Artificial intelligence and machine learning for precision

medicine with the special focus on oncology- the state

of the art

Pekka Neittaanmäki

Dean of the Faculty of Information Technology

Professor, Department of Mathematical information Technology

University of Jyväskylä

Sami Äyrämö

Research Coordinator

Department of Mathematical Information Technology

University of Jyväskylä

Khaula Zeeshan

Web Intelligence and service engineering

Department of Mathematical Information Technology

University of Jyväskylä

TEKES-HANKE: Value from Public Health Data with Cognitive Computing

CONTENTS

FUTURE IS WHAT WE CALL ARTIFICIAL INTELLIGENCE ............................. 3

MIND BLOWING AI APPLICATIONS ................................................................... 4

AI AND PRECISION MEDICINE – ROAD TO SUPER INTELLIGENCE ............ 6

AI AND MACHINE LEARNING ............................................................................. 8

AI DRIVING PRECISION MEDICINE IN 2017..................................................... 10

PREDICTIVE MODELS FOR PRECISION MEDICINE ........................................ 11

FOCUS ON ONCOLOGY AS SPECIAL CASE...................................................... 15

NEW ERA OF ONCOLOGY - STATE OF THE ART ............................................ 17

CANCER PROGNOSIS AND PREDICTION – ML AT WORK ........................... 20

ML PREDICTIVE TOOLS AND MODELS ........................................................... 21

DEEP LEARNING IN ONCOLOGY – FIGHTING CANCER .............................. 28

DATA TYPES – GENOMICS AND CLINICAL .................................................... 31

NEXT GENERATION SEQUENCING ................................................................... 32

MICROARRAY TECHNOLOGY AND GENE EXPRESSION DATA ................. 33

AI AND GENOMICS ............................................................................................... 34

COMMERCIAL AI/ML SOLUTIONS FOR ONCOLOGY ................................... 37

ML TECHNIQUES FOR DIFFERENT CANCER TYPES – SURVEY TABLES ... 40

CANCER DATABASES ........................................................................................... 46

NATIONAL AND INTERNATIONAL RESEARCH GROUPS ........................... 47

Future is what we call Artificial Intelligence

Artificial intelligence is the technology of future, research in this field is going to change the

face of the modern world. From exploring extraterrestrial planets, to cars driving by

themselves, robots serving coffee in cafes, computers assisting doctors for treating patients

and making accurate predictions, working robots in fields, on roads as traffic guards, in

homes and offices helping and assisting humans is no longer a science fiction, but soon will

be a reality of this earth.

http://www.bbc.com/future/story/20161114-how-we-built-machines-that-can-think-for-

themselves

https://www.youtube.com/watch?v=0XmUaHf-11A&feature=youtu.be

https://www.youtube.com/watch?v=5J5bDQHQR1g

https://www.youtube.com/watch?v=fP5zFpsThqk

Mind blowing AI applications

Humanesquue behavior, mind and actions make Artificial Intelligence!

Mind-blowing AI applications representing humanesque behavior

http://www.pcworld.com/article/220685/tech_of_the_future_today_breakthroughs_in_

artificial_intelligence.html

Police Robots

They are powered by solar panels and are equipped with surveillance cameras

.http://edition.cnn.com/2014/02/24/tech/robot-cops-rule-kinshasa/index.html

Autonomous car-A Neural Network drives A Car

The software learns from its human counterparts to identify the multiple features and

objects encountered in the driving experience like lane markings, streetlights, bushes and,

of course, other cars.

http://www.lostateminor.com/2017/06/16/artificial-intelligence-learning-drive-car/

Autonomous Robot Surgeon-Soft Tissue Autonomous Robot STAR: STAR solved

the soft tissue challenge by integrating a few different technologies. Its vision system relied

on near-infrared fluorescent (NIRF) tags placed in the intestinal tissue; a specialized NIRF

camera tracked those markers while a 3D camera recorded images of the entire surgical

field. Combining all this data allowed STAR to keep its focus on its target. The robot made its

own plan for the suturing job, and it adjusted that plan as tissues moved during the

operation.

http://spectrum.ieee.org/the-human-os/robotics/medical-robots/autonomous-robot-

surgeon-bests-human-surgeons-in-world-first

Latest trends in Artificial Intelligence, reshaping the modern age!

● Deep learning

● AI as manpower

● Autonomous vehicles

● Medicine

● Internet of things (IoT)

● Emotional understanding

● Shopping and customer service

http://www.techrepublic.com/article/7-trends-for-artificial-intelligence-in-2016-like-2015-

on-steroids/

Artificial Intelligence and Precision Medicine - Road to

Superintelligence

● AI making smart wearable devices

● Providing Machine learning tools for medical diagnosis

● AI framework for stimulating clinical decisions and predicting disease outcomes

https://phys.org/news/2017-06-artificial-intelligence-health-revolution.html

http://www.aiimjournal.com/

How AI affecting the medical domain

Our next doctor could very well be a bot. Bots, or automated programs, are likely to play a

key role in finding cures for some of the most difficult-to-treat diseases and conditions.

http://www.healthcentral.com/slideshow/8-ways-artificial-intelligence-is-affecting-the-

medical-field#slide=10

https://www.chipin.com/artificial-intelligence-health-care/

http://www.cs.cmu.edu/~neill/papers/ieee-is2013.pdf

http://www.newvision.co.ug/new_vision/news/1455810/artificial-intelligence-health-

care

https://thenextweb.com/artificial-intelligence/2017/04/13/artificial-intelligence-

revolutionizing-healthcare/#.tnw_YZSfu1AG

The AI Boogie Man-Watson

https://www.youtube.com/watch?v=ZPXCF5e1_HI

Watson is not alone. Other AI computer applications, including chatbots, promise to assist

humans to practice medicine: advice, counsel, treat, and correct defects, disease, and

illness. In fact, telemedicine or remote clinical services–virtual office visits, for example–are

predicted to increase 700% by 2020, according to MIT Sloan’s calculations.

https://hitinfrastructure.com/news/artificial-intelligence-key-in-ibm-watson-health-

partnerships

● Artificial Intelligence Uses EHRs as Smart Analytics Tools

● IBM Watson Artificial Intelligence Improves Cancer Treatment

● BI Artificial Intelligence Supports Health IT Analytics

Each year, more and more medications, scientific developments, treatments and technology

enter the healthcare area. It is unreasonable to expect a healthcare provider to be able to

sift through all that information on their own. After all, we are just humans.

