Browsing by Author "Elmarakeby, Haitham A."
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- Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screenMenden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham A.; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, Donatien Chedom; Fourches, Denis; Friend, Stephen; Frigessi, Arnoldo; Gao, Feng; Gao, Xiaoting; Gerold, Jeffrey M.; Gestraud, Pierre; Ghosh, Samik; Gillberg, Jussi; Godoy-Lorite, Antonia; Godynyuk, Lizzy; Godzik, Adam; Goldenberg, Anna; Gomez-Cabrero, David; Gonen, Mehmet; de Graaf, Chris; Gray, Harry; Grechkin, Maxim; Guimera, Roger; Guney, Emre; Haibe-Kains, Benjamin; Han, Younghyun; Hase, Takeshi; He, Di; He, Liye; Heath, Lenwood S.; Hellton, Kristoffer H.; Helmer-Citterich, Manuela; Hidalgo, Marta R.; Hidru, Daniel; Hill, Steven M.; Hochreiter, Sepp; Hong, Seungpyo; Hovig, Eivind; Hsueh, Ya-Chih; Hu, Zhiyuan; Huang, Justin K.; Huang, R. Stephanie; Hunyady, Laszlo; Hwang, Jinseub; Hwang, Tae Hyun; Hwang, Woochang; Hwang, Yongdeuk; Isayev, Olexandr; Walk, Oliver Bear Don't; Jack, John; Jahandideh, Samad; Ji, Jiadong; Jo, Yousang; Kamola, Piotr J.; Kanev, Georgi K.; Karacosta, Loukia; Karimi, Mostafa; Kaski, Samuel; Kazanov, Marat; Khamis, Abdullah M.; Khan, Suleiman Ali; Kiani, Narsis A.; Kim, Allen; Kim, Jinhan; Kim, Juntae; Kim, Kiseong; Kim, Kyung; Kim, Sunkyu; Kim, Yongsoo; Kim, Yunseong; Kirk, Paul D. W.; Kitano, Hiroaki; Klambauer, Gunter; Knowles, David; Ko, Melissa; Kohn-Luque, Alvaro; Kooistra, Albert J.; Kuenemann, Melaine A.; Kuiper, Martin; Kurz, Christoph; Kwon, Mijin; van Laarhoven, Twan; Laegreid, Astrid; Lederer, Simone; Lee, Heewon; Lee, Jeon; Lee, Yun Woo; Leppaho, Eemeli; Lewis, Richard; Li, Jing; Li, Lang; Liley, James; Lim, Weng Khong; Lin, Chieh; Liu, Yiyi; Lopez, Yosvany; Low, Joshua; Lysenko, Artem; Machado, Daniel; Madhukar, Neel; De Maeyer, Dries; Malpartida, Ana Belen; Mamitsuka, Hiroshi; Marabita, Francesco; Marchal, Kathleen; Marttinen, Pekka; Mason, Daniel; Mazaheri, Alireza; Mehmood, Arfa; Mehreen, Ali; Michaut, Magali; Miller, Ryan A.; Mitsopoulos, Costas; Modos, Dezso; Van Moerbeke, Marijke; Moo, Keagan; Motsinger-Reif, Alison; Movva, Rajiv; Muraru, Sebastian; Muratov, Eugene; Mushthofa, Mushthofa; Nagarajan, Niranjan; Nakken, Sigve; Nath, Aritro; Neuvial, Pierre; Newton, Richard; Ning, Zheng; De Niz, Carlos; Oliva, Baldo; Olsen, Catharina; Palmeri, Antonio; Panesar, Bhawan; Papadopoulos, Stavros; Park, Jaesub; Park, Seonyeong; Park, Sungjoon; Pawitan, Yudi; Peluso, Daniele; Pendyala, Sriram; Peng, Jian; Perfetto, Livia; Pirro, Stefano; Plevritis, Sylvia; Politi, Regina; Poon, Hoifung; Porta, Eduard; Prellner, Isak; Preuer, Kristina; Angel Pujana, Miguel; Ramnarine, Ricardo; Reid, John E.; Reyal, Fabien; Richardson, Sylvia; Ricketts, Camir; Rieswijk, Linda; Rocha, Miguel; Rodriguez-Gonzalvez, Carmen; Roell, Kyle; Rotroff, Daniel; de Ruiter, Julian R.; Rukawa, Ploy; Sadacca, Benjamin; Safikhani, Zhaleh; Safitri, Fita; Sales-Pardo, Marta; Sauer, Sebastian; Schlichting, Moritz; Seoane, Jose A.; Serra, Jordi; Shang, Ming-Mei; Sharma, Alok; Sharma, Hari; Shen, Yang; Shiga, Motoki; Shin, Moonshik; Shkedy, Ziv; Shopsowitz, Kevin; Sinai, Sam; Skola, Dylan; Smirnov, Petr; Soerensen, Izel Fourie; Soerensen, Peter; Song, Je-Hoon; Song, Sang Ok; Soufan, Othman; Spitzmueller, Andreas; Steipe, Boris; Suphavilai, Chayaporn; Tamayo, Sergio Pulido; Tamborero, David; Tang, Jing; Tanoli, Zia-ur-Rehman; Tarres-Deulofeu, Marc; Tegner, Jesper; Thommesen, Liv; Tonekaboni, Seyed Ali Madani; Tran, Hong T.; De