Results 241 to 250 of about 24,059,316 (329)

Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models Identifies the Most Important Salt Stress–Responsive Pathways in Microalga Dunaliella

, 2019
Bahman Panahi, Mohammad Frahadian, Jacob T. Dums, Mohammad Amin Hejazi   +3 more
openalex   +1 more source

Predictors of response and rational combinations for the novel MCL‐1 inhibitor MIK665 in acute myeloid leukemia

Molecular Oncology, EarlyView.
This study characterizes the responses of primary acute myeloid leukemia (AML) patient samples to the MCL‐1 inhibitor MIK665. The results revealed that monocytic differentiation is associated with MIK665 sensitivity. Conversely, elevated ABCB1 expression is a potential biomarker of resistance to the treatment, which can be overcome by the combination ...
Joseph Saad, Rhiannon Newman, Elmira Khabusheva, Sofia Aakko, Eric Durand, Mahesh Tambe, Heikki Kuusanmäki, Alun Parsons, Juho J. Miettinen, Komal Kumar Javarappa, Ezgi June Olgac, Nemo Ikonen, Mika Kontro, Kimmo Porkka, Heiko Maacke, Janghee Woo, Ensar Halilovic, Caroline A. Heckman   +17 more
wiley   +1 more source

Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.

J Med Internet Res
Ju L, Guo Y, Guo H, Liu R, Wang Y, Wang S, Ma N, Ren J.   +7 more
europepmc   +1 more source

Evaluating genetic-based disease prediction approaches through simulation. [PDF]

Hum Genet
Shpak M, Parfitt E, Mahmoudiandehkordi S, Maadooliat M, Schrodi SJ.   +4 more
europepmc   +1 more source

Aggressive prostate cancer is associated with pericyte dysfunction

Molecular Oncology, EarlyView.
Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero, Ane Martinez‐Larrinaga, Joaquim Grego‐Bessa, Saioa Garcia‐Longarte, Hielke van Splunder, Ianire Astobiza, Amaia Ercilla, Laura Bozal‐Basterra, Isabel Mendizabal, Pilar Villacampa, Arkaitz Carracedo, Mariona Graupera   +11 more
wiley   +1 more source

Benchmarking survival machine learning models for 10-year cardiovascular disease risk prediction using large-scale electronic health records. [PDF]

Digit Health
Liu T, Krentz A, Lu L, Wang Y, Curcin V.
europepmc   +1 more source

Tumor and germline testing with next generation sequencing in epithelial ovarian cancer: a prospective paired comparison using an 18‐gene panel

Molecular Oncology, EarlyView.
Genetic testing in epithelial ovarian cancer includes both germline and tumor‐testing. This approach often duplicates resources. The current prospective study assessed the feasibility of tumor‐first multigene testing by comparing tumor tissue with germline testing of peripheral blood using an 18‐gene NGS panel in 106 patients.
Elisabeth Spenard, Cristina Mitric, Melanie Care, Tracy L. Stockley, Raymond H. Kim, Jeanna McCuaig, Blaise Clarke, Laura Ranich, Clare Sheen, Sarah E. Ferguson, Liat Hogen, Taymaa May, Marcus Q. Bernardini   +12 more
wiley   +1 more source

Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective. [PDF]

Sci Rep
Alsulami AG, Alharbi A, Khan UA, Al-Hejri AM, Alshamrani SS, Almotiri J.   +5 more
europepmc   +1 more source

Barriers to adopting simulation modelling in construction industry

, 2019
Mohammed Abdelmegid, Vicente A. González, Mani Poshdar, Michael O’Sullivan, Cameron Walker, Fei Ying   +5 more
openalex   +2 more sources

The neural crest‐associated gene ERRFI1 is involved in melanoma progression and resistance toward targeted therapy

Molecular Oncology, EarlyView.
ERRFI1, a neural crest (NC)‐associated gene, was upregulated in melanoma and negatively correlated with the expression of melanocytic differentiation markers and the susceptibility of melanoma cells toward BRAF inhibitors (BRAFi). Knocking down ERRFI1 significantly increased the sensitivity of melanoma cells to BRAFi.
Nina Wang, Qian Sun, Daniel Novak, Lei Zhu, Juliane Poelchen, Tamara Steinfass, Yiman Wang, Viktor Umansky, Jochen Utikal   +8 more
wiley   +1 more source