Supercomputers and AI can help make quickly finding the right information a reality.

https://www.fool.com/investing/2017/03/19/ibms-watson-is-tackling-healthcare-with-

artificial.aspx

Artificial Intelligence and Machine Learning

One of the basic requirement of intelligent behavior is learning, and there is no intelligence

without learning so machine learning is one of major branches of artificial intelligence and

most rapidly developing subfield of AI research.

Machine learning algorithms have been used for years in medical domain for the intelligent

data analysis to make not only future predictions of outcome of certain treatment but also

finding hidden relationships within the medical data. Some of the most commonly used

state of the art ML algorithms used in medical domain are as follows:

Assistance-R, Assistance-I, LFC, Naive Bayesian classifier, Semi Naive Bayesian classifier, ANN

(Backpropagation with weight elimination), k-NN.

http://www.sciencedirect.com/science/article/pii/S093336570100077X

http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/ai-predicts-heart-

attacks-more-accurately-than-standard-doctor-method

● Heart murmur detection using ANN and modified neighbor annealing methods

● Prediction of apoptosis proteins based on evolutionary information and SVM

● Identifying risk factors and diagnose ovarian cancer recurrence

● ML based identification of protein-protein interactions

● Prediction of heart attacks and strokes by AI

● Prediction of Autism from infant brain scan

http://www.infoworld.com/article/3199295/artificial-intelligence/primer-how-to-tell-if-

ai-or-machine-learning-is-real.html

AI assists in the home treatment of heart patients in Finland http://sciencebusiness.net/health/research-reports/finland-artificial-intelligence-assists-

in-the-home-treatment-of-heart-patients/

Machine learning reshaping diagnostic medicine

Much of the diagnostic data is image-based, such as X-rays, MRI scans, and ultrasound

imagery, but can also include things like genomic profiles, epidemiological data, blood tests,

biopsy results, and even medical research papers. As a result, there is a wealth of data

available for training neural networks and for other machine learning techniques.

https://www.top500.org/news/machine-learning-will-reshape-diagnostic-medicine/

http://analytics-magazine.org/healthcare-analytics/

State of the art ML applications in health domain

Burgeoning applications of ML in pharma and medicine are glimmers of a potential future in

which synchronicity of data, analysis, and innovation are an everyday reality. McKinsey

estimates that big data and machine learning in pharma and medicine could generate a

value of up to $100B annually, based on better decision-making, and optimized innovation,

improved efficiency of research /clinical trials, and new tool creation for physicians,

consumers, insurers, and regulators.

https://www.techemergence.com/applications-machine-learning-in-pharma-medicine/

http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/in-hospital-intensive-

care-units-ai-could-predict-which-patients-are-likely-to-die

http://it.toolbox.com/blogs/accessible-bi/applications-of-machine-learning-in-

healthcare-diagnosis-76933

https://medium.com/health-ai/artificial-intelligence-in-health-care-weekly-roundup-8-

da5dcf3ff449

AI driving Precision Medicine in 2017

Precision medicine (PM) is a medical model that proposes the customization of

healthcare, with medical decisions, practices, or products being tailored to the individual

patient. In this model, diagnostic testing is often employed for selecting appropriate and

optimal therapies based on the context of a patient’s genetic content or other molecular or

cellular analysis. Tools employed in precision medicine can include molecular diagnostics,

analytics and imaging.

https://www.brighttalk.com/webcast/9293/243547/machine-learning-towards-precision-

medicine

http://www.cio.com/article/3157477/healthcare/how-ai-and-blockchain-are-driving-

precision-medicine-in-2017.html

https://content.medicine.ai/what-is-medicine-ai-b2e5a2a9fbf4

AI techniques have been applied in cardiovascular medicine to explore novel genotypes and

phenotypes in existing diseases, improve the quality of patient care, enable cost-

effectiveness, and reduce readmission and mortality rates. Over the past decade, several

machine-learning techniques have been used for cardiovascular disease diagnosis and

prediction.

http://www.medscape.com/viewarticle/880843

The basic idea behind precision medicine is that large quantities of health data can be

analyzed to determine how small differences among people affect their health outcomes.

That analysis can then help people understand how their unique traits make them more or

less susceptible to a given disease or condition.

https://www.linkedin.com/pulse/artificial-intelligence-precision-treatment-transform-

daniel-burrus

Predictive models and Precision medicine

Appropriate diagnosis is fundamental in medicine because it sets the basis for the prediction

of disease outcome at the single patient level (prognosis) and decisions regarding the most

appropriate therapy. However, given the large series of social, clinical and biological factors

that determine the likelihood of an individual's future outcome, prognosis only partly

depends on diagnosis and aetiology and treatment is not decided solely on the basis of the

underlying diagnosis. Predictive models with deep analytics of big medical data is facilitating

clinicians to step towards high precision medicine with more confidence and reliability in

this modern age. Approaches that take due account of prognosis limit the lingering risk of

over diagnosis and maximize the value of prognostic information in the clinical decision

process.

http://www.thejournalofprecisionmedicine.com/wp-content/uploads/2015/01/Robert-

Hun

ter-Article1.pd

Predictive Analytics and Electronic health records(EHR)

Predictive analytics is fueling a transformation from a focus on the volume of procedures to

the value of outcome. Predictive tools are helping providers — both doctors’ groups and

hospitals — assess patients’ risk of contracting a whole host of diseases and conditions.

