Results 41 to 50 of about 3,002,307 (310)

Deep Learning for Forecasting Stock Returns in the Cross-Section

, 2018
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition ...
A Subrahmanyam, AJ Smola, C Krauss, CR Harvey, D Olson, GS Atsalakis, I Goodfellow, L Breiman, L Kryzanowski, Q Cao, RD McLean, S Soni, Y LeCun   +12 more
core   +1 more source

Single‐cell insights into the role of T cells in B‐cell malignancies

FEBS Letters, EarlyView.
Single‐cell technologies have transformed our understanding of T cell–tumor cell interactions in B‐cell malignancies, revealing new T‐cell subsets, functional states, and immune evasion mechanisms. This Review synthesizes these findings, highlighting the roles of T cells in pathogenesis, progression, and therapy response, and underscoring their ...
Laura Llaó‐Cid
wiley   +1 more source

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

FEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan, Lu Qi, Pierre Zalloua   +2 more
wiley   +1 more source

Fairness in Machine Learning [PDF]

, 2020
Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model ...
Oneto L., Chiappa S.
openaire   +4 more sources

Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning

Journal of Cheminformatics
We introduce PoLiGenX, a novel generative model for de novo ligand design that employs latent-conditioned, target-aware equivariant diffusion. Our approach leverages the conditioning of the ligand generation process on reference molecules located within ...
Tuan Le, Julian Cremer, Djork-Arné Clevert, Kristof T. Schütt   +3 more
doaj   +1 more source

Identifying gene-specific subgroups: an alternative to biclustering

BMC Bioinformatics, 2019
Background Transcriptome analysis aims at gaining insight into cellular processes through discovering gene expression patterns across various experimental conditions.
Vincent Branders, Pierre Schaus, Pierre Dupont   +2 more
doaj   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

What is the machine learning? [PDF]

Physical Review D, 2018
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background.
Chang, Spencer, Cohen, Timothy, Ostdiek, Bryan   +2 more
openaire   +3 more sources

Objective criteria for explanations of machine learning models

Applied AI Letters, 2021
Objective criteria to evaluate the performance of machine learning (ML) model explanations are a critical ingredient in bringing greater rigor to the field of explainable artificial intelligence.
Chih‐Kuan Yeh, Pradeep Ravikumar
doaj   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source