Sach Mukherjee
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View article: Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data
Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data Open
Homologous recombination deficiency (HRD) leads to genomic instability, and patients with HRD can benefit from HRD‐targeting therapies. Previous studies have primarily focused on identifying HRD biomarkers using data from a single technolo…
View article: Large Language Models for Zero-shot Inference of Causal Structures in Biology
Large Language Models for Zero-shot Inference of Causal Structures in Biology Open
Genes, proteins and other biological entities influence one another via causal molecular networks. Causal relationships in such networks are mediated by complex and diverse mechanisms, through latent variables, and are often specific to ce…
View article: Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments Open
Causal structure learning (CSL) refers to the task of learning causal relationships from data. Advances in CSL now allow learning of causal graphs in diverse application domains, which has the potential to facilitate data-driven causal dec…
View article: Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification
Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification Open
View article: High-dimensional undirected graphical models for arbitrary mixed data
High-dimensional undirected graphical models for arbitrary mixed data Open
Graphical models are an important tool in exploring relationships between variables in complex, multivariate data.Methods for learning such graphical models are well-developed in the case where all variables are either continuous or discre…
View article: Detecting Homologous Recombination Deficiency for Breast Cancer Through Integrative Analysis of Genomic Data
Detecting Homologous Recombination Deficiency for Breast Cancer Through Integrative Analysis of Genomic Data Open
View article: Development and validation of a reliable DNA copy-number-based machine learning algorithm (<i>CopyClust</i>) for breast cancer integrative cluster classification
Development and validation of a reliable DNA copy-number-based machine learning algorithm (<i>CopyClust</i>) for breast cancer integrative cluster classification Open
The Integrative Clusters (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct genomic subtypes based on DNA copy number and gene expression. Current classifiers achieve only low accuracy without g…
View article: Improved baselines for causal structure learning on interventional data
Improved baselines for causal structure learning on interventional data Open
Causal structure learning (CSL) refers to the estimation of causal graphs from data. Causal versions of tools such as ROC curves play a prominent role in empirical assessment of CSL methods and performance is often compared with “random” b…
View article: Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers Open
We propose a method for learning dynamical systems from high-dimensional empirical data that combines variational autoencoders and (spatio-)temporal attention within a framework designed to enforce certain scientifically-motivated invarian…
View article: Deep Learning of Causal Structures in High Dimensions
Deep Learning of Causal Structures in High Dimensions Open
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for…
View article: High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data Open
Graphical models are an important tool in exploring relationships between variables in complex, multivariate data. Methods for learning such graphical models are well developed in the case where all variables are either continuous or discr…
View article: Human variation in population-wide gene expression data predicts gene perturbation phenotype
Human variation in population-wide gene expression data predicts gene perturbation phenotype Open
View article: Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state
Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state Open
Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chlorop…
View article: Author response: Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state
Author response: Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state Open
Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract Omics-based technologies are driving…
View article: Scalable Regularised Joint Mixture Models
Scalable Regularised Joint Mixture Models Open
In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the heterogeneity can affect both predictive performance and in…
View article: Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data
Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data Open
This is the official test datasets of "Generalization of deep recurrent optical flow estimation for particle-image velocimetry data" published in Measurement Science and Technology. Particle-Image Velocimetry (PIV) is one of the key techni…
View article: Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data
Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data Open
This is the official test datasets of "Generalization of deep recurrent optical flow estimation for particle-image velocimetry data" published in Measurement Science and Technology. Particle-Image Velocimetry (PIV) is one of the key techni…
View article: On unsupervised projections and second order signals
On unsupervised projections and second order signals Open
Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class discriminatory signals are retained under projection is cru…
View article: Decoding mechanism of action and susceptibility to drug candidates from integrated transcriptome and chromatin state
Decoding mechanism of action and susceptibility to drug candidates from integrated transcriptome and chromatin state Open
Omics-based technologies are driving major advances in precision medicine but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropi…
View article: The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up Open
Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and…
View article: Causation as a High-Level Affair
Causation as a High-Level Affair Open
View article: Swarm Learning for decentralized and confidential clinical machine learning
Swarm Learning for decentralized and confidential clinical machine learning Open
View article: Tailored Bayes: a risk modeling framework under unequal misclassification costs
Tailored Bayes: a risk modeling framework under unequal misclassification costs Open
Summary Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification …
View article: Tailored Bayes: a risk modelling framework under unequal misclassification costs
Tailored Bayes: a risk modelling framework under unequal misclassification costs Open
Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors a…
View article: Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data
Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data Open
This is the official dataset of Recurrent All-Pairs Field Transforms for Particle Image Velocimetry Data (RAFT-PIV) published in Nature Machine Intelligence. In this work, we propose a deep neural network-based approach for learning displa…
View article: Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data
Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data Open
This is the official dataset of Recurrent All-Pairs Field Transforms for Particle Image Velocimetry Data (RAFT-PIV) published in Nature Machine Intelligence. In this work, we propose a deep neural network-based approach for learning displa…
View article: Penalized longitudinal mixed models with latent group structure, with an application in neurodegenerative diseases
Penalized longitudinal mixed models with latent group structure, with an application in neurodegenerative diseases Open
S ummary Large-scale longitudinal data are often heterogeneous, spanning latent subgroups such as disease subtypes. In this paper, we present an approach called longitudinal joint cluster regression (LJCR) for penalized mixed modelling in …
View article: The Future of Data Science
The Future of Data Science Open
View article: Evaluation of Causal Structure Learning Algorithms via Risk Estimation
Evaluation of Causal Structure Learning Algorithms via Risk Estimation Open
Recent years have seen many advances in methods for causal structure learning from data. The empirical assessment of such methods, however, is much less developed. Motivated by this gap, we pose the following question: how can one assess, …
View article: Inferring causal molecular networks: empirical assessment through a community-based effort
Inferring causal molecular networks: empirical assessment through a community-based effort Open
Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here…