They can come up with individualized regimens by tapping into electronic medical records

to identify the types of patients who are most likely to respond to a particular type of

therapy. They can pinpoint treatments that sustain health in a more precise way than ever

before. And they can identify individuals who are likely to stop benefiting from a specific

regimen at a given time. For the volume-to-value paradigm shift in healthcare, predictive

analytics, though rarely visible, is the essential enabler.

https://hbr.org/2016/04/making-predictive-analytics-a-routine-part-of-patient-

care?referral=03759&cm_vc=rr_item_page.bottom

https://hbr.org/2014/10/predictive-medicine-depends-on-analytics

https://www.elsevier.com/connect/seven-ways-predictive-analytics-can-improve-

healthcare

Modern Statistical methodologies

● Trellis Graphics: Scatterplot matrix

● Generalized linear models: Smooth in time Logistic Regression

● Time Sliced Log Linear Regression

● Survival Time Analysis: Cox Regression

● Smooth Regression: Local Regression, Spline Regression

● Tree Based Methods: Regression Tree, Classification Tree

Predictive models from development to validation to clinical impact

http://www.canceropole-gso.org/download/fichiers/2774/1_Moons.pdf

Regression Analysis in Medical Research

Regression Analysis: Mathematical measure of average relationship between two or

more variables in terms of original units of data. It is a powerful technique used for

predicting the unknown value of a variable from known value of two or more variables, also

known as Predictors.

http://biostat.mc.vanderbilt.edu/wiki/pub/Main/ClinStat/model.pdf

https://www.ncbi.nlm.nih.gov/pubmed/6729549

Multiple regression analysis: It is a powerful technique used for predicting the

unknown value of a variable from known value of two or more variables, also known as

Predictors.

https://explorable.com/multiple-regression-analysis

Regression Models in Medicine by Markku Nurminen

A knowledge based report on diagnostic, etciognosti, prognostic regression models in

medicine.

https://www.researchgate.net/publication/303891610_Diagnostic_etiognostic_prognosti

c_regression_models_in_medicine

Regression Modelling and Validation Strategies

http://biostat.mc.vanderbilt.edu/wiki/pub/Main/ClinStat/model.pdf

Monte Carlo Model and Simulations

Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational

algorithms that rely on repeated random sampling to obtain numerical results. They are

often used in physical and mathematical problems and are most useful when it is difficult or

impossible to use other mathematical methods.

This technique is widely used for diagnostic and predictive applications. Monte Carlo

method is used for imaging, nuclear medicine, risk assessment and for many other cases.

https://www.amazon.com/Monte-Carlo-Calculations-Nuclear-Medicine/dp/0750304790

http://pinlab.hcuge.ch/pdf/IAEA02.pdf

http://www.ingentaconnect.com/content/tandf/gsar/2015/00000026/00000006/art0000

2

http://omlc.org/software/mc/

Testing the accuracy of Predictive Models

http://www.plottingsuccess.com/3-predictive-model-accuracy-tests-0114/

External validation of clinical prediction models

http://www.bmj.com/content/bmj/353/bmj.i3140.full.pdf

https://www.nap.edu/read/13395/chapter/7

Focus on Oncology as special case

Oncology is the branch of medicine that deals with the prevention, diagnosis and treatment

of cancer. With the advent of new technologies like Artificial Intelligence and cognitive

science, there is tremendous research has done in past decades, and its potential is

increasing each year. Oncologists are now looking towards this new era of cancer prognosis

and prediction assisted by the state of the art algorithms based on machine learning

techniques, including Artificial neural network (ANN), Bayesian Network (BN), Support

Vector Machine (SVM), and Decision trees (DT`s). Modeling of cancer progression provides

oncologists a decision support system by developing predictive models, resulting in effective

and accurate decision making and improving the understanding of cancer progression.

Cancer prognosis/predictions is concerned with three predictive tasks;

● Prediction of cancer susceptibility (risk assessment)

● Prediction of cancer recurrence/local control

● Prediction of cancer survival

Challenges while treating cancer Cancer is highly heterogeneous disease consisting of many subtypes. The early diagnosis and

prognosis of a cancer type have become a necessity in cancer research. Applications of

Artificial intelligence and Machine learning techniques has opened a new avenue of

research in bioinformatics and biomedical field to dig into the data pools and bring valuable

information to fight cancer by making reliable and well in time cancer diagnosis, prognosis

and prediction. Prediction of cancer outcome usually refers to the cases of Life expectancy,

survivability, progression and treatment sensitivity.

https://www.cancer.gov/research/areas/public-health

https://www.cancer.gov/research/areas/biology

https://www.cancer.gov/research/areas/disparities

Cancer facts and figures 2017

Cancer is the leading cause of human deaths and proved to be most fatal disease. In 2015,

about 90.5 million people had cancer. About 14.1 million new cases occur a year and it

caused about 8.8 million deaths a year. Most common types of cancer in males is lung

cancer, prostate cancer, colorectal cancer and stomach cancer. In females, the most

common types are lung cancer, breast cancer, colorectal cancer and cervical cancer. In

children, acute lymphoblastic leukemia and brain tumors are most common types of cancer.

