Search Results - "Dhruba, Saugato Rahman"

Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks by Bazgir, Omid, Zhang, Ruibo, Dhruba, Saugato Rahman, Rahman, Raziur, Ghosh, Souparno, Pal, Ranadip

“…Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive…”
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Functional random forest with applications in dose-response predictions by Rahman, Raziur, Dhruba, Saugato Rahman, Ghosh, Souparno, Pal, Ranadip

“…Drug sensitivity prediction for individual tumors is a significant challenge in personalized medicine. Current modeling approaches consider prediction of a…”
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Application of transfer learning for cancer drug sensitivity prediction by Dhruba, Saugato Rahman, Rahman, Raziur, Matlock, Kevin, Ghosh, Souparno, Pal, Ranadip

“…In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale…”
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Active Shooter Detection in Multiple-Person Scenario Using RF-Based Machine Vision by Bazgir, Omid, Nolte, Daniel, Dhruba, Saugato Rahman, Li, Yiran, Li, Changzhi, Ghosh, Souparno, Pal, Ranadip

“…Emerging applications of radio frequency (RF) vision sensors for security and gesture recognition primarily target single individual scenarios which restricts…”
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A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade by Sahni, Sahil, Wang, Binbin, Wu, Di, Dhruba, Saugato Rahman, Nagy, Matthew, Patkar, Sushant, Ferreira, Ingrid, Day, Chi-Ping, Wang, Kun, Ruppin, Eytan

“…Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop I…”
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Evaluating the consistency of large-scale pharmacogenomic studies by Rahman, Raziur, Dhruba, Saugato Rahman, Matlock, Kevin, De-Niz, Carlos, Ghosh, Souparno, Pal, Ranadip

“…Abstract Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer…”
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Recursive model for dose-time responses in pharmacological studies by Dhruba, Saugato Rahman, Rahman, Aminur, Rahman, Raziur, Ghosh, Souparno, Pal, Ranadip

“…Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation,…”
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Tuning force field parameters of ionic liquids using machine learning techniques by Islam, Rafikul, Kabir, Md Fauzul, Dhruba, Saugato Rahman, Afroz, Khurshida

“…•Machine Learning (ML) technique was used to tune the force field parameters of ionic liquids.•The best tuned force field parameters were predicted by the…”
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REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks by Bazgir, Omid, Zhang, Ruibo, Dhruba, Saugato Rahman, Rahman, Raziur, Ghosh, Souparno, Pal, Ranadip

“…Deep learning with Convolutional Neural Networks has shown great promise in various areas of image-based classification and enhancement but is often unsuitable…”
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Abstract 473: GeneMasking: A new approach for inferring the functional role of cancer driver genes and its application in prostate cancer by Dhruba, Saugato Rahman, Amitay, Niv, Wolf, Lior, Ruppin, Eytan

“…Introduction: Genomic events in cancer driver genes such as mutations and copy number alterations play critical roles in cancer onset and progression. Here we…”
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Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors by SInha, Sanju, Vegesna, Rahulsimham, Dhruba, Saugato Rahman, Wu, Wei, Kerr, D. Lucas, Stroganov, Oleg V., Grishagin, Ivan, Aldape, Kenneth D., Blakely, Collin M., Jiang, Peng, Thomas, Craig J, Bivona, Trever G., Schaffer, Alejandro, Ruppin, Eytan

“…e20540 Background: Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and…”
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Abstract LB245: Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design by Madan, Sanna, Chang, Tiangen, Wang, Binbin, Dhruba, Saugato Rahman, Schäffer, Alejandro A., Ruppin, Eytan

“…The advancement of chimeric antigen receptor (CAR) T-cell therapy has been groundbreaking in the treatment of hematological malignancies. However, its…”
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Integrated multiomics analysis identifies molecular landscape perturbations during hyperammonemia in skeletal muscle and myotubes by Welch, Nicole, Singh, Shashi Shekhar, Kumar, Avinash, Dhruba, Saugato Rahman, Mishra, Saurabh, Sekar, Jinendiran, Bellar, Annette, Attaway, Amy H., Chelluboyina, Aruna, Willard, Belinda B., Li, Ling, Huo, Zhiguang, Karnik, Sadashiva S., Esser, Karyn, Longworth, Michelle S., Shah, Yatrik M., Davuluri, Gangarao, Pal, Ranadip, Dasarathy, Srinivasan

“…Ammonia is a cytotoxic molecule generated during normal cellular functions. Dysregulated ammonia metabolism, which is evident in many chronic diseases such as…”
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Abstract LB002: Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to immune checkpoint blockade by Sahni, Sahil, Wang, Binbin, Wu, Di, Dhruba, Saugato Rahman, Nagy, Matthew, Patkar, Sushant, Ferreira, Ingrid, Wang, Kun, Ruppin, Eytan

“…Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance often develops. To learn more about ICB resistance mechanisms, we developed…”
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Abstract LB242: Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome by Dhruba, Saugato Rahman, Sahni, Sahil, Wang, Binbin, Wu, Di, Schmidt, Yael, Shulman, Eldad, Sinha, Sanju, Sammut, Stephen-John, Caldas, Carlos, Wang, Kun, Ruppin, Eytan

“…Introduction: The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays critical roles in tumor development and clinical outcome. While…”
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Abstract 1411: Cell-type-specific transcriptomic immune aging clocks reveal clinically relevant associations with chronic illnesses including cancer by Gurevich-Schmidt, Yael, Wang, Kun, Wu, Di, Madan, Sanna, Gopalan, Vishaka, Sinha, Sanju, Wang, Binbin, Dhruba, Saugato Rahman, Schäffer, Alejandro A., Ruppin, Eytan

“…The aging of the immune system has profound implications for individual immune responses, yet precise quantification of immune age remains a challenge…”
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LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features by Chang, Tian-Gen, Cao, Yingying, Sfreddo, Hannah J, Dhruba, Saugato Rahman, Lee, Se-Hoon, Valero, Cristina, Yoo, Seong-Keun, Chowell, Diego, Morris, Luc G T, Ruppin, Eytan

“…Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we…”
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PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors by Sinha, Sanju, Vegesna, Rahulsimham, Mukherjee, Sumit, Kammula, Ashwin V, Dhruba, Saugato Rahman, Wu, Wei, Kerr, D Lucas, Nair, Nishanth Ulhas, Jones, Matthew G, Yosef, Nir, Stroganov, Oleg V, Grishagin, Ivan, Aldape, Kenneth D, Blakely, Collin M, Jiang, Peng, Thomas, Craig J, Benes, Cyril H, Bivona, Trever G, Schäffer, Alejandro A, Ruppin, Eytan

“…Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized…”
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Dimensionality Reduction based Transfer Learning applied to Pharmacogenomics Databases by Dhruba, Saugato Rahman, Rahmanl, Raziur, Matlockl, Kevin, Ghosh, Soupatno, Pal, Ranadip

“…Recent years have observed a number of Pharmacogenomics databases being published that enable testing of various predictive modeling techniques for…”
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An investigation of proteomic data for application in precision medicine by Matlock, Kevin, Dhruba, Saugato Rahman, Nazir, Moazzam, Pal, Ranadip

“…The majority of cancer drug sensitivity models are built utilizing genomic data measured before drug application to predict the steady state sensitivity of an…”
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