Coupling AI and Network Biology

Coupling AI and network biology

Generate insights for disease understanding and target identification

Alexandr Ivliev, Director of Bioinformatics Cheng Fang, Scientific Consultant 03.06.2020 Agenda 1. Introduction 2. Coupling AI and network biology 3. High-quality biological networks in microbiome for target ID 4. Key takeaways Introduction

Computer Natural language Reinforcement vision processing learning • Self-driving cars • Machine translation • Chess, go, computer games • Face recognition • Speech analysis • Robotics • And more • And more • And more

https://www.asimovinstitute.org/neural-network-zoo/

Esteva et al, Nat Med, 2019

Drug target

• Disease mechanism understanding Disease genes • Target ID

Target

© 2020 Clarivate 12 What approaches are you taking to understand disease mechanisms and identify novel drug targets? a. Literature searches b. OMICs data analysis c. Small scale lab experiments d. Classical machine learning e. Deep learning f. Other or inapplicable

Text and sequences have Images have 2D grid Networks are more linear 1D structure structure complex

CGT TTA GAA

“To be or not to be, that is the question”

0.123 0.224 0.851 0.401 0.486 0.298 Prediction 0.694 0.887 0.653 0.101 0.696

• Embedding = dense vector that captures important information from the input queen • Embeddings can be word2vec king learned automatically as opposed to feature engineering • “Similar” objects have bagel close embeddings • It’s easy to use embeddings as input into AI techniques

Sequences Numeric space

Node 1 Node 2 Node 3

Node 4 Node 5 Node 6 Node 7 Node 3,816

Biological network Numeric space

node2vec

word2vec Node 1 Node 2 Node 4 Node 3 Node 5 Node 3,816 Node 6 Node 7

Biological network Sequences Numeric space

Examples of random walks starting 3 -> 4 -> 6 -> 7 -> 8 from node 3 with four steps: 3 -> 2 -> 3 -> 1 -> 3 3 -> 4 -> 6 -> 3 -> 4

Random walk sequences Node graph embeddings 3 4 6 7 8 Text Node 1: -0.01822536, 0.14636423, 0.023379749 … 3 2 3 1 3 Embedding Node 2: 0.10925472, 0.00750885, -0.019593006 … 3 4 6 3 4 Model … …

It’s a scale of behavior controlled by two hyperparameters Breadth first Depth first

Nodes in “local communities” Nodes having alike “structural roles” are more similar to each other are more similar to each other

© 2020 Clarivate 24 node2vec: Scalable Feature Learning for Networks. Grover, A., & Leskovec, J. (2016). Coupling biological networks with deep neural networks to enable disease understanding and target ID

DEGs Node embeddings Novel from random predicted walks targets

Training set of known targets

Attributes • Nodes 1 and 8 are e.g.: • up-regulated = yes “similar” as they have • protein class = kinase • known target = yes the same attributes But they will be far from each other in in random walks • Node 9 is unreachable from any other node, yet it’s “similar” to Nodes node 6 e.g.: • protein 1 • protein 2 • protein 3

Random walk => node ids “words” Structural graph From structural graph: without attributes 1 -> 3 -> 2 => “1 3 2”

1 a From attributes graph: 8 b • Nodes 1 and 8 are are close in bipartite 1 -> a -> 8 -> b -> 2 => “1 8 2” graph 2 • Node 9 is connected to other nodes on Caveats: bipartite graph - Attributes can be only discrete - Cannot use complex attributes, like SMILES, amino-acid sequences, etc Bipartite attributes graph

gat2vec: representation learning for attributed graphs. Sheikh, N., Kefato, Z., & Montresor, A. (2019). Computing, 101(3), 187–209. © 2020 Clarivate 26 Example application Target prioritization using gat2vec - GuiltyTargets Best ROC AUC for different - STRING diseases - HIPPIE ≈0.92-0.94 Protein-protein interaction network Annotated Features Positive- RNASeq from different protein-protein Rank candidate obtained using unlabeled cohorts interaction targets Gat2Vec learning (MSBB, MayoRNASeq, network Discrete ROSMAP, etc.) differential gene expression

- Open Targets Known targets - Therapeutic Targets for the disease Database

GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning. Muslu, Ö., Hoyt, C. T., Hofmann-Apitius, M., & Fröhlich, H. (2019). BioRxiv, 521161. © 2020 Clarivate 27 Coupling biological networks with deep neural networks to enable disease understanding and target ID

Kinases

GWAS hits

Node DEGs embeddings * from random + * walks in Novel * (1) structural predicted graph; and (2) targets + attribute graph

Training set of known targets

Input Graph neural net

Molecule

Physical path

Text The little cat looks lovely.

Any differentiable Features on e.g.: function that aggregates node A multiple vectors into one • protein class • druggability • genetic link Features on • differential node C expression

BIOLOGICAL NETWORK

The beauty is: it’s all a differentiable computational graph that can be optimized using backpropagation.