The risk of cancer significantly increases with age.

https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-

statistics/annual-cancer-facts-and-figures/2017/cancer-facts-and-figures-2017.pdf

New era of Oncology…. State of the art

For the first time, artificial intelligence has been used to discover the exact

interventions needed to obtain a specific, previously unachievable result in vivo,

providing new insight into the biophysics of cancer and raising broad implications for

biomedicine.

https://www.fronteo-healthcare.com/en/cpmais/en/index.html

https://www.sciencedaily.com/releases/2017/01/170127113030.htm

http://esmoopen.bmj.com/content/2/2/e000198

http://www.bbc.com/news/health-36482333

AI and big genomic data:

https://www.bbvaopenmind.com/en/fight-against-cancer-with-artificial-intelligence-and-

big-data/

https://www.mesotheliomahelpnow.com/blog/artificial-intelligence-cancer-patients/

Big Data and artificial intelligence, combined with genetic analysis, allow researchers to

search for and find patterns among patients with rare diseases, who may be separated by

distance but carry the same mutation. The ultimate goal is to create a huge digital medical

data library, a kind of big data of medicine, which respects the privacy of the patient but

accelerates diagnosis and treatment.

http://jamanetwork.com/journals/jamaoncology/article-abstract/2330621

AI Improving oncology safety through post approval analytics

AI has ability to impact patient care through; 1) Innovations and improvements in efficiency,

2) Patient safety in the care delivery process and 3) Efficacy of care delivery

http://www.mwestonchapman.com/artificial-intelligence-improving-safety-through-post-

approval-analytics/

http://www.cbsnews.com/news/artificial-intelligence-making-a-difference-in-cancer-care

AI helping in finding cancer cells

http://sciencenewsjournal.com/artificial-intelligence-helps-find-cancer-cells/

https://news.microsoft.com/stories/computingcancer/

AI powered Microscope to detect cancer cells-State of the art

A new microscope, developed by researchers from using AI for detecting cancer cells in

blood samples. Faster and more accurate than its contemporary techniques, it can analyze

36 million images every second without damaging the blood samples. Deep learning is a

popularly used artificial intelligence that works with complex algorithms to pull meaning

from data, leading to better decision making.

The photos are processed using deep learning, which runs data through a mass of

algorithms to efficiently and accurately “read” the information. Deep learning has also been

used to analyze patients’ genes, allowing identification of diseases or cancer that may

otherwise go undetected, and has the potential to further understand cancer-forming

mutations.

https://futurism.com/microscope-uses-artificial-intelligence-find-cancer-cells/

http://www.breitbart.com/tech/2016/04/28/ai-powered-microscope-helps-root-out-

cancer

http://www.popsci.com/artificial-intelligence-helps-diagnose-cancer

Cancer Prognosis and Prediction-Machine learning at work

Several studies have shown that, the application of ML techniques has significantly

improved the cancer prediction outcome by 15%-20% the last years. Continuous evolution

related to cancer research has been performed. Oncologists have applied different

screening techniques to detect cancer types before it shows symptoms. There are also

statistical predictive models for early prediction. However, to meet the challenging task of

accurate prediction of disease outcome, ML methods have become a popular tool for

medical researchers. These techniques can discover and identify patterns and relationships

between them, from complex datasets, while they are able to effectively predict future

outcomes of a cancer.

http://europepmc.org/articles/PMC4348437

https://www.researchgate.net/publication/285779472_Machine_Learning_

n_Genomic_Medicine_A_Review_of_Computational_Problems_and_Data_Sets

http://csbj.org/articles/e2015004.pdf

https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2008-6.pdf

Machine Learning Predictive Models-Different techniques

For cancer, diagnosis and prognosis many supervised ML techniques have been

applied for years, but they are not adopted in daily clinical routines because of lack

of external validation. Most common ML techniques includes, Artificial neural

network (ANN), Bayesian network (BN`s), Support vector machine (SVM) and

Decision tree (DT´s).

Clinical or genomic data samples are given as inputs with several features and every

feature is having different types of values. Data is preprocessed to address the

quality issues. Preprocessing data steps are, dimensionality reduction, feature

selection and feature selection. ML techniques classify the data into predefined

classes.

http://www.nature.com/ctg/journal/v5/n1/full/ctg201319a.html

Methods of validation: Hold-out method, Random sampling, cross validation, Bootstrap

http://www.sciencedirect.com/science/article/pii/S2001037014000464

http://newatlas.com/machine-learning-predicts-breast-cancer-treatment-

responses/39510/

A Data-mining approach: Data collected from hematopoietic SCT (HSCT) centers are

becoming more abundant and complex owing to the formation of organized registries and

incorporation of biological data. Typically, conventional statistical methods are used for the

development of outcome prediction models and risk scores. However, these analyses carry

inherent properties limiting their ability to cope with large data sets with multiple variables

and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a

wider approach for data analysis termed data mining (DM). It enables prediction in complex

data scenarios, familiar to practitioners and researchers. Technological and commercial

applications are all around us, gradually entering clinical research.

http://www.nature.com/bmt/journal/v49/n3/full/bmt2013146a.html

http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-015-0015-0

Predict-ML Tool for clinical data

https://link.springer.com/article/10.1186/s13755-016-0018-1

Artificial Neural Network

In machine learning and cognitive science, artificial neural networks (ANNs) are a

family of models inspired by biological neural networks (the central nervous systems

of animals, in particular the brain) and are used to estimate or approximate functions

that can depend on a large number of inputs and are generally unknown.

http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol2/ds12/article2.html

http://www.phil.gu.se/ann/annimabintro.pdf

https://www.researchgate.net/publication/228349773_Artificial_Neural_Network_in_Me

dicine

http://onlinelibrary.wiley.com/doi/10.1111/jan.12691/abstract

ANN in medical imaging

http://www.ehealthlab.cs.ucy.ac.cy/oldmedinfo/documents/medinf_03_NeuralNetworks

MedicalImaging.pdf

http://www.ehealthlab.cs.ucy.ac.cy/oldmedinfo/documents/medinf_03_NeuralNetworks

MedicalImaging.pdf

https://pure.strath.ac.uk/portal/files/34307047/medical_imaging_ann_v1.pdf

ANN for breast cancer diagnosis and prognosis

http://neuroph.sourceforge.net/tutorials/PredictingBreastCancer/PredictingBreastCancer

