Artificial neural networks based optimization techniques: A review
In the last few years, intensive research has been done to enhance artificial intelligence (AI)
using optimization techniques. In this paper, we present an extensive review of artificial …
using optimization techniques. In this paper, we present an extensive review of artificial …
Artificial intelligence and machine learning technology driven modern drug discovery and development
C Sarkar, B Das, VS Rawat, JB Wahlang… - International Journal of …, 2023 - mdpi.com
The discovery and advances of medicines may be considered as the ultimate relevant
translational science effort that adds to human invulnerability and happiness. But advancing …
translational science effort that adds to human invulnerability and happiness. But advancing …
Artificial intelligence in drug discovery and development
KK Mak, YH Wong, MR Pichika - Drug Discovery and Evaluation: Safety …, 2023 - Springer
This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …
discovery and development, encapsulating its potentials, methodologies, real-world …
A survey of traffic prediction: from spatio-temporal data to intelligent transportation
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes
K Shafin, T Pesout, R Lorig-Roach, M Haukness… - Nature …, 2020 - nature.com
De novo assembly of a human genome using nanopore long-read sequences has been
reported, but it used more than 150,000 CPU hours and weeks of wall-clock time. To enable …
reported, but it used more than 150,000 CPU hours and weeks of wall-clock time. To enable …
Understanding deep learning techniques for image segmentation
The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …
based approaches. Many challenging computer vision tasks, such as detection, localization …
From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy
FJ Rang, WP Kloosterman, J de Ridder - Genome biology, 2018 - Springer
Nanopore sequencing is a rapidly maturing technology delivering long reads in real time on
a portable instrument at low cost. Not surprisingly, the community has rapidly taken up this …
a portable instrument at low cost. Not surprisingly, the community has rapidly taken up this …
A survey of clustering with deep learning: From the perspective of network architecture
Clustering is a fundamental problem in many data-driven application domains, and
clustering performance highly depends on the quality of data representation. Hence, linear …
clustering performance highly depends on the quality of data representation. Hence, linear …
Deep learning with long short-term memory networks for financial market predictions
Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence
learning. They are less commonly applied to financial time series predictions, yet inherently …
learning. They are less commonly applied to financial time series predictions, yet inherently …
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …