Results 41 to 50 of about 1,129,295 (286)

Online Learning for Statistical Machine Translation [PDF]

Computational Linguistics, 2016
We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT—for example, in the context of translation agencies or the daily translation of government proceedings.
openaire   +3 more sources

Classification Based on Decision Tree Algorithm for Machine Learning

Journal of Applied Science and Technology Trends, 2021
Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision ...
Bahzad Charbuty, Adnan Abdulazeez
semanticscholar   +1 more source

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

Machine Learning in Official Statistics

, 2018
In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and ...
Beck, Martin, Dumpert, Florian, Feuerhake, Joerg   +2 more
openaire   +2 more sources

Machine learning, statistical learning and the future of biological research in psychiatry [PDF]

Psychological Medicine, 2016
Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may ...
Iniesta, R.; Stahl, D.; McGuffin, P.
openaire   +6 more sources

Circulating tumor DNA monitoring and blood tumor mutational burden in patients with metastatic solid tumors treated with atezolizumab

Molecular Oncology, EarlyView.
In patients treated with atezolizumab as a part of the MyPathway (NCT02091141) trial, pre‐treatment ctDNA tumor fraction at high levels was associated with poor outcomes (radiographic response, progression‐free survival, and overall survival) but better sensitivity for blood tumor mutational burden (bTMB).
Charles Swanton, Russell W. Madison, Candice Francheska B. Tambaoan, Funda Meric‐Bernstam, Christopher J. Sweeney, Razelle Kurzrock, Howard A. Burris III, David R. Spigel, Hanna Tukachinsky, Jason Hughes, Julia Malato, Bongin Yoo, Tania Szado, Cheryl Schwab, Lincoln W. Pasquina, Amaya Gasco, Katja Schulze, Claire F. Friedman   +17 more
wiley   +1 more source

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

Molecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima, Yuko Takami, Ken Kawabe, Kazuhiro Tanabe, Chihiro Hayashi, Mikio Mikami, Tetsuya Kusumoto   +6 more
wiley   +1 more source

Unveiling unique protein and phosphorylation signatures in lung adenocarcinomas with and without ALK, EGFR, and KRAS genetic alterations

Molecular Oncology, EarlyView.
Proteomic and phosphoproteomic analyses were performed on lung adenocarcinoma (LUAD) tumors with EGFR, KRAS, or EML4–ALK alterations and wild‐type cases. Distinct protein expression and phosphorylation patterns were identified, especially in EGFR‐mutated tumors. Key altered pathways included vesicle transport and RNA splicing.
Fanni Bugyi, Mirjam Balbisi, Simon Sugár, Lóránd Váncza, Eszter Regős, Ilona Kovalszky, Ibolya Laczó, Tünde Harkó, Gábor Kecskeméti, Zoltán Szabó, Judit Moldvay, László Drahos, Lilla Turiák   +12 more
wiley   +1 more source

Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and Evaluation Using Traditional Statistics and Machine Learning [PDF]

Biomedicines
Paola A, Daniela M, Carlos F, Enrique G, Eduardo R, Nancy S, Diego R, Manuel V, Matias C, Yanitza G, Doris S, Catalina O, Jaime S, Mercedes O, Reggie G.   +14 more
europepmc   +2 more sources

Tumor‐agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell‐free DNA fragmentomics

Molecular Oncology, EarlyView.
We evaluated circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer using DNA methylation, cell‐free DNA fragment lengths, and 5′ end motifs. Machine learning models were trained to estimate ctDNA levels from each feature and their combination.
Morten Lapin, Kjersti Tjensvoll, Karin Hestnes Edland, Satu Oltedal, Herish Garresori, Bjørnar Gilje, Saga Ekedal, Trygve Eftestøl, Jan Terje Kvaløy, Filip Janku, Oddmund Nordgård   +10 more
wiley   +1 more source