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A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

Cell chemical biology, 2016-10, Vol.23 (10), p.1294-1301

2016

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Artificial Intelligence in Cancer Research and Precision Medicine

Cancer discovery, 2021-04, Vol.11 (4), p.900-915

2021

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A Bayesian machine learning approach for drug target identification using diverse data types

Nature communications, 2019-11, Vol.10 (1), p.5221-14, Article 5221

2019

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The Missing Pieces of Artificial Intelligence in Medicine

Trends in pharmacological sciences (Regular ed.), 2019-08, Vol.40 (8), p.555-564

2019

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Imipridone ONC212 activates orphan G protein-coupled receptor GPR132 and integrated stress response in acute myeloid leukemia

Leukemia, 2019-12, Vol.33 (12), p.2805-2816

2019

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A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product

Cell metabolism, 2017-10, Vol.26 (4), p.648-659.e8

2017

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Organization of enzyme concentration across the metabolic network in cancer cells

PloS one, 2015-01, Vol.10 (1), p.e0117131-e0117131

2015

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Cancer stem cell-related gene expression as a potential biomarker of response for first-in-class imipridone ONC201 in solid tumors

PloS one, 2017-08, Vol.12 (8), p.e0180541

2017

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Dopamine Receptor D5 is a Modulator of Tumor Response to Dopamine Receptor D2 Antagonism

Clinical cancer research, 2019-04, Vol.25 (7), p.2305-2313

2019

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A machine learning and network framework to discover new indications for small molecules

PLoS computational biology, 2020-08, Vol.16 (8), p.e1008098-e1008098

2020

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Prediction of Genetic Interactions Using Machine Learning and Network Properties

Frontiers in bioengineering and biotechnology, 2015-01, Vol.3, p.172

2015

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Abstract 395: Prediction of genomic based drug sensitivity signatures to enable optimal drug positioning

Cancer research (Chicago, Ill.), 2021-07, Vol.81 (13_Supplement), p.395-395

2021

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Abstract B031: OneThree Biotech’s artificial intelligence platform for optimizing drug development

Molecular cancer therapeutics, 2019-12, Vol.18 (12_Supplement), p.B031-B031

2019

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Abstract 219: Identifying genetic interactions resulting form diverse biological mechanisms to inform cancer drug development

Cancer research (Chicago, Ill.), 2021-07, Vol.81 (13_Supplement), p.219-219

2021

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Abstract 3896: A big data approach to predicting context specific synergistic drug combinations

Cancer research (Chicago, Ill.), 2018-07, Vol.78 (13_Supplement), p.3896-3896

2018

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Abstract B142: A “moneyball” approach to predicting clinical trial failures and successes

Molecular cancer therapeutics, 2015-12, Vol.14 (12_Supplement_2), p.B142-B142

2015

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Abstract 5039: A data driven approach to predicting tissue-specific adverse events

Cancer research (Chicago, Ill.), 2017-07, Vol.77 (13_Supplement), p.5039-5039

2017

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Abstract 682: Drug repurposing engine fueled by diverse drug similarity data

Cancer research (Chicago, Ill.), 2019-07, Vol.79 (13_Supplement), p.682-682

2019

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Abstract 5063: Predicting target driven toxicity for small molecule inhibitors to aid in drug development

Cancer research (Chicago, Ill.), 2022-06, Vol.82 (12_Supplement), p.5063-5063

2022

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Abstract 5062: Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development

Cancer research (Chicago, Ill.), 2022-06, Vol.82 (12_Supplement), p.5062-5062

2022

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Abstract 1563: A machine learning approach to predict platform specific gene essentiality in cancer

Cancer research (Chicago, Ill.), 2017-07, Vol.77 (13_Supplement), p.1563-1563

2017

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CLIP-170S is a microtubule +TIP variant that confers resistance to taxanes by impairing drug-target engagement

Developmental cell, 2021-12, Vol.56 (23), p.3264-3275.e7

2021

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Abstract 3817: Systems biology identifies that Gleevec reverses taxane resistance in solid tumors by selective inhibition of a novel +Tip microtubule-binding variant

Cancer research (Chicago, Ill.), 2019-07, Vol.79 (13_Supplement), p.3817-3817

2019

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Abstract 641: Systems biology identifies Gleevec as a specific inhibitor of CLIP-170S, a novel +TIP isoform, which causes taxane resistance in cancer cells and patients by obstructing the Microtubule pore

Cancer research (Chicago, Ill.), 2020-08, Vol.80 (16_Supplement), p.641-641

2020

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Abstract LB-106: Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance

Cancer research (Chicago, Ill.), 2016-07, Vol.76 (14_Supplement), p.LB-106-LB-106

2016

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A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

Artificial Intelligence in Cancer Research and Precision Medicine

A Bayesian machine learning approach for drug target identification using diverse data types

The Missing Pieces of Artificial Intelligence in Medicine

Imipridone ONC212 activates orphan G protein-coupled receptor GPR132 and integrated stress response in acute myeloid leukemia

A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product

Organization of enzyme concentration across the metabolic network in cancer cells

Cancer stem cell-related gene expression as a potential biomarker of response for first-in-class imipridone ONC201 in solid tumors

Dopamine Receptor D5 is a Modulator of Tumor Response to Dopamine Receptor D2 Antagonism

A machine learning and network framework to discover new indications for small molecules

Prediction of Genetic Interactions Using Machine Learning and Network Properties

Abstract 395: Prediction of genomic based drug sensitivity signatures to enable optimal drug positioning

Abstract B031: OneThree Biotech’s artificial intelligence platform for optimizing drug development

Abstract 219: Identifying genetic interactions resulting form diverse biological mechanisms to inform cancer drug development

Abstract 3896: A big data approach to predicting context specific synergistic drug combinations

Abstract B142: A “moneyball” approach to predicting clinical trial failures and successes

Abstract 5039: A data driven approach to predicting tissue-specific adverse events

Abstract 682: Drug repurposing engine fueled by diverse drug similarity data

Abstract 5063: Predicting target driven toxicity for small molecule inhibitors to aid in drug development

Abstract 5062: Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development

Abstract 1563: A machine learning approach to predict platform specific gene essentiality in cancer

CLIP-170S is a microtubule +TIP variant that confers resistance to taxanes by impairing drug-target engagement

Abstract 3817: Systems biology identifies that Gleevec reverses taxane resistance in solid tumors by selective inhibition of a novel +Tip microtubule-binding variant

Abstract 641: Systems biology identifies Gleevec as a specific inhibitor of CLIP-170S, a novel +TIP isoform, which causes taxane resistance in cancer cells and patients by obstructing the Microtubule pore

Abstract LB-106: Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance

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