Tim Hare Email & Phone Number

New York, Ny, Us

New York, New York, Us

https://www.81qd.com/wp-content/uploads/81qd-_Two-High-Leverage-Points-for-AI-in-Rare-Disease-_Whitepaper.pdfhttps://www.linkedin.com/pulse/95-accurate-100-wrong-rare-disease-tim-hare-1fhttps://pubmed.ncbi.nlm.nih.gov/?term=Hare+TR&cauthor_id=7545321https://www.slideshare.net/tim_hare/simulations-of-multinomial-randomized-responsehttps://www.slideshare.net/tim_hare/naive-bayes-with-conditionally-dependent-datahttps://pmsa.org/jpmsa-vol06-article07https://www.firstwordpharma.com/node/1816307https://www.firstwordpharma.com/node/1811573https://twitter.com/81qd/status/1201894442451525632https://www.pharmavoice.com/digital-edition/nov-dec-2019/#119https://www.biopharmadive.com/spons/the-use-of-ai-in-rare-disease-has-hidden-flaws/599043/https://www.pharmavoice.com/article/2019-01-pharma-ai/https://www.pm360online.com/the-intelligent-ai-consumer/https://www.pharmavoice.com/article/2019-03-small-data/

Blue Bell, Pennsylvania, Us

Requested speaker Intellus Worldwide 2018 Summit2018 PMSA Podium Talk: http://www.pmsa.net/past-conference-presentations/2018/1574-winning-with-analytics-when-the-chips-are-stacked-against-you-lin/file2018 Publication: "Improving Accuracy in Rare Disease Patient Identification Using Pattern Recognition Ensembles" http://www.pmsa.net/jpmsa-vol06-article07"2017 BEST POSTER PRESENTATION AWARD RECIPIENTRedefining Trigger Design Based on Machine Learning ModelingTim Hare and Ewa J. Kleczyk, PhD, Symphony Health SolutionsHigh value physicians are those that are associated with cohorts of patients who have key diagnostic and/or prescribing attributes. Patients that acquire one or more of these key attributes can then be mapped to their associated physician, who are in turn ‘triggered’ for a sales call. The likelihood that a patient will be prescribed a drug is often strongest when a combination of key attributes are present. The space for even a modest number of triggers can be large. For example, testing 10 triggers with 2 states (True/False) represents a search space of 2036 unique binary patterns, each of which needs to be enumerated and tested. Here we present an efficient machine learning approach based on TREE algorithms, to search this space for optimal trigger combinations by building supervised learning classification models, and then extracting the TREE node-specific information as database query rules for the best patient cohorts within each model. These rules can then be used to monitor and trigger physicians. While the search is not exhaustive, it can be conducted in a fraction of the time it takes to test all possible combinations, and the process becomes more efficient as the space grows. "https://twitter.com/symphonyhealth/status/856987131499036672?lang=enhttp://www.pmsa.net/poster-presentations-20172017 PMSA Podium talk: Machine learning in Rare Disease Patient Finding

Blue Bell, Pennsylvania, Us

Data analysis and model development around the full market lifecycle, from predictive market analysis to patient influence, physician prescribing, pharmacy fulfillment, and payer reimbursement. • Novel high dimensional claims data space representations for optimized machine learning • Targeted productivity enhancement with just-in-time rapid application development• Tactical informatics to target customer needs with ultra short cycle times

Jacksonville, Fl, Us

Business-to-Business Data Curation & Validation, Statistical study design consulting, Explanatory and predictive statistical modeling, Machine Learning imputation models in large sparse data sets

Rahway, New Jersey, Us

IT, statistics, informatics consulting and strategic planning for ~40 drug discovery screens per year in a matrix environment.• Six Sigma Green Belt : Identification of false negative risk in high throughput screening. Implementation real time false negative rate risk reduction platform• Implementation of statistical approaches to process improvement in high throughput screening reducing false negative rates by > 50%• Design of high throughput screening QC methods to spot biochemical control instability• Visual Basic for Applications (VBA) Rapid Application Development (RAD) and process automation to eliminate data integrity risk and reduce staff time commitment by > 60-fold• Consultant for internal and external project teams including just in time (JIT) custom data mining platform development using TIBCO Spotfire, Pipeline Pilot, and Visual Basic for Applications (VBA)Publications:• First author, in progress: “Measuring and Reducing False Negative Rates in High Throughput Screening” • Master's Thesis,: “Use of Artificial Neural Networks in the Prediction of Biological Assay Response to Different Chemical Classes as a means to Improve Drug Discovery”• Zhang XD et al, “SSMD-based false discovery and false non-discovery rates in genome-scale RNAi screens”, JBS. 2010: 15(9)

Rahway, New Jersey, Us

Data mining, application development, and consulting for ~40 lead identification screens per year in a matrix environment• Liaison for external partners, design , validation of multimillion dollar laboratory information management system• “Special Achievement Award”: Design and implementation of enhanced data capture pipeline to support the analysis of and additional 14 million data points (~600,000 additional EC/IC50 estimates) per year• “Award for Excellence”: inexpensive VBA-Excel-Oracle system for drug lead impact tracking• Instantiation of key policy of “just in time” rapid application development of VBA tools, resulting in the delivery to staff of 100’s of critical-to-quality applications developed in < 5% standard cycle times• Three instances of “Award for Excellence”: informatics support for two target/lead identification project and instantiation of a design of experiments consulting servicePublications:• Ross DA et al, Multiplexed Assays by High-Content Imaging for Assessment of GPCR Activity, JBS, 2008 Jun 2.