.html

http://www.ijcaonline.org/journal/number26/pxc387783.pdf

http://www.sciencedirect.com/science/article/pii/S0957417408001103

Breast cancer risk predictiML and breast cancer http://newatlas.com/machine-

learning-predicts-breast-cancer-treatment-responses/39510/

on models

https://epi.grants.cancer.gov/cancer_risk_prediction/breast.html

Breast cancer risk assessment tool

https://www.cancer.gov/bcrisktool/

Prognosis of prostate cancer by artificial neural networks

The ANN, yielded high rates of reliability, will help doctors make quick and reliable

diagnoses without any risks and make it a better option to monitor patients with low

prostate cancer risk on whom biopsies must not be carried out through a policy of wait and

see.

http://www.sciencedirect.com/science/article/pii/S095741741000237X

ANN for survival prediction in colon cancer

http://molecular-cancer.biomedcentral.com/articles/10.1186/1476-4598-4-29

ANN advanced technology for forecasting and clustering-NeuroXL

NeuroXL Clusterizer and Predictor are both powerful, easy-to-use and affordable solutions

for advanced prediction and clustering of medical data. Both are designed as add-ins to

Microsoft Excel, are easy to learn and do not require that data be exported out of or

imported into Excel. They provide a cost-effective way to harness the power of artificial

intelligence for a wide variety of applications.

http://neuroxl.com/applications/medicine/neural-networks-in-medicine/index.htm

Predicting prostate biopsy outcome: artificial neural networks and

polychotomous regression are equivalent models

A polychotomous logistic regression (PR) model and an artificial neural network (ANN) for

predicting biopsy results, particularly for clinically significant PC.

https://link.springer.com/article/10.1007/s11255-010-9750-7

ANN in Medicine world map

http://www.phil.gu.se/ann/annworld.html

Other Machine Learning Techniques

Other ML techniques like Bayesian Network has been used for the cancer recurrence

prediction in case of oral cancer, for survival prediction in case of breast cancer and for

susceptibility prediction in case of colon carcinomatosis.

http://www.sciencedirect.com/science/article/pii/S2001037014000464

Support vector Machine: In machine learning, support vector machines (SVMs, also

support vector networks) are supervised learning models with associated learning

algorithms that analyze data used for classification and regression analysis.

http://www.sciencedirect.com/science/article/pii/S1110866510000241

Support Vector Machines combined with Feature selection for breast cancer

diagnosis

http://www.sciencedirect.com/science/article/pii/S0957417408000912

Automated diagnostic systems for breast cancer detection and high precision

accuracy of Support vector Machines

http://www.sciencedirect.com/science/article/pii/S0957417406002442

Cancer prognosis using Support Vector regression in image modality

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3095462/

es/PMC3095462/

Latent Space Support vector machine for cancer diagnosis

http://www.sciencedirect.com/science/article/pii/S1877050914012721

Deep Learning in oncology-Applications in fighting cancer

Deep learning is a class of machine learning algorithm that are based on the (unsupervised)

learning of multiple levels of features or representations of the data. Higher level features

are derived from lower level features to form a hierarchical representation.

Deep Learning plays a vital role in the early detection of cancer. A study published by NVIDIA

showed that deep learning drops error rate for breast cancer 85%.

https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer-diagnosis/

https://www.techemergence.com/deep-learning-in-oncology/

Deep Learning a tool for increased efficiency and accuracy

https://www.nature.com/articles/srep26286

Google Deep learning system for Pathologists

https://9to5google.com/2017/03/03/google-deep-learning-cancer-diagnosis/

https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html

Deep Learning approach for cancer detection and gene expression

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5177447/

https://rpubs.com/EdMwa/241538

Leveraging deep learning to predict breast cancer proliferation scores with

Apache Spark and Apache SystemML

https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56151

http://systemml.apache.org/

DeepGene: An advanced cancer type classifier based on deep

learning and somatic point mutations

Based on deep learning and somatic point mutation data, DeepGene, an advanced cancer

type classifier. Experiments indicate that DeepGene outperforms three widely adopted

existing classifiers, which is mainly attributed to its deep learning module that is able to

extract the high-level features between combinatorial somatic point mutations and cancer

types.

.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-

1334-9

http://ascopubs.org/doi/abs/10.1200/JCO.2017.35.4_suppl.164

MODCELL Predictive Model: Mechanistic predictive model with wide range of

applications in medicine;

● Personalized Medicine

● Virtual Clinical Trials

● Drug Target Identification

http://www.alacris.de/modcell/

Predictive model for cancer*

http://www.oncodesign.com/assets/files/Webinars/Webinar_PDX_Predictive_Cancer_M

odels_for_Better_Precision_Medicine.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC467154

Data types-Genomic and Clinical

Cancer is fundamentally a disease of the genome, caused by changes in the DNA, RNA, and

proteins of a cell that push cell growth into overdrive. Identifying the genomic alterations

that arise in cancer can help researchers decode how cancer develops and improve upon

the diagnosis and treatment of cancers based on their distinct molecular abnormalities.

https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/data-types

http://www.nature.com/articles/srep41674

https://wiki.nci.nih.gov/display/TCGA/Data+Levels+and+Data+Types

Genomic Profiling of Multiple Data types

Genomic profiling of multiple data types in the same set of tumors has gained prominence.

https://academic.oup.com/bioinformatics/article/25/22/2906/180866/Integrative-

clustering-of-multiple-genomic-data

Next Generation Sequencing

Next generation sequencing (NGS), massively parallel or deep sequencing are related terms

that describe a DNA sequencing technology which has revolutionized genomic research.

