles and lifespan assays using model organisms. Identifying a shared ageing-related mechanism among multiple diseases offer an opportunity to target or even prevent multiple pathologies with a limited number of drugs and decrease the effect of polypharmacy on elderly while retaining the benefits.
5:30 PM-5:40 PM
Clustering multivariate longitudinal clinical patient data using variational deep embedding with recurrence
Room: Delhi (Ground Floor)
Johann de Jong, UCB Biosciences, Germany
Holger Froehlich, University of Bonn / UCB Biosciences, Germany
In the literature, the problem of clustering multivariate short time series is still largely unaddressed. However, multivariate short time series are common in clinical data, when multivariate patient measurements are taken over time. The clustering (stratification) of such clinical data is additionally complicated by the typically high degree of missingness.
For this purpose, we developed variational deep embedding with recurrence (VaDER). VaDER extends variational deep embedding (VaDE), a clustering algorithm built on variational autoencoder principles. VaDER enables the analysis of multivariate short time series with many missing values, by (1) incorporating long short term memory networks (LSTMs) into VaDE's architecture, and (2) defining an architecture and loss function that directly deal with missing values by implicit imputation and loss re-weighting.
We technically validated VaDER by accurately recovering clusters from noisy simulated data with known ground truth clustering. We then used VaDER to successfully stratify (1) Alzheimer's disease patients and (2) Parkinson's disease patients into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected significant underlying biological differences.
We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate short time series clustering in general.
5:40 PM-5:50 PM
From question to publication in five days - how OHDSI is changing medical evidence generation through open science
Population genetics and genomics is an emerging topic for the application of machine learning methods in healthcare and biomedical sciences. Currently, several large genomics initiatives, such as Genomics England, UK Biobank, the All of Us Project, and Europe's 1 Million Genomes Initiative are all in the process of making both clinical and genomics data available from large numbers of patients to benefit biomedical research. However, a key challenge in these initiatives is the standardization of the clinical and outcomes data in such a way that machine learning methods can be effectively trained to discover useful medical and scientific insights. In this talk, we will look at the application of open common data and evidence models such as OMOP, FHIR, GA4GH, RADAR-BASE etc., and in particular zoom in on the OMOP CDM and the open source OHDSI ATLAS tooling, as used in a.o. the IMI EHDEN project.
Please check the webinar video as an alternative reference for how the OMOP CDM and the ATLAS open source tooling have a real impact on biomedical open science.
5:50 PM-6:00 PM
CDx / NGS & Regulation: 5 perspectives from the Pistoia Alliance
Room: Delhi (Ground Floor)
Dominic Clark, Pistoia Alliance, Inc., United Kingdom
Companion Diagnostics (CDx) are essential to the practice of Precision Medicine. Next Generation Sequencing is an increasingly important tool in the development of Companion Diagnostics (CDx). However, for a CDx to be deployed, many different biopharma industry sectors need to collaborate. This paper outlines some of the challenges and opportunities perceived by the bio-pharmaceutical industry, the Europe Molecular Quality Network, a regulatory agency, a notified body and a CDx service provider.
6:00 PM-6:05 PM
Closing remarks from the TransMed committee
Room: Delhi (Ground Floor)
Biography: Isaac Kohane
Isaac Kohane, MD, PhD is the inaugural Chair of the Department of Biomedical Informatics and the Marion V. Nelson Professor of Biomedical Informatics at Harvard Medical School. He develops and applies computational techniques to address disease at multiple scales—from whole healthcare systems as “living laboratories” to the functional genomics of neurodevelopment with a focus on autism. Kohane’s i2b2 project is currently deployed internationally to over 120 major academic health centers to drive discovery research in disease and pharmacovigilance (including providing evidence on drugs which ultimately contributed to “boxed warning” by the FDA). Dr. Kohane has published several hundred papers in the medical literature and authored a widely-used book on Microarrays for an Integrative Genomics. He is a member of the Institute of Medicine and the American Society for Clinical Investigation.
Biography: Burkhard Rost
Professor Rost (b. 1961) conducts research on bioinformatics and computer-aided biology, with a focus on predicting the functions and structures of proteins and genes. His particular interest is predicting protein interactions and the effects of changing individual amino acids, with the goal of fostering a better understanding of how proteins, genes and cells work. He also focuses on enabling earlier diagnosis and more effective treatment of illnesses. The specific niche of his research group links artificial intelligence and machine learning to evolution.
After studying physics, history and philosophy at the Universities of Giessen and Heidelberg, Professor Rost received his doctorate at the European Molecular Biology Laboratory (EMBL) in 1994. Following research stays at EMBL and the European Bioinformatics Institute in Cambridge (UK), as well as a brief period in industry at LION Bioscience in Heidelberg, he assumed a professorship at Columbia University (New York) in 1998. In 2009, he accepted an appointment to the Chair of Bioinformatics at TUM. He is a member of the New York Academy of Sciences and has been President of the International Society for Computational Biology since 2007. He has authored 200 scientific publications with a Hirsch index of 50 (2010).
Biography: Bissan Al-Lazikani
Professor Bissan Al-Lazikani is Head of Data Science at the Institute of Cancer Research. There she leads the Big Data efforts to tackle key problems in Cancer drug discovery and Cancer therapy.
Bissan led the development of integrative computational approaches to inform drug discovery that are now internationally adopted and provided to the community via the canSAR knowledgebase. She applies data science and machine learning approaches to the discovery of novel therapies and pharmacological and radiation to adapting and individualising therapy to patients.
Bissan has a B.Sc (Hons) in Molecular Biology from University College London, an M.Sc in Computer Science from Imperial College and a PhD in Computational Biology from Cambridge University. Bissan has worked on drug discovery and personalised medicine both in academia and industry.