Results 71 to 80 of about 120,084 (296)

An Optimal Network-Aware Scheduling Technique for Distributed Deep Learning in Distributed HPC Platforms

, 2023
Deep learning is a growing technique used to solve complex artificial intelligence (AI) problems. Large-scale deep learning has become a significant issue as a result of the expansion of datasets and the complexity of deep learning models.
Woojin Seok, Syed Hasnain Raza Shah, Syed Asif Raza Shah, Jeonghoon Moon, Sangkwon Lee, Kihyeon Kim   +5 more
core   +1 more source

Interpreting the effects of DNA polymerase variants at the structural level

Molecular Oncology, EarlyView.
Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi, Karolina Krzesińska, Ludovica Beltrame, Pablo Sánchez‐Izquierdo Besora, Matteo Tiberti, Mef Nilbert, Anna Rohlin, Elena Papaleo   +7 more
wiley   +1 more source

Tackling the Communication Bottlenecks of Distributed Deep Learning Training Workloads

, 2023
Deep Neural Networks (DNNs) find widespread applications across various domains, including computer vision, recommendation systems, and natural language processing.
Ho, Chen-Yu
core   +1 more source

Deep Clustering via Distribution Learning

CoRR
Distribution learning finds probability density functions from a set of data samples, whereas clustering aims to group similar data points to form clusters. Although there are deep clustering methods that employ distribution learning methods, past work still lacks theoretical analysis regarding the relationship between clustering and distribution ...
Guanfang Dong, Zijie Tan, Chenqiu Zhao, Anup Basu   +3 more
openaire   +2 more sources

Flow Enabled Target Capture Halbach‐based magnetic enrichment increases circulating tumor cell capture from blood in metastatic cancer patients

Molecular Oncology, EarlyView.
Pair‐wise comparison of the CellSearch and FETCH enrichment technologies for circulating tumor cells (CTCs) from metastatic breast, prostate, and small cell lung cancer patients shows an increased capture of CTCs using FETCH enrichment. The clinical implementation of circulating tumor cells (CTCs) as a predictive tool for therapy efficacy in the ...
Michiel Stevens, Anouk Mentink, Tom Niessink, Frank Coumans, Leonie Mekenkamp, Ruchi Bansal, Jeroen Hiltermann   +6 more
wiley   +1 more source

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

FEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

DPro-SM – A distributed framework for proactive straggler mitigation using LSTM

Heliyon
The recent advancement in deep learning with growth in big data and high-performance computing is Distributed Deep Learning. The rapid rise in the volume of data and network complexity has led to significant growth in DDL.
Aswathy Ravikumar, Harini Sriraman
doaj   +1 more source

Going Forward-Forward in Distributed Deep Learning [PDF]

We introduce a new approach in distributed deep learning, utilizing Geoffrey Hinton's Forward-Forward (FF) algorithm to speed up the training of neural networks in distributed computing environments. Unlike traditional methods that rely on forward and backward passes, the FF algorithm employs a dual forward pass strategy, significantly diverging from ...
Ege Aktemur, Ege Zorlutuna, Kaan Bilgili, Tacettin Emre Bok, Berrin Yanikoglu, Suha Orhun Mutluergil   +5 more
openaire   +2 more sources

The skills required for transition to university and study in biological sciences: A student perspective

FEBS Open Bio, EarlyView.
Bioscience students were asked for their opinions on the value and teaching of skills. 204 responded that teamwork, time management and study skills are necessary to reach University, that scientific writing, research, laboratory and presentation skills are taught effectively during their studies, while other skills are gained inherently through study ...
Janella Borrell, Susan Crennell
wiley   +1 more source