Using NGS an entire human genome can be sequenced within a single day.

https://www.ncbi.nlm.nih.gov/pubmed/25108476

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841808/

https://www.illumina.com/science/technology/next-generation-sequencing.html

Fighting cancer with AI and DNA sequencing using Big Data

http://www.wired.co.uk/video/fighting-cancer-with-dna-sequencing-big-data-ai

Cancer Genome Atlas

Genome Atlas is a platform to provide understanding of the molecular analysis of cancer

through the application of genome analysis technologies and explore the entire Cancer

spectrum of genomic changes involved in human cancer.

https://tcga-data.nci.nih.gov/docs/publications/tcga/?

https://biospecimens.cancer.gov/relatedinitiatives/overview/tcga.asp

Microarray technology and Gene Expression data

The DNA microarray is a tool used to determine whether the DNA from a particular

individual contains a mutation in genes like BRCA1 and BRCA2. The chip consists of a small

glass plate encased in plastic. Some companies manufacture microarrays using methods

similar to those used to make computer microchips. On the surface, each chip contains

thousands of

short, synthetic, single-stranded DNA sequences, which together add up to the normal gene

in question, and to variants (mutations) of that gene that have been found in the human

population.

http://www.premierbiosoft.com/tech_notes/microarray.html

https://www.genome.gov/10000533/dna-microarray-technology/

Gene expression data for cancer classification and Prediction

Gene Expression is the process by which information from a gene is used in the synthesis of

a functional gene product. These products are often proteins but in non-protein coding

genes such as transfer RNA or small nuclear RNA genes, the product is a functional RNA.

http://hanj.cs.illinois.edu/pdf/is03_cancer.pdf

http://www.sciencedirect.com/science/article/pii/S187705091500561X

https://www.ncbi.nlm.nih.gov/books/NBK6624/

Gene expression profiling and data is used for the classification and prediction of cancer

and neural network is used as a method to get meaningful outcome in this regard.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1282521/

https://www.ncbi.nlm.nih.gov/pubmed/1940735

Microarray and clinical data yielded excellent prediction results using

Artificial neural network

http://ieeexplore.ieee.org/document/5162409/

http://cbbp.thep.lu.se/~markus/publications/papers/nm0601_673.pdf

Artificial intelligence and Genomics

Genomics is the study of genome, the complete set of genetic material within an organism.

Genomics involves sequencing and analysis of genome.

Deep Genomics-AI meets the Human Genome

Using artificial intelligence (AI), deep learning algorithms, and complex data sets, the entire

healthcare industry could be revolutionized — from diagnostics to gene therapies to

personalized medicine. Deep Genomics holds the key to unlocking the biggest disruptions in

the medical, life sciences, and pharmaceutical industries.

https://www.vlab.org/events/deep-genomics/

https://www.deepgenomics.com/news/

AI cracking the Genomic mysteries with Deep Genomics

http://www.businessinsider.com/how-deep-genomics-is-using-ai-to-solve-genetic-

mysteries-2015-9?r=US&IR=T&IR=T

How AI and Genomic sequencing helping cancer patients*(Sophia DDM)

http://www.techrepublic.com/article/how-ai-and-next-generation-genomic-sequencing-

is-helping-cancer-patients/

Intersection of AI and Genomics

https://www.wilsoncenter.org/article/brave-new-world-the-intersection-genomics-and-

artificial-intelligence

DNA Avatar

https://www.wilsoncenter.org/blog-post/your-dna-avatar-what-happens-when-artificial-

intelligence-meets-cutting-edge-genetics

https://aitrends.com/features/deep-genomics-applies-artificial-intelligence-personalized-

medicine/

Genomic Mapping by Deep Learning

http://medcitynews.com/2015/07/deep-learning-artificial-intelligence-genome/

https://www.forbes.com/sites/mikemontgomery/2016/12/22/in-cancer-fight-artificial-

intelligence-is-a-smart-move-for-everyone/#6ab724704064

Watson for Genomics

http://researcher.watson.ibm.com/researcher/view_group.php?id=5347

http://www.healthcareitnews.com/news/ai-can-speed-precision-medicine-new-york-

genome-center-ibm-watson-study-shows

Commercial AI/ML solutions for Oncology

Oncology's Incessant Grip

http://socialarma.com/column/oncologys-incessant-grip

IBM Watson for Oncology

https://www.ibm.com/watson/health/oncology-and-genomics/oncology/

Watson Genomics Analytics for Cancer

http://wwwna.sanfordhealth.org/sioux-falls/watson-genomic-analytics

Watson for Oncology SlideShare

https://www.slideshare.net/InsideDNA/watson-genomics

Integrated Genomic solution for Oncology by Philips and Illumi

http://www.philips.com/a-w/about/news/archive/standard/news/press/2017/20170109-

philips-and-illumina-join-forces-to-offer-integrated-genomics-solutions-for-oncology.htm

Berg´s AI Solution for pancreatic and prostate cancer

http://fortune.com/2015/04/07/pancreatic-cancer-research-berg/

HALO® Deep Learning Technology for Automated Breast Cancer Metastasis

Staging http://tissuepathology.com/2017/05/01/halo-deep-learning-technology-awarded-for-automated-

breast-cancer-metastasis-staging/#axzz4nBj52nv8

Microsoft Fighting Cancer

https://news.microsoft.com/stories/computingcancer/

ai4gi-For gastrointestinal cancer

http://ai4gi.com/our-vision/

Google for treating cancer and Genomic Research

https://edgylabs.com/google-ai-cancer-diagnosis/

http://hitconsultant.net/2014/10/16/google-partners-create-cancer-genomics-cloud/

https://9to5google.com/2017/03/03/google-deep-learning-cancer-diagnosis/

https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html

GENOME 7-Providing cutting edge AI solutions for cancer

http://genome7.com/en/index.cfm#.WXCQeoVOI2w

Amazon and cancer genomic research

http://www.frontlinegenomics.com/review/12588/amazon-cloud-solutions-support-

cancer-genomics-research/

Predicting breast cancer biopsies for malignancies using ML

https://www.bing.com/videos/search?q=ML+commercial+models+for+cancer+prognosis

&&view=detail&mid=4FAA79AC9CA8B26CC2BF4FAA79AC9CA8B26CC2BF&FORM=VRDGA

R

AI diagnosis skin cancer with dermatologist accuracy skin cancer App

https://blogs.nvidia.com/blog/2017/05/23/ai-app-skin-cancer-diagnosis/

https://cosmosmagazine.com/technology/artificial-intelligence-diagnoses-skin-cancers-

as-well-as-dermatologists

https://skinvision.com/

http://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/

Machine Learning Techniques for different cancer types-Survey

Table

Cancer Type

Clinical

Endpoint

Machine

Learning

Algorithm Benchmark

Improvement

(%)

