Adityanarayanan Radhakrishnan
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Explore the profile of Adityanarayanan Radhakrishnan including associated specialties, affiliations and a list of published articles.
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11
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153
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Recent Articles
1.
Carlson R, Patten J, Stefanakis G, Soong B, Radhakrishnan A, Singh A, et al.
bioRxiv
. 2024 Apr;
PMID: 38617272
Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide...
2.
Radhakrishnan A, Beaglehole D, Pandit P, Belkin M
Science
. 2024 Mar;
383(6690):1461-1467.
PMID: 38452048
Understanding how neural networks learn features, or relevant patterns in data, for prediction is necessary for their reliable use in technological and scientific applications. In this work, we presented a...
3.
Cai C, Radhakrishnan A, Uhler C
bioRxiv
. 2023 Dec;
PMID: 38106093
Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively...
4.
Radhakrishnan A, Ruiz Luyten M, Prasad N, Uhler C
Nat Commun
. 2023 Sep;
14(1):5570.
PMID: 37689796
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are...
5.
Radhakrishnan A, Friedman S, Khurshid S, Ng K, Batra P, Lubitz S, et al.
Nat Commun
. 2023 Apr;
14(1):2436.
PMID: 37105979
A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework...
6.
Radhakrishnan A, Belkin M, Uhler C
Proc Natl Acad Sci U S A
. 2023 Mar;
120(14):e2208779120.
PMID: 36996114
While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standard procedures are consistent for classification,...
7.
Radhakrishnan A, Stefanakis G, Belkin M, Uhler C
Proc Natl Acad Sci U S A
. 2022 Apr;
119(16):e2115064119.
PMID: 35412891
Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational...
8.
Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing
Belyaeva A, Cammarata L, Radhakrishnan A, Squires C, Yang K, Shivashankar G, et al.
Nat Commun
. 2021 Feb;
12(1):1024.
PMID: 33589624
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been...
9.
Yang K, Belyaeva A, Venkatachalapathy S, Damodaran K, Katcoff A, Radhakrishnan A, et al.
Nat Commun
. 2021 Jan;
12(1):31.
PMID: 33397893
The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on...
10.
Radhakrishnan A, Belkin M, Uhler C
Proc Natl Acad Sci U S A
. 2020 Oct;
117(44):27162-27170.
PMID: 33067397
Identifying computational mechanisms for memorization and retrieval of data is a long-standing problem at the intersection of machine learning and neuroscience. Our main finding is that standard overparameterized deep neural...