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  1. AU=Ramkumar Pavan
  2. AU="Araujo, Deusa Teixeira"
  3. AU=Yang Mengling
  4. AU=Bria Carley
  5. AU="Melesse, Yekatit"
  6. AU="Camacho-Aguayo, Javier"
  7. AU="Zhou, Cong"
  8. AU="Hubner, P"
  9. AU="Suppanz, Ida"
  10. AU="Maria do Rosário Gondim Peixoto"
  11. AU="Lehto, Timo-Pekka K"
  12. AU="Bhattacharya, Joyeeta"
  13. AU="Gabriela Lopez‐Gonzalez"
  14. AU="South, Andrew M."
  15. AU="Skei, Sigmund"
  16. AU="Gillet, Jean‐François"
  17. AU="McKee, Mary"
  18. AU="Hajar Vaseghi"
  19. AU="M.Haney, "
  20. AU="R. Kappenberger"
  21. AU="Sempoux, Christine" AU="Sempoux, Christine"
  22. AU=Choi Wonseon
  23. AU=Gudowska-Sawczuk Monika AU=Gudowska-Sawczuk Monika
  24. AU="Roufosse, Florence"
  25. AU=Rugolotto Silvana
  26. AU="Herron, Benjamin"
  27. AU="Fernández Menéndez, Jorge"
  28. AU="Nguyen, Hang Tt"
  29. AU="Tahiri, M"
  30. AU=Zhang Ying-Ying
  31. AU="Warkaye, Samson"
  32. AU="Bavor, Claire"

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  1. Artikel ; Online: Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar.

    Ramkumar, Pavan / Kato, Saul / Escola, G Sean

    Patterns (New York, N.Y.)

    2022  Band 3, Heft 4, Seite(n) 100490

    Abstract: Sean Escola, Saul Kato, and Pavan Ramkumar explain the importance of data science in their research ...

    Abstract Sean Escola, Saul Kato, and Pavan Ramkumar explain the importance of data science in their research. They have developed a simple non-parametric statistical method called the Rank-to-Group (RTG) score that identifies hierarchical confounder effects in raw data and machine learning-derived data embeddings. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in machine learning models.
    Sprache Englisch
    Erscheinungsdatum 2022-04-08
    Erscheinungsland United States
    Dokumenttyp News
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2022.100490
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer.

    Ramkumar, M / Kalirajan, K / Kumar, U Pavan / Surya, P

    Biomedical engineering letters

    2023  Band 14, Heft 2, Seite(n) 221–233

    Abstract: Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the ...

    Abstract Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the field of medicine including lung cancer. Lung cancer treatment is possible if it is detected at the initial stage. Radiologists have great difficulty identifying and categorizing lung cancers in the initial phase. So, several methods were used to predict the lung cancer but does not provide accurate solutions with increased error rate. To overcome these issues, a Deep Volcanic Residual U-Net (DVR U-Net) for nodal metastasis is proposed in this manuscript which identifies the LC accurately in the early stage. Initially, the input images are taken from two datasets. After that, these input data are pre-processed using Anisotropic Diffusion Filter with a Fuzzy based Contrast-Limited Adaptive Histogram Equalization (ADFFCLAHE) method
    Sprache Englisch
    Erscheinungsdatum 2023-10-31
    Erscheinungsland Germany
    Dokumenttyp Journal Article
    ZDB-ID 2602422-6
    ISSN 2093-985X ; 2093-9868
    ISSN (online) 2093-985X
    ISSN 2093-9868
    DOI 10.1007/s13534-023-00332-5
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Hierarchical confounder discovery in the experiment-machine learning cycle.

    Rogozhnikov, Alex / Ramkumar, Pavan / Bedi, Rishi / Kato, Saul / Escola, G Sean

    Patterns (New York, N.Y.)

    2022  Band 3, Heft 4, Seite(n) 100451

    Abstract: The promise of machine learning (ML) to extract insights from high-dimensional datasets is tempered by confounding variables. It behooves scientists to determine if a model has extracted the desired information or instead fallen prey to bias. Due to ... ...