Training

Data Reference

bladder recurrence fuzzy logic statistics 16 mixed

Catto et al,

2003

bladder recurrence ANN N/A N/A clinical

Fujikawa et

al, 2003

bladder survivability ANN N/A N/A clinical Ji et al, 2003

bladder recurrence ANN N/A N/A clinical

Spyridonos

et al, 2002

brain survivability ANN statistics N/A genomic

Wei et al,

2004

breast recurrence clustering statistics N/A mixed

Dai et al,

2005

breast survivability decision tree statistics 4 clinical

Delen et al,

2005

breast susceptibility SVM random 19 genomic

Listgarten et

al, 2004

breast recurrence ANN N/A N/A clinical

Mattfeldt et

al, 2004

breast recurrence ANN N/A N/A mixed

Ripley et al,

2004

breast recurrence ANN statistics 1 clinical

Jerez-

Aragones et

al, 2003

breast survivability ANN statistics N/A clinical

Lisboa et al,

2003

breast

treatment

response ANN N/A N/A proteomic

Mian et al,

2003

breast survivability clustering statistics 0 clinical

Seker et al,

2003

breast survivability fuzzy logic statistics N/A proteomic

Seker et al,

2002

breast survivability SVM N/A N/A clinical

Lee et al,

2000

breast recurrence ANN expert 5 mixed

De Laurentiis

et al, 1999

breast survivability ANN statistics 1 clinical

Lundin et al,

1999

breast recurrence ANN statistics 23 mixed

Marchevsky

et al, 1999

breast recurrence ANN N/A N/A clinical

Naguib et al,

1999

breast survivability ANN N/A N/A clinical Street, 1998

breast survivability ANN expert 5 clinical

Burke et al,

1997

breast recurrence ANN statistics N/A mixed

Mariani et al,

1997

breast recurrence ANN expert 10 clinical

Naguib et al,

1997

cervical survivability ANN N/A N/A mixed

Ochi et al,

2002

colorectal recurrence ANN statistics 12 clinical

Grumett et

al, 2003

colorectal survivability ANN statistics 9 clinical

Snow et al,

2001

colorectal survivability clustering N/A N/A clinical

Hamilton et

al, 1999

colorectal recurrence ANN statistics 9 mixed

Singson et

al, 1999

colorectal survivability ANN expert 11 clinical

Bottaci et al,

1997

esophageal

treatment

response SVM N/A N/A proteomic

Hayashida et

al, 2005

esophageal survivability ANN statistics 3 clinical

Sato et al,

2005

leukemia recurrence decision tree N/A N/A proteomic

Masic et al,

1998

liver recurrence ANN statistics 25 genomic

Rodriguez-

Luna et al,

2005

liver recurrence SVM N/A N/A genomic

Iizuka et al,

2003

liver susceptibility ANN statistics –2 clinical

Kim et al,

2003

liver survivability ANN N/A N/A clinical

Hamamoto

et al, 1995

lung survivability ANN N/A N/A clinical

Santos-

Garcia et al,

2004

lung survivability ANN statistics 9 mixed

Hanai et al,

2003

lung survivability ANN N/A N/A mixed Hsia et al,

2003

lung survivability ANN statistics N/A mixed

Marchevsky

et al, 1998

lung survivability ANN N/A N/A clinical

Jefferson et

al, 1997

lymphoma survivability ANN statistics 22 genomic

Ando et al,

2003

lymphoma survivability ANN expert 10 mixed

Futschik et

al, 2003

lymphoma survivability ANN N/A N/A genomic

O’Neill and

Song, 2003

lymphoma survivability ANN expert N/A genomic

Ando et al,

2002

lymphoma survivability clustering N/A N/A genomic

Shipp et al,

2002

head/neck survivability ANN statistics 11 clinical

Bryce et al,

1998

neck

treatment

response ANN N/A N/A clinical

Drago et al,

2002

ocular survivability SVM N/A N/A genomic

Ehlers and

Harbour,

2005

osteosarcoma

treatment

response SVM N/A N/A genomic

Man et al,

2005

pleural

mesothelioma survivability clustering N/A N/A genomic

Pass et al,

2004

prostate

treatment

response ANN N/A N/A mixed

Michael et al,

2005

prostate recurrence ANN statistics 0 clinical

Porter et al,

2005

prostate

treatment

response ANN N/A N/A clinical

Gulliford et

al, 2004

prostate recurrence ANN statistics 16 mixed

Poulakis et

al, 2004a

prostate recurrence ANN statistics 11 mixed

Poulakis et

al, 2004b

prostate recurrence SVM statistics 6 clinical

Teverovskiy

et al, 2004

prostate recurrence ANN statistics 0 clinical Kattan, 2003

prostate recurrence

genetic

algorithm N/A N/A mixed

Tewari et al,

2001

prostate recurrence ANN statistics 0 clinical

Ziada et al,

2001

prostate susceptibility decision tree N/A N/A clinical Crawford et

al, 2000

prostate recurrence ANN statistics 13 clinical

Han et al,

2000

prostate

treatment

response ANN N/A N/A proteomic

Murphy et al,

2000

prostate recurrence naïve Bayes statistics 1 clinical

Zupan et al,

2000

prostate recurrence ANN N/A N/A clinical

Mattfeldt et

al, 1999

prostate recurrence ANN statistics 17 clinical

Potter et al,

1999

prostate recurrence ANN N/A N/A mixed

Naguib et al,

1998

skin survivability ANN expert 14 clinical

Kaiserman et

al, 2005

skin recurrence ANN expert 27 proteomic

Mian et al,

2005

skin survivability ANN expert 0 clinical

Taktak et al,

2004

skin survivability

genetic

algorithm N/A N/A clinical

Sierra and

Larranga,

1998

stomach Recurrence ANN expert 28 clinical

Bollschweiler

et al, 2004

throat Recurrence fuzzy logic N/A N/A clinical

