Biology subjects

Liany, H.

Publications and source records attributed to Liany, H..

6 recordsLinked to original sources

bioRxiv2021

DruID: Personalized Drug Recommendations by Integrating Multiple Biomedical Databases for Cancer

Advances in next-generation sequencing technologies have led to the development of personalized genomic profiles in diagnostic panels that inform oncologists of alterations in clinically relevant genes. While targeted therapies for some alterations may be found, an effective therapeutic strategy should consider multiple and dependent genetic interactions that affect cancer progression, a task which remains challenging. There are ongoing efforts to profile cancer cells in-vitro, both to catalog their genomic information and study their sensitivity to various drugs. There is a need for tools that can interpret the personalized genomic profile of a patient in light of information from these biological and pre-clinical studies and recommend potentially useful drugs. To address this need, we develop a new algorithmic framework called DruID, to effectively combine drug efficacy predictions from a deep neural network model with information, such as drug sensitivity, drug-drug interactions and genetic dependencies, from multiple publicly available databases. We empirically evaluate DruID on cancer cell line data on which efficacy of many drugs have been experimentally determined. We find that DruID outperforms competing approaches and promises to be a useful tool in clinical decision-making.

bioinformatics↗

bioRxiv2020

ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Interactions

A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of SL. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits. CCS CONCEPTS* Applied computing [->] Computational genomics; Health informatics; * Mathematics of computing [->] Hypothesis testing and confidence interval computation. ACM Reference FormatHerty Liany, Anand Jeyasekharan, and Vaibhav Rajan. 2021. ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Genetic Interactions. In Proceedings of ACM Conference. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2020

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

Study of pairwise genetic interactions such as mutual exclusivity or synthetic lethality has led to the development of targeted anticancer therapies, and mining the network of such interactions is a common approach used to obtain deeper insights into the mechanism of cancer. A number of useful graph clustering-based tools exist to mine interaction networks. These tools find subgraphs or groups of genes wherein each gene belongs to a single subgraph. However, a gene may be present in multiple groups - for instance, a gene can be involved in multiple signalling pathways. We develop a new network mining algorithm, that does not impose this constraint and can provide a novel pathway-centric view. Our approach is based on finding edge-disjoint bipartite subgraphs of highest weights in an input network of genes, where edge weights indicate the significance of the interaction and each set of nodes in every bipartite subgraph is constrained to belong to a single pathway. This problem is NP-hard and we develop an Integer Linear Program to solve this problem. We evaluate our algorithm on breast and stomach cancer data. Our algorithm mines dense between-pathway interactions that are known to play important roles in cancer and are therapeutically actionable. Our algorithm complements existing network mining tools and can be useful to study the mutational landscape of cancer and inform therapy development.

bioinformatics↗

bioRxiv2019

Predicting Synthetic Lethal Interactions usingHeterogeneous Data Sources

MotivationA synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.\n\nResultsIn this paper we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.\n\nAvailabilitySoftware available at https://github.com/lianyh\n\nContactvaibhav.rajan@nus.edu.sg

bioinformatics↗

bioRxiv2019

Integrative analysis identifies key molecular signatures underlying neurodevelopmental deficits in Fragile X Syndrome

Fragile X syndrome (FXS) is an incurable neurodevelopmental disorder with no effective treatment. FXS is caused by epigenetic silencing of FMR1 and loss of FMRP expression. To investigate the consequences of FMRP deficiency in the context of human physiology, we established isogenic FMR1 knockout (FMR1KO) human embryonic stem cells (hESCs). Integrative analysis of the transcriptomic and proteomic profiles of hESC-derived FMRP-deficient neurons revealed several dysregulated pathways important for brain development including processes related to axon development, neurotransmission, and the cell cycle. We functionally validated alterations in a number of these pathways, showing abnormal neural rosette formation and increased neural progenitor cell proliferation in FMR1KO cells. We further demonstrated neurite outgrowth and branching deficits along with impaired electrophysiological network activity in FMRP-deficient neurons. Using isogenic FMR1KO hESC-derived neurons, we reveal key molecular signatures and neurodevelopmental abnormalities arising from loss of FMRP. We anticipate that the FMR1KO hESCs and the neuronal transcriptome and proteome datasets will provide a platform to delineate the pathophysiology of FXS in human neural cells.

neuroscience↗

bioRxiv2017

MultiDCoX: Multi-factor Analysis of Differential Co-expression

BackgroundDifferential co-expression signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression.\n\nResultsWe developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis of transcriptomic data. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially coexpressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression.\n\nConclusionsMultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression.\n\nSoftwareR function will be available upon request.

bioinformatics↗