    Abstract The promise of machine learning (ML) to extract insights from high-dimensional datasets is tempered by confounding variables. It behooves scientists to determine if a model has extracted the desired information or instead fallen prey to bias. Due to features of natural phenomena and experimental design constraints, bioscience datasets are often organized in nested hierarchies that obfuscate the origins of confounding effects and render confounder amelioration methods ineffective. We propose a non-parametric statistical method called the rank-to-group (RTG) score that identifies hierarchical confounder effects in raw data and ML-derived embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders when linear methods fail. In a public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover crossmodal correlated variability in a multi-phenotypic biological dataset. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in ML models.
    Sprache Englisch
    Erscheinungsdatum 2022-02-22
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2022.100451
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: Testing independent component patterns by inter-subject or inter-session consistency.

    Hyvärinen, Aapo / Ramkumar, Pavan

    Frontiers in human neuroscience

    2013  Band 7, Seite(n) 94

    Abstract: Independent component analysis (ICA) is increasingly used to analyze patterns of spontaneous activity in brain imaging. However, there are hardly any methods for answering the fundamental question: are the obtained components statistically significant? ... ...

    Abstract Independent component analysis (ICA) is increasingly used to analyze patterns of spontaneous activity in brain imaging. However, there are hardly any methods for answering the fundamental question: are the obtained components statistically significant? Most methods considering the significance of components either consider group-differences or use arbitrary thresholds with weak statistical justification. In previous work, we proposed a statistically principled method for testing if the coefficients in the mixing matrix are similar in different subjects or sessions. In many applications of ICA, however, we would like to test the reliability of the independent components themselves and not the mixing coefficients. Here, we develop a test for such an inter-subject consistency by extending our previous theory. The test is applicable, for example, to the spatial activity patterns obtained by spatial ICA in resting-state fMRI. We further improve both this and the previously proposed testing method by introducing a new way of correcting for multiple testing, new variants of the clustering method, and a computational approximation which greatly reduces the memory and computation required.
    Sprache Englisch
    Erscheinungsdatum 2013-03-22
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2425477-0
    ISSN 1662-5161
    ISSN 1662-5161
    DOI 10.3389/fnhum.2013.00094
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Population coding of conditional probability distributions in dorsal premotor cortex

    Joshua I. Glaser / Matthew G. Perich / Pavan Ramkumar / Lee E. Miller / Konrad P. Kording

    Nature Communications, Vol 9, Iss 1, Pp 1-

    2018  Band 14

    Abstract: Movements are continually constrained by the current body position and its relation to the surroundings. Here the authors report that the population activity of monkey dorsal premotor cortex neurons dynamically represents the probability distribution of ... ...

    Abstract Movements are continually constrained by the current body position and its relation to the surroundings. Here the authors report that the population activity of monkey dorsal premotor cortex neurons dynamically represents the probability distribution of possible reach directions.
    Schlagwörter Science ; Q
    Sprache Englisch
    Erscheinungsdatum 2018-05-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Population coding of conditional probability distributions in dorsal premotor cortex

    Joshua I. Glaser / Matthew G. Perich / Pavan Ramkumar / Lee E. Miller / Konrad P. Kording

    Nature Communications, Vol 9, Iss 1, Pp 1-

    2018  Band 14

    Abstract: Movements are continually constrained by the current body position and its relation to the surroundings. Here the authors report that the population activity of monkey dorsal premotor cortex neurons dynamically represents the probability distribution of ... ...

    Abstract Movements are continually constrained by the current body position and its relation to the surroundings. Here the authors report that the population activity of monkey dorsal premotor cortex neurons dynamically represents the probability distribution of possible reach directions.
    Schlagwörter Science ; Q
    Sprache Englisch
    Erscheinungsdatum 2018-05-01T00:00:00Z
    Verlag Nature Publishing Group
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel ; Online: Population coding of conditional probability distributions in dorsal premotor cortex.

    Glaser, Joshua I / Perich, Matthew G / Ramkumar, Pavan / Miller, Lee E / Kording, Konrad P

    Nature communications

    2018  Band 9, Heft 1, Seite(n) 1788

    Abstract: Our bodies and the environment constrain our movements. For example, when our arm is fully outstretched, we cannot extend it further. More generally, the distribution of possible movements is conditioned on the state of our bodies in the environment, ... ...

