26 papers · ranked by Valyu relevance
Daniel R. Schrider, Andrew D. Kern
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we…
Juan Jovel, Russell Greiner
Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data-e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of…
Narjice Chafai, Ichrak Hayah, Isidore Houaga, Bouabid Badaoui
The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal…
Zhaoyi Xu, Joseph H. Saleh
Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented…
Cheng Xu, Ling-Yun Zhao, Cun-Si Ye, Ke-Chen Xu + 1 more
With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of…
Camilla Mapstone, Berenika Plusa
The rapid increase in the amount of available biological data together with increasing computational power and innovative new machine learning algorithms has resulted in great potential for machine learning approaches to revolutionise image analysis in developmental biology and clinical embryology. In this Spotlight…
Francisco A. Rodrigues
– Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as…
Taehoon Kim
| 1 | | Introduction to Key Concepts in Machine Learning and Deep Learning | 12 | | --- | --- | --- | --- | | | 1.1 | Introduction to Machine Learning | 12 | | | 1.2 | Supervised Learning Fundamentals | 13 | | | 1.2.1 | Generalization to New Examples | 14 | | | 1.2.2 | Underfitting and Overfitting | 14 | | | 1.3 |…
Carlos Pacheco, Carlos Francisco Brito Loeza
Se examina el problema de aprendizaje supervisado en su planteación continua. Posteriormente se da una condición de optimalidad general a través de técnicas del análisis funcional y el cálculo de variaciones. Esto nos permite resolver la condición de optimalidad para la función deseada u numéricamente y hacer varias…
Osvaldo Simeone
—Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper…
Lingfei Mo, Gang Wang, Erhong Long, Mingsong Zhuo
Spiking neural network (SNN) is considered to be the brain-like model that best conforms to the biological mechanism of the brain. Due to the non-differentiability of the spike, the training method of SNNs is still incomplete. This paper proposes a supervised learning method for SNNs based on associative learning…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…
Duccio Fanelli, Luca Bindi, Lorenzo Chicchi, Claudio Pereti + 2 more
Neural networks are gaining widespread relevance for their versatility, holding the promise to yield a significant methodological shift in different domain of applied research. Here, we provide a simple pedagogical account of the basic functioning of a feedforward neural network. Then we move forward to reviewing two…
Daniel R. Schrider, Andrew D. Kern
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we…
Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev + 6 more
Traditional small molecule drug discovery is a time consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine learning methods for virtual chemical screening enables training models on known active and…
Anthony M. Zador
Over the last decade, artificial neural networks (ANNs), have undergone a revolution, catalyzed in large part by better tools for supervised learning. However, training such networks requires enormous data sets of labeled examples, whereas young animals (including humans) typically learn with few or no labeled…
Hyebin Song, Bennett J. Bremer, Emily C. Hinds, Garvesh Raskutti + 1 more
Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It’s challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related…
Stefan P. Schmid, Leon Schlosser, Frank Glorius, Kjell Jorner
Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied…
Alexander P. Keil, Daniel Westreich, Jessie K. Edwards, Stephen R. Cole
'Stephen R. Cole'] Background and objective Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. An implementation of stacking, called super learning, has been developed as a general approach to supervised…
E. Moebel, C. Kervrann
Cryo electron tomography visualizes native cells at nanometer resolution, but analysis is challenged by noise and artifacts. Recently, supervised deep learning methods have been applied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown objects, unsupervised classification…
Ashley I. Naimi, Laura B. Balzer
Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into what is now known as “Super Learner”. Super Learner uses V -fold cross-validation to build the optimal…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Raheel Hammad, Sownyak Mondal
Auxetics are a rare class of materials that exhibit a negative Poisson's ratio. The existence of these auxetic materials is rare but has a large number of applications in designing exotic materials. We build a complete machine learning framework to detect Auxetic materials as well as Poisson's ratio of non-auxetic…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Yue Lyu, Steven Hsesheng Lin, Xuelin Huang, Ziyi Li
This paper introduces SuperSurv, a user-friendly R package for building, evaluating, and interpreting ensemble models for right-censored survival data. Although many survival modeling methods are available, existing tools are often model-specific and lack a unified platform for systematically integrating, comparing…