25 papers · ranked by Valyu relevance
Adi L Tarca, Vincent J Carey, Xue-wen Chen, Roberto Romero + 2 more
Let us consider the general case in which we want to classify a collection of objects i = 1, . . ., n into K predefined classes. For instance, if one wants to distinguish between different types of tumors based on gene expression values, then K would represent the number of known existing tumor types. Without loss of…
Luis Guerra, Laura M McGarry, Víctor Robles, Concha Bielza + 2 more
'Pedro Larrañaga' 'Rafael Yuste'] In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons…
Nadège Dossat, Alain Mangé, Jérôme Solassol, William Jacot + 4 more
'Ludovic Lhermitte' 'Thierry Maudelonde' 'Jean-Pierre Daurès' 'Nicolas Molinari'] Title: Summary: A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations…
Praful Koturwar, Sheetal Girase, Debajyoti Mukhopadhyay
Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users to identify accurate data from huge unstructured data. So…
Víctor Blanco, Kothari, Harshit, James Luedtke
In this paper, we propose a new mathematical optimization model for multiclass classification based on arrangements of hyperplanes. Our approach preserves the core support vector machine (SVM) paradigm of maximizing class separation while minimizing misclassification errors, and it is computationally more efficient…
Firouz Abdullah Al-Wassai, N. V. Kalyankar
This paper focuses on two main issues; first one is the impact of combination of multi-sensor images on the supervised learning classification accuracy using segment Fusion (SF). The second issue attempts to undertake the study of supervised machine learning classification technique of remote sensing images by using…
Md. Siraj-Ud-Doula, Md. Ashad Alam
Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the…
Raeuf Roushangar, George I. Mias
ClassificaIO is an open-source Python graphical user interface (GUI) for machine learning classification for the scikit-learn module. ClassificaIO aims to provide an easy-to-use interactive way to train, validate, and test data on a range of classification algorithms. The GUI enables fast comparisons within and across…
Jiahui Zhong, Minjie Lyu, Huan Jin, Zhiwei Cao + 4 more
Single-cell transcriptome (SCT) sequencing technology has reached the level of high-throughput technology where gene expression can be measured concurrently from large numbers of cells. The results of gene expression studies are highly reproducible when strict protocols and standard operating procedures (SOP) are…
Rimah Amami, Dorra Ben Ayed, Noureddine Ellouze
In this article, we conduct a study on the performance of some supervised learning algorithms for vowel recognition. This study aims to compare the accuracy of each algorithm. Thus, we present an empirical comparison between five supervised learning classifiers and two combined classifiers: SVM, KNN, Naive Bayes…
Nathan T. Johnson, Andi Dhroso, Katelyn J. Hughes, Dmitry Korkin
The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological…
Peng Xie, Mingxuan Gao, Chunming Wang, Pawan Noel + 5 more
Characterization of individual cell types is fundamental to the study of multicellular samples such as tumor tissues. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses…
Vinita Periwal, Jinuraj K Rajappan, Abdul UC Jaleel, Vinod Scaria
Background Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used…
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…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
Johannes Leuschner, Maximilian Schmidt, Pascal Fernsel, Delf Lachmund + 3 more
The NMF methods described in the previous section yield decompositions of the data matrix Y that provide a good approximation using only a small number p of basis patterns (rows of X) and channels (columns of K). Thus, we can expect X to consist of those spectral patterns that were found to be, in some sense, most…
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…
Rahman Salim Zengin, Volkan Sezer
Voronoi tessellations are used to partition the Euclidean space into polyhedral regions, which are called Voronoi cells. Labeling the Voronoi cells with the class information, we can map any classification problem into a Voronoi tessellation. In this way, the classification problem changes into a query of just finding…
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…
Chih-Feng Chao, Ming-Huwi Horng
The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed…
Aida Khozaei, Hadi Moradi, Reshad Hosseini
There are partially separable data types that make classification tasks very hard. In other words, only parts of the data are informative meaning that looking at the rest of the data would not give any distinguishable hint for classification. In this situation, the typical assumption of having the whole labeled data as…
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…
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
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Hyuna Kwon, Zulfikhar Ali, Bryan Wong
Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this…