Tom Eck

Tom Eck

New York City Metropolitan Area
500+ connections

About

As SVP of Digital Transformation at Fiserv, I focus on digital transformation and…

Articles by Tom

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Experience

  • Fiserv Graphic

    Fiserv

    New York City Metropolitan Area

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    NYC

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    Greater New York City Area

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    New York City Area

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    NJ/NYC

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    Busch Campus, Piscataway, NJ

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    NY/NJ area

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    Center for Advanced Biotech and Medicine

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    NY/NJ area

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Education

  • University of Pennsylvania Graphic

    University of Pennsylvania

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    Activities and Societies: Received $50,000 award to develop my software which identified genetic aberrations linked to specific subclasses of cancer

    Final GPA 4.0

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    GPA 4.0

    I am studying how to apply genomics and cheminformatics to the design of new drugs.

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    Final GPA 4.0

    Thesis project involved developing artificial intelligence software (ART 2 neural network) to identify human voices from very short speech samples. This work was applied and utilized at the Department of Defense, US Army, Electronic Warfare

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    Activities and Societies: Rugby

    Pre-Med
    Computer Science minor

Licenses & Certifications

Volunteer Experience

  • Christian Brothers Academy Graphic

    Founder of Technology and Coding Club

    Christian Brothers Academy

    - Present 8 years 6 months

    Education

    I founded the technology club with my son at his High School so that I can share my knowledge and enthusiasm for programming and building tech solutions.

  • Youth Group Leader

    Numerous churches

    - 17 years

    Children

    Energetic youth group leader who ran meetings, chaperoned many retreats, and led youth group rock bands.

  • Co-founder and inaugural Coach

    Wall Township Girls Lacrosse Club

    - 3 years 1 month

    Children

Publications

  • Detecting Allosteric Sites of HIV-1 Reverse Transcriptase by X-ray Crystallographic Fragment Screening

    Journal of Medicinal Chemistry

    HIV-1 reverse transcriptase (RT) undergoes a series of conformational changes during viral replication and is a central target for antiretroviral therapy. The intrinsic flexibility of RT can provide novel allosteric sites for inhibition. Crystals of RT that diffract X-rays to better than 2 Å resolution facilitated the probing of RT for new druggable sites using fragment screening by X-ray crystallography. A total of 775 fragments were grouped into 143 cocktails, which were soaked into crystals…

    HIV-1 reverse transcriptase (RT) undergoes a series of conformational changes during viral replication and is a central target for antiretroviral therapy. The intrinsic flexibility of RT can provide novel allosteric sites for inhibition. Crystals of RT that diffract X-rays to better than 2 Å resolution facilitated the probing of RT for new druggable sites using fragment screening by X-ray crystallography. A total of 775 fragments were grouped into 143 cocktails, which were soaked into crystals of RT in complex with the non-nucleoside drug rilpivirine (TMC278). Seven new sites were discovered, including the Incoming Nucleotide Binding, Knuckles, NNRTI Adjacent, and 399 sites, located in the polymerase region of RT, and the 428, RNase H Primer Grip Adjacent, and 507 sites, located in the RNase H region. Three of these sites (Knuckles, NNRTI Adjacent, and Incoming Nucleotide Binding) are inhibitory and provide opportunities for discovery of new anti-AIDS drugs.

    Other authors
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  • Preneoplastic lesion growth driven by the death of adjacent normal stem cells

    Proceedings of the National Academy of Sciences

    Clonal expansion of premalignant lesions is an important step in the progression to cancer. This process is commonly considered to be a consequence of sustaining a proliferative mutation. Here, we investigate whether the growth trajectory of clones can be better described by a model in which clone growth does not depend on a proliferative advantage. We developed a simple computer model of clonal expansion in an epithelium in which mutant clones can only colonize space left unoccupied by the…

    Clonal expansion of premalignant lesions is an important step in the progression to cancer. This process is commonly considered to be a consequence of sustaining a proliferative mutation. Here, we investigate whether the growth trajectory of clones can be better described by a model in which clone growth does not depend on a proliferative advantage. We developed a simple computer model of clonal expansion in an epithelium in which mutant clones can only colonize space left unoccupied by the death of adjacent normal stem cells. In this model, competition for space occurs along the frontier between mutant and normal territories, and both the shapes and the growth rates of lesions are governed by the differences between mutant and normal cells' replication or apoptosis rates. The behavior of this model of clonal expansion along a mutant clone's frontier, when apoptosis of both normal and mutant cells is included, matches the growth of UVB-induced p53-mutant clones in mouse dorsal epidermis better than a standard exponential growth model that does not include tissue architecture. The model predicts precancer cell mutation and death rates that agree with biological observations. These results support the hypothesis that clonal expansion of premalignant lesions can be driven by agents, such as ionizing or nonionizing radiation, that cause cell killing but do not directly stimulate cell replication.

    Other authors
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  • STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments

    Journal of Genome Research

    Regions of gain and loss of genomic DNA occur in many cancers and can drive the genesis and progression of disease. These copy number aberrations (CNAs) can be detected at high resolution by using microarray-based techniques. However, robust statistical approaches are needed to identify nonrandom gains and losses across multiple experiments/samples. We have developed a method called Significance Testing for Aberrant Copy number (STAC) to address this need.

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  • Identification of common deletions in breast cancers from non-BRCA1/BRCA2 families using array-based comparative genomic hybridization

    Cancer Research

    Germline mutations in BRCA1 and BRCA2 account for 20-40% of large multiple case breast cancer families. However, breast cancer susceptibility in the majority of other families (termed BRCAX families) is not due to BRCA1 and BRCA2 mutations. Here we applied array-based Comparative Genomic Hybridization (aCGH) to characterize copy number alterations in 44 breast cancers from the 38 BRCAX families currently included in an international collaborative linkage analysis.

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  • An automatic text-free speaker recognition system based on an enhanced Art 2 neural architecture

    Information Sciences

    The adaptive-resonance-theory (ART) architectures comprise a family of neural networks that efficiently self-organize stable recognition codes in response to arbitrary sequences of input patterns. This paper focuses on the ART 2 network architecture that is designed for the processing of both binary- and analog-valued patterns. The problems encountered in implementing the primary ART 2 architecture as originally presented by Gail Carpenter and Stephen Grossberg are discussed. An enhanced ART 2…

    The adaptive-resonance-theory (ART) architectures comprise a family of neural networks that efficiently self-organize stable recognition codes in response to arbitrary sequences of input patterns. This paper focuses on the ART 2 network architecture that is designed for the processing of both binary- and analog-valued patterns. The problems encountered in implementing the primary ART 2 architecture as originally presented by Gail Carpenter and Stephen Grossberg are discussed. An enhanced ART 2 architecture that receives its input from a functional-link preprocessor is proposed. Experimental results that demonstrate this architecture's superior performance over the previous ART 2 architecture are provided. Finally, a text-free speaker recognition system that employs the enhanced ART 2 architecture as its classifier is described.

    Other authors
    • Frank Y. Shih
    See publication

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