Troyer, Ewoud; Truong, Amy; Tsunoda, Tatsuhiko; Turu, Gabor; Tzeng, Guang-Yo; Verbeke, Lieven; Videla, Santiago; Vis, Daniel; Voronkov, Andrey; Votis, Konstantinos; Wang, Ashley; Wang, Hong-Qiang Horace; Wang, Po-Wei; Wang, Sheng; Wang, Wei; Wang, Xiaochen; Wang, Xin; Wennerberg, Krister; Wernisch, Lorenz; Wessels, Lodewyk; van Westen, Gerard J. P.; Westerman, Bart A.; White, Simon Richard; Willighagen, Egon; Wurdinger, Tom; Xie, Lei; Xie, Shuilian; Xu, Hua; Yadav, Bhagwan; Yau, Christopher; Yeerna, Huwate; Yin, Jia Wei; Yu, Michael; Yu, MinHwan; Yun, So Jeong; Zakharov, Alexey; Zamichos, Alexandros; Zanin, Massimiliano; Zeng, Li; Zenil, Hector; Zhang, Frederick; Zhang, Pengyue; Zhang, Wei; Zhao, Hongyu; Zhao, Lan; Zheng, Wenjin; Zoufir, Azedine; Zucknick, Manuela (Springer Nature, 2019-06-17)The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
- A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in ArabidopsisGrene, Ruth; Heath, Lenwood S.; Li, Song; Collakova, Eva; Elmarakeby, Haitham A.; Ni, Ying; Aghamirzaie, Delasa (Frontiers, 2016-12-23)Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.
- Similarity-based codes sequentially assigned to ebolavirus genomes are informative of species membership, associated outbreaks, and transmission chains.Weisberg, Alexandra J.; Elmarakeby, Haitham A.; Heath, Lenwood S.; Vinatzer, Boris A. (2015-01)Background. Developing a universal standardized microbial typing and nomenclature system that provides phylogenetic and epidemiological information in real time has never been as urgent in public health as it is today. We previously proposed to use genome similarity as the basis for immediate and precise typing and naming of individual organisms or viruses. In this study, we tested the validity of the proposed system and applied it to the epidemiology of infectious diseases using Ebola virus disease (EVD) outbreaks as the example. Methods. One hundred twenty-eight publicly available ebolavirus genomes were compared with each other, and average nucleotide identity (ANI) was calculated. The ANI was then used to assign unique codes, hereafter referred to as Life Identification Numbers (LINs), to every viral isolate, whereby each LIN consisted of a series of positions reflecting increasing genome similarity. Congruence of LINs with phylogenetic and epidemiological relationships was then determined. Results. Assigned LINs correlate with phylogeny at the species and infraspecies level and can even identify some individual transmission chains during the 2014-2015 EVD epidemic in West Africa. Conclusions. Life Identification Numbers can provide a fast, automated, standardized, and scalable approach to precisely identify and name viral isolates upon genome sequence submission, facilitating unambiguous communication during disease epidemics among clinicians, epidemiologists, and governments.
- Systematic auditing is essential to debiasing machine learning in biologyEid, Fatma-Elzahraa; Elmarakeby, Haitham A.; Chan, Yujia Alina; Fornelos, Nadine; ElHefnawi, Mahmoud; Van Allen, Eliezer M.; Heath, Lenwood S.; Lage, Kasper (2021-02-10)Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications. Fatma-Elzahraa Eid et al. illustrate a principled approach for identifying biases that can inflate the performance of biological machine learning models. When applied to three biomedical prediction problems, they identify previously unrecognized biases and ultimately show that models are likely to learn primarily from data biases when there is insufficient learnable signal in the data.