    Abstract Our bodies and the environment constrain our movements. For example, when our arm is fully outstretched, we cannot extend it further. More generally, the distribution of possible movements is conditioned on the state of our bodies in the environment, which is constantly changing. However, little is known about how the brain represents such distributions, and uses them in movement planning. Here, we record from dorsal premotor cortex (PMd) and primary motor cortex (M1) while monkeys reach to randomly placed targets. The hand's position within the workspace creates probability distributions of possible upcoming targets, which affect movement trajectories and latencies. PMd, but not M1, neurons have increased activity when the monkey's hand position makes it likely the upcoming movement will be in the neurons' preferred directions. Across the population, PMd activity represents probability distributions of individual upcoming reaches, which depend on rapidly changing information about the body's state in the environment.
    Mesh-Begriff(e) Animals ; Brain Mapping ; Hand ; Haplorhini ; Motor Cortex/physiology ; Movement/physiology ; Probability ; Psychomotor Performance/physiology
    Sprache Englisch
    Erscheinungsdatum 2018-05-03
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-018-04062-6
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: Modeling peripheral visual acuity enables discovery of gaze strategies at multiple time scales during natural scene search.

    Ramkumar, Pavan / Fernandes, Hugo / Kording, Konrad / Segraves, Mark

    Journal of vision

    2015  Band 15, Heft 3

    Abstract: Like humans, monkeys make saccades nearly three times a second. To understand the factors guiding this frequent decision, computational models of vision attempt to predict fixation locations using bottom-up visual features and top-down goals. How do the ... ...

    Abstract Like humans, monkeys make saccades nearly three times a second. To understand the factors guiding this frequent decision, computational models of vision attempt to predict fixation locations using bottom-up visual features and top-down goals. How do the relative influences of these factors evolve over multiple time scales? Here we analyzed visual features at fixations using a retinal transform that provides realistic visual acuity by suitably degrading visual information in the periphery. In a task in which monkeys searched for a Gabor target in natural scenes, we characterized the relative importance of bottom-up and task-relevant influences by decoding fixated from nonfixated image patches based on visual features. At fast time scales, we found that search strategies can vary over the course of a single trial, with locations of higher saliency, target-similarity, edge–energy, and orientedness looked at later on in the trial. At slow time scales, we found that search strategies can be refined over several weeks of practice, and the influence of target orientation was significant only in the latter of two search tasks. Critically, these results were not observed without applying the retinal transform. Our results suggest that saccade-guidance strategies become apparent only when models take into account degraded visual representation in the periphery.
    Mesh-Begriff(e) Animals ; Computer Simulation ; Female ; Fixation, Ocular/physiology ; Macaca mulatta ; Pattern Recognition, Visual/physiology ; Saccades/physiology ; Visual Acuity/physiology
    Sprache Englisch
    Erscheinungsdatum 2015-03-26
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2106064-2
    ISSN 1534-7362 ; 1534-7362
    ISSN (online) 1534-7362
    ISSN 1534-7362
    DOI 10.1167/15.3.19
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel ; Online: Premotor and Motor Cortices Encode Reward.

    Ramkumar, Pavan / Dekleva, Brian / Cooler, Sam / Miller, Lee / Kording, Konrad

    PloS one

    2016  Band 11, Heft 8, Seite(n) e0160851

    Abstract: Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and ... ...

    Abstract Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and above limb kinematics and dynamics. Here, we report a categorical reward signal in dorsal premotor (PMd) and primary motor (M1) neurons that corresponds to an increase in firing rates when a trial was not rewarded regardless of whether or not a reward was expected. We show that this signal is unrelated to error magnitude, reward prediction error, or other task confounds such as reward consumption, return reach plan, or kinematic differences across rewarded and unrewarded trials. The availability of reward information in motor cortex is crucial for theories of reward-based learning and motivational influences on actions.
    Mesh-Begriff(e) Action Potentials/physiology ; Animals ; Biomechanical Phenomena ; Electrodes ; Learning/physiology ; Linear Models ; Macaca mulatta ; Motivation ; Motor Cortex/physiology ; Movement/physiology ; Neurons/metabolism ; Neurons/physiology ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Reward
    Sprache Englisch
    Erscheinungsdatum 2016-08-26
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0160851
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: Premotor and Motor Cortices Encode Reward.

    Pavan Ramkumar / Brian Dekleva / Sam Cooler / Lee Miller / Konrad Kording

    PLoS ONE, Vol 11, Iss 8, p e

    2016  Band 0160851

    Abstract: Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and ... ...

    Abstract Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and above limb kinematics and dynamics. Here, we report a categorical reward signal in dorsal premotor (PMd) and primary motor (M1) neurons that corresponds to an increase in firing rates when a trial was not rewarded regardless of whether or not a reward was expected. We show that this signal is unrelated to error magnitude, reward prediction error, or other task confounds such as reward consumption, return reach plan, or kinematic differences