Nagata et al,

2005

throat Recurrence ANN statistics 0 genomic

Kan et al,

2004

throat survivability decision tree statistics N/A proteomic

Seiwerth et

al, 2000

thoracic

treatment

response ANN N/A N/A clinical

Su et al,

2005

thyroid survivability decision tree statistics N/A clinical

Kukar et al,

1997

tropho- survivability

genetic

algorithm N/A N/A clinical

Marvin et al,

blastic 1999

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675494/

Publications relevant to ML methods used for cancer susceptibility prediction

Publication Ayer T et al. [19]

Waddell M et al. [44]

Listgarten J et al. [45]

Stajadinovic et al. [46]

Method ANN SVM SVM BN

Cancer type Breast cancer Multiple myeloma Breast cancer

Colon carcinomatosis

No of patients 62,219 80 174 53

Type of data Mammographic,

demographic SNPs SNPs Clinical, pathologic

Accuracy AUC = 0.965 71% 69% AUC = 0.71

Validation method

10-fold cross validation

Leave-one-out cross validation

20-fold cross validation Cross-validation

Important features

Age, mammography

findings

snp739514, snp521522, snp994532

snpCY11B2 (+) 4536 T/C

snpCYP1B1 (+) 4328 C/G

Primary tumor histology, nodal

staging, extent of peritoneal cancer

Publications relevant to ML methods used for cancer recurrence prediction

Publication Exarchos K et al.

[24] Kim W et al. [47] Park C et al. [48] Tseng C-J et al.

[49] Eshlaghy A et al. [34]

ML method BN SVM Graph-based SSL

algorithm SVM SVM

Cancer type Oral cancer Breast cancer Colon cancer, breast cancer Cervical cancer

Breast cancer

No of patients 86 679

437 374 168 547

Type of data

Clinical, imaging tissue genomic, blood genomic

Clinical, pathologic,

epidemiologic Gene expression,

PPIs Clinical,

pathologic Clinical,

population

Accuracy 100% 89% 76.7% 80.7% 68% 95%

Validation method

10-fold cross validation Hold-out

10-fold cross validation Hold-out

10-fold cross

validation

Important features

Smoker, p53 stain, extra-tumor

spreading, TCAM, SOD2

Local invasion of tumor

BRCA1, CCND1, STAT1, CCNB1

pathologic_S, pathologic_T, cell type RT

target summary

Age at diagnosis,

age at menarche

Publications relevant to ML methods used for cancer survival prediction

http://www.sciencedirect.com/science/article/pii/S2001037014000464

Publication Chen Y-C et al. [50]

Park K et al. [26]

Chang S-W et al. [32]

Xu X et al. [51]

Gevaert O et al. [52]

Rosado P et al. [53]

Delen D et al. [54]

Kim J et al. [36]

ML method ANN

Graph-based SSL algorithm SVM SVM BN SVM DT

SSL Co-training algorithm

Cancer type

Lung cancer Breast cancer Oral cancer

Breast cancer Breast cancer Oral cancer

Breast cancer Breast cancer

No of patients 440 162,500 31 295 97 69 200,000 162,500

Type of data

Clinical, gene

expression SEER Clinical, genomic Genomic

Clinical, microarray

Clinical, molecular SEER SEER

Accuracy 83.50% 71% 75% 97% AUC = 0.851 98% 93% 76%

Validation method

Cross validation

5-fold cross validation

Cross validation

Leave-one-out cross validation Hold-Out

Cross validation

Cross validation

5-fold cross validation

Important features

Sex, age, T_stage, N_stage LCK and ERBB2 genes

Tumor size, age at

diagnosis, number of

nodes

Drink, invasion, p63 gene

50-gene signature

Age, angioinvasion,

grade MMP9,

HRASLA and RAB27B genes

TNM_stage, number of

recurrences

Age at diagnosis,

tumor size, number of

nodes, histology

Age at diagnosis,

tumor size, number of

nodes, extension of

tumor

Cancer Databases

Databases for oncogenomic research are biological databases dedicated to cancer data and

oncogenomic research. They can be a primary source of cancer data, offer a certain level of

analysis (processed data) or even offer online data mining.

https://en.wikipedia.org/wiki/List_of_databases_for_oncogenomic_research

The National Cancer Database

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234447/

SEER Cancer Research Database

https://healthcaredelivery.cancer.gov/seermedicare

ESMO-European Society for Medical Oncology

http://www.esmo.org/Research/Research-Groups-Databases-and-Tools

NCRI Cancer research database

http://www.ncri.org.uk/what-we-do/research-database/

International/National Research groups by cancer type and topic

http://www.esmo.org/Research/Research-Groups-Databases-and-Tools/Cancer-research-

groups-by-type

Cancer Research Groups in Finland

https://www.docrates.com/en/treatments/patient-satisfaction/finland-leading-country-

in-cancer-care/

Finnish Cancer Registry

http://www.cancer.fi/syoparekisteri/en/research/

Finnish center of Excellence in cancer Genetics Research

http://www.helsinki.fi/coe/

University of Helsinki

http://research.med.helsinki.fi/cancerbio/Research/Index.html

University of Eastern Finland

https://www.sciencedaily.com/releases/2016/06/160621094243.htm

University of Tampere

http://www.uta.fi/bmt/institute/research/nykter/index.html

University of Oulu

http://www.oulu.fi/medicine/research-groups/cancer-research-and-translational-

medicine-research-unit

http://www.oulu.fi/biocenter/research