“Deep Learning for Network Biology -- snap.stanford.edu/deepnetbio-ismb -- ISMB 2018” © 2020 Clarivate 31 Graph neural network for target ID • Every node has its own unique computational graph defined by the biological network structure • These computational graphs are BIOLOGICAL NETWORK neural networks that can be trained using standard AI techniques

A B C D E F

COMPUTATIONAL GRAPH = ARTIFICIAL NEURAL NETWORK FOR EACH NODE

“Deep Learning for Network Biology -- snap.stanford.edu/deepnetbio-ismb -- ISMB 2018” © 2020 Clarivate ‹#› Example: Decagon algorithm Modeling polypharmacy side effects with graph convolutional networks

Modeling polypharmacy side effects with graph convolutional networks. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). © 2020 Clarivate 33 These methods open the doors to coupling AI with biological networks

Targets

Indications

Mechanisms

Garbage in – Knowledge bias Model interpretation garbage out • The need for high- • It’s hard to predict • Opening the “black box” quality networks completely unknown • And large high-quality from the known training sets

© 2020 Clarivate 35 How optimistic are you that AI will transform pharma R&D in 5 years? a. It'll revolutionize research b. It'll yield incremental advances c. It won't solve any of the major challenges d. Other

Curating high-quality networks in microbiome for target ID

© 2020 Clarivate 39 Why should we care about the microbiome? Its importance has long been recognized (first described 1700 years ago!) and used in medical practice– FMT (fecal microbiota transplant). Fecal transplantation is performed as a treatment for recurrent C. difficile colitis infection (CDI). C. difficile colitis, a complication of antibiotic therapy, may be associated with diarrhea, abdominal cramping and sometimes fever.

Adverse effects are poorly understood.

Microbiome is implicated in health and diseases: • IBD and Crohn’s diseases • Obesity & Diabetes • Immune functions & malfunction • Autoimmunity diseases • Cardiovascular diseases • Neurological diseases • Oncology

Cani, P. Nat Rev Gastroenterol Hepatol 14, 321–322 (2017).

Source: Cortellis Drug Discovery Intelligence

© 2020 Clarivate 42 Active drug development • Vast majority of the microbiome drugs in clinical trials have no specific mechanisms • Sodium oligo- mannurarate extracted from algae developed at Shanghai Green Valley to treat mild to moderate AD. • Sibofimloc is inhibitor of type 1 fimbrial adhesin from E. Coli. It’s in phase one for IBD.

Source: Cortellis Drug Discovery Intelligence

Drug Discovery Preclinical Clinical Trial Regulatory Review Marketing Translational API Synthesis Scale-up to MFG Manufacturing Precision Medicine Post-market Surveillance (Ph. IV)

IND Submitted NDA Submitted   APPROVAL 5,000-10,000 ~250 compounds compounds <5 compounds

PHASE I PHASE II PHASE III 20 – 100 100 – 1,000 – 5,000 volunteers 500patients patients

Safety Safety/ Efficacy, Dosing Adverse Events

Among 640 novel therapeutics of Phase 3 clinical trials (1998-2008), 344 (54%) failed in clinical development, 230 (36%) were approved by the US Food and Drug Administration (FDA), and 66 (10%) were approved in other countries but not by the FDA. Most products failed due to inadequate efficacy (n = 195; 57%), while 59 (17%) failed because of safety concerns and 74 (22%) failed due to commercial reasons.

Novel predicted targets

• A microbe-host interaction network could be used to: – Networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions – Determine how mutations on either host or microbial proteins affect the interaction – Delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics

Microbe-Host MAMP (microbe Microbial Microbe-microbe protein-protein associated metabolite – host interactions interactions 1 molecular pattern) protein (protein-protein or – Host protein- interactions 2 protein- protein metabolite) interactions

1 approx. 16,000 publications 2 approx. 10,000 publications

Source: Clarivate Analytics Web of Science, using title search terms (human microbiota, human microbiome, microbiome, human microbial, human microbes, or gut ecology).

Construct Review and Acquire Annotate QC and Define search prioritize data and and curate format for project strings abstracts articles articles delivery

Define curation Find Manual Experience Controlled Knowledge in template, relevant review and in vocabularies development inclusion/exclusion articles for prioritize Biomedical and public of biological criteria and review based on literature database IDs databases prioritization strategy inclusion/exc monitoring lusion criteria

A solution for interactome reconstruction, data management and integration

Literature curation and database Proprietary data sources User interface construction

Curator • Annotates • Enriches data User query • Quality control

Articles and data • Metabolite-host interactions Summary statistics • Microbe-microbe interactions • And more Database of Microbiome-Host Administrator Interactions (DoMI) Interaction networks • Design • Table of interactions • Development • Access to related articles • Maintenance • And more Public data sources

MICROBIOME HOST

MetaG MetaB RNA-Seq Metabolite Taxonomy KO BGC IL12 NOS2

Activation IL6

GPR109A IL10 Butyrate MYD88 TRAF6 Inhibition protein NFKB

TLR4 IKKE TlpA

LPS TRAM IRF3 IFNB CD11B TBK1 CASP7 ACT

FHA CD18 COG

node2vec

word2vec Node 1 Node 2 Node 4 Node 3 Node 5 Node 3,816 Node 6 Node 7

Host-microbiome network Sequences Numeric space

Novel predicted targets

AI is promising Biological networks Manual curation Time will show significant are different from remains important how much of advances in the common AI inputs for creating high- transformation data-rich but approaches quality biological versus incremental biomedical field have emerged to networks and progress AI will feed biological training sets for AI bring into pharma networks into AI R&D techniques

Alexandr Ivliev [email protected] Cheng Fang [email protected]

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