Saket Navlakha

Memorial Sloan Kettering Cancer Center

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Ziv Bar−Joseph

Carnegie Mellon University

Sanjoy Dasgupta

University of California, San Diego

Carl Kingsford

Carnegie Mellon University

Yang Shen

Hohai University

Sabrina Mohd Rashid

Northern University of Malaysia

Arjun Chandrasekhar

University of Pittsburgh

Jonathan Y. Suen

Cold Spring Harbor Laboratory

Alison L. Barth

Carnegie Mellon University

Zoltán N. Oltvai

University of Pittsburgh

Shashank Singh

Jawaharlal Institute of Post Graduate Medical Education and Research

Cooperative Institutions

Cold Spring Harbor Laboratory

Salk Institute for Biological Studies

University of California, San Diego

Carnegie Mellon University

University of Maryland, College Park

University of Pittsburgh

Howard Hughes Medical Institute

Memorial Sloan Kettering Cancer Center

Cornell University

Center for the Neural Basis of Cognition

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Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging

BMC Bioinformatics (2013)

Saket Navlakha Parvez Ahammad Eugene W. Myers

Segmenting electron microscopy (EM) images of cellular and subcellular processes in the nervous system is a key step in many bioimaging pipelines involving classification and labeling of ultrastructures. However, fully automated techniques to segment images are often susceptible to noise and heterogeneity in EM images (e.g. different histological preparations, different organisms, different brain regions, etc.). Supervised techniques to address this problem are often helpful but require large sets of training data, which are often difficult to obtain in practice, especially across many conditions. We propose a new, principled unsupervised algorithm to segment EM images using a two-step approach: edge detection via salient watersheds following by robust region merging. We performed experiments to gather EM neuroimages of two organisms (mouse and fruit fly) using different histological preparations and generated manually curated ground-truth segmentations. We compared our algorithm against several state-of-the-art unsupervised segmentation algorithms and found superior performance using two standard measures of under-and over-segmentation error. Our algorithm is general and may be applicable to other large-scale segmentation problems for bioimages.

Ground truth

Connectomics

Market Segmentation

10.1186/1471-2105-14-294

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A neural algorithm for computing bipartite matchings

Proceedings of the National Academy of Sciences (2024)

Sanjoy Dasgupta Yaron Meirovitch Xingyu Zheng Inle Bush Jeff W. Lichtman

Finding optimal bipartite matchings—e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review—is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons “compete” with each other to “win” muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems.

Pruning

10.1073/pnas.2321032121

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BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Amitava Banerjee David Pattinson Cornelia L. Wincek Paul Bunk Sarah R. Chapin

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.

Benchmarking

Transferability

10.1101/2024.01.22.576714

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Mobility Is Medicine, Too: Creating a Culture of Mobility Amongst Hospitalized Patients With Cancer to Improve Patient Outcomes

Cancer Care Research Online (2024)

Sejal Morjaria Claire Carmody Saket Navlakha Wei Zhou Donna Braccia

Background: Despite decades of evidence demonstrating the effectiveness of multidisciplinary mobility initiatives in improving patient clinical outcomes, the impact of mobility programs in oncology-specific settings has been ignored. Objective: The objective of this initiative is to test evidence-based mobility interventions in a cancer care center to create a culture among clinicians that prioritizes patient mobilization. Methods: We compared postintervention and preintervention outcomes using logistic regression analysis and time-to-event modeling to assess hospital length of stay. Basic statistical methods assessed whether improving mobility influenced clinical outcomes and clinician culture. Results: Outcome data from 493 in the postintervention arm were compared to 498 patients in the preintervention arm. Patients in the postintervention group had 39% decreased odds of having the rapid response team called (confidence interval [CI] =0.39–0.97; P = .03) and 46% decreased odds of being admitted to the intensive care unit (CI = 0.29–1.02; P = .05) compared to the preintervention group. No safety issues were associated with these interventions. Conclusions: Hospitalized patients with cancer are especially prone to considerable debility due to their disease and treatment effects. Our initiative to create a mobility protocol in 1 medical unit resulted in positive clinical outcomes. Implications for Practice: Findings from this study can be used to increase recognition of the benefits of mobility programs for hospitalized cancer patients. What is Foundational: Promoting mobility in the hospital often involves collaboration among various healthcare professionals, including nurses, physical therapists, occupational therapists, and physicians. This interdisciplinary approach ensures that patients receive holistic care tailored to their specific needs.

10.1097/cr9.0000000000000057

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Network Design and the Brain

Trends in Cognitive Sciences (2017)

Saket Navlakha Ziv Bar−Joseph Alison L. Barth

Biological neural network

10.1016/j.tics.2017.09.012

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The power of protein interaction networks for associating genes with diseases

Bioinformatics (2010)

Saket Navlakha Carl Kingsford

Abstract Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques. Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet Contact: carlk@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

10.1093/bioinformatics/btq076

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Citations (357)

A distributed algorithm to maintain and repair the trail networks of arboreal ants

bioRxiv (Cold Spring Harbor Laboratory) (2017)

Arjun Chandrasekhar Deborah M. Gordon Saket Navlakha

Abstract We study how the arboreal turtle ant ( Cephalotes goniodontus ) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm’s success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

Arboreal locomotion

10.1101/194480

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A bacterial based distributed gradient descent model for mass scale evacuations

Swarm and Evolutionary Computation (2019)

Sabrina Mohd Rashid Shashank Singh Saket Navlakha Ziv Bar−Joseph

10.1016/j.swevo.2019.01.011

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Distributed Gradient Descent in Bacterial Food Search

arXiv (Cornell University) (2016)

Shashank Singh Sabrina Mohd Rashid Zhicheng Long Saket Navlakha Hanna Salman

Communication and coordination play a major role in the ability of bacterial cells to adapt to ever changing environments and conditions. Recent work has shown that such coordination underlies several aspects of bacterial responses including their ability to develop antibiotic resistance. Here we develop a new distributed gradient descent method that helps explain how bacterial cells collectively search for food in harsh environments using extremely limited communication and computational complexity. This method can also be used for computational tasks when agents are facing similarly restricted conditions. We formalize the communication and computation assumptions required for successful coordination and prove that the method we propose leads to convergence even when using a dynamically changing interaction network. The proposed method improves upon prior models suggested for bacterial foraging despite making fewer assumptions. Simulation studies and analysis of experimental data illustrate the ability of the method to explain and further predict several aspects of bacterial swarm food search.

10.48550/arxiv.1604.03052

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Can a Fruit Fly Learn Word Embeddings

Yuchen Liang Chaitanya K. Ryali Benjamin Hoover Lea T. Grinberg Saket Navlakha

The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representation of the inputs. In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task. We show that this network can learn semantic representations of words and can generate both static and context-dependent word embeddings. Unlike conventional methods (e.g., BERT, GloVe) that use dense representations for word embedding, our algorithm encodes semantic meaning of words and their context in the form of sparse binary hash codes. The quality of the learned representations is evaluated on word similarity analysis, word-sense disambiguation, and document classification. It is shown that not only can the fruit fly network motif achieve performance comparable to existing methods in NLP, but, additionally, it uses only a fraction of the computational resources (shorter training time and smaller memory footprint).

Memory footprint

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