Rahway, New Jersey, Us

Planning, staffing, training, and daily operation of compound management facility housing ~ 800,000 samples to support ~40 high throughput small molecule lead identification screens per year in a matrix environment• Established inventory risk reduction contracts with vendors, specifying goals and damages for non-compliance• “Award for Excellence”: management of laboratory operations and staff during implementation of two rounds of compound storage restructuring to reduce collection screening time and storage cost by 8-16 fold• “Award for Excellence”: production of special sample sets for rapid tactical advantage in lead identification space• Two instances of “Awards for Excellence”: oversight of staff and robotics design team, oversight of factory acceptance and site acceptance for a $500,000 Package PlateTrak replication system, and two REMP automated compound storage systems valued at $2,000,000 and $6,000,000• Design of DMSO-water uptake studies to improve robotic sample containment which delivered time and cost savings for high throughput screening library storage and deploymentPublications:• Antithrombotic efficacy of thrombin inhibitor L-374,087: Intravenous activity in a primate model of venous thrombus extension and oral activity in a canine model of primary venous and coronary artery thrombosis. Cook-JJ, et al. J-Pharmacol-Exp-Ther. 1999 Apr; 289(1)

Rahway, New Jersey, Us

Antithrombotic efficacy of thrombin inhibitor L-374,087: intravenous activity in a primate model of venous thrombus extension and oral activity in a canine model of primary venous and coronary artery thrombosis. Cook JJ, Gardell SJ, Holahan MA, Sitko GR, Stump GL, Wallace AA, Gilberto DB, Hare TR, Krueger JA, Dyer DL, Sanderson PE, Vacca JP, Shafer JA, Lynch JJ Jr.J Pharmacol Exp Ther. 1999 Apr;289(1):503-10. Efficacious, orally bioavailable thrombin inhibitors based on 3-aminopyridinone or 3-aminopyrazinone acetamide peptidomimetic templates. Sanderson PE, Lyle TA, Cutrona KJ, Dyer DL, Dorsey BD, McDonough CM, Naylor-Olsen AM, Chen IW, Chen Z, Cook JJ, Cooper CM, Gardell SJ, Hare TR, Krueger JA, Lewis SD, Lin JH, Lucas BJ Jr, Lyle EA, Lynch JJ Jr, Stranieri MT, Vastag K, Yan Y, Shafer JA, Vacca JP.J Med Chem. 1998 Nov 5;41(23):4466-74.Discovery of a novel, selective, and orally bioavailable class of thrombin inhibitors incorporating aminopyridyl moieties at the P1 position. Feng DM, Gardell SJ, Lewis SD, Bock MG, Chen Z, Freidinger RM, Naylor-Olsen AM, Ramjit HG, Woltmann R, Baskin EP, Lynch JJ, Lucas R, Shafer JA, Dancheck KB, Chen IW, Mao SS, Krueger JA, Hare TR, Mulichak AM, Vacca JP.J Med Chem. 1997 Nov 7;40(23):3726-33.Vampire bat salivary plasminogen activator evokes minimal bleeding relative to tissue-type plasminogen activator as assessed by a rabbit cuticle bleeding time model.Mellott MJ, Ramjit DR, Stabilito II, Hare TR, Senderak ET, Lynch JJ Jr, Gardell SJ.Thromb Haemost. 1995 Mar;73(3):478-83.

Rahway, New Jersey, Us

Vampire bat salivary plasminogen activator promotes robust lysis of plasma clots in a plasma milieu without causing fluid phase plasminogen activation.Hare TR, Gardell SJ. Thromb Haemost. 1992 Aug 3;68(2):165-9.Vampire bat salivary plasminogen activator is quiescent in human plasma in the absence of fibrin unlike human tissue plasminogen activator. Gardell SJ, Hare TR, Bergum PW, Cuca GC, O'Neill-Palladino L, Zavodny SM. Blood. 1990 Dec 15;76(12):2560-4.Additional fucosyl residues on membrane glycoproteins but not a secreted glycoprotein from cystic fibrosis fibroblasts. Wang YM, Hare TR, Won B, Stowell CP, Scanlin TF, Glick MC, Hård K, van Kuik JA, Vliegenthart JF. Clin Chim Acta. 1990 May;188(3):193-210.Purification and characterization of human plasminogen activator inhibitor type I expressed in Saccharomyces cerevisiae. Gardell SJ, Hare TR, Han JH, Markus HZ, Keech BJ, Carty CE, Ellis RW, Schultz LD. Arch Biochem Biophys. 1990 May 1;278(2):467-74.Isolation, characterization, and cDNA cloning of a vampire bat salivary plasminogen activator.Gardell SJ, Duong LT, Diehl RE, York JD, Hare TR, Register RB, Jacobs JW, Dixon RA, Friedman PA.J Biol Chem. 1989 Oct 25;264(30):17947-52.

Master Of Science (Ms), Applied Statistics. Thesis: Random Subspace & Neural Networks In Sparse High Dimensional Modeling

Bachelor'S, Biology: Cum Laude: Honors, Departmental Comprehensive Exams & Honors, Oral Defense, Premed, Biology, Chemistry, Biochemisty

Mathematics: Standard Calculus Sequence, Linear Algebra, Differential Equations Gpa: 3.86