Browsing by Author "Aghazadeh, Amirali"

Now showing 1 - 3 of 3
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    Current progress and open challenges for applying deep learning across the biosciences
    (Springer Nature, 2022) Sapoval, Nicolae; Aghazadeh, Amirali; Nute, Michael G.; Antunes, Dinler A.; Balaji, Advait; Baraniuk, Richard; Barberan, C.J.; Dannenfelser, Ruth; Dun, Chen; Edrisi, Mohammadamin; Elworth, R.A. Leo; Kille, Bryce; Kyrillidis, Anastasios; Nakhleh, Luay; Wolfe, Cameron R.; Yan, Zhi; Yao, Vicky; Treangen, Todd J.
    Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.
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    Universal microbial diagnostics using random DNA probes
    (2021-08-31) Drezek, Rebekah A.; Baraniuk, Richard G.; Aghazadeh, Amirali; Sheikh, Mona; Lin, Adam Y.; Chen, Allen L.; Bugga, Pallavi; Rice University; United States Patent and Trademark Office
    The present disclosure is directed to compositions and methods present a universal microbial diagnostic (UMD) platform to screen for microbial organisms in a sample using a small number of random DNA probes that are agnostic to the target DNA sequences. The UMD platform can be used to direct and monitor appropriate treatments, thus minimizing the risk of antibiotic resistance, and enhancing patient care.
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    Universal microbial diagnostics using random DNA probes
    (AAAS, 2016) Aghazadeh, Amirali; Lin, Adam Y.; Sheikh, Mona A.; Chen, Allen L.; Atkins, Lisa M.; Johnson, Coreen L.; Petrosino, Joseph F.; Drezek, Rebekah A.; Baraniuk, Richard G.
    Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of a microbial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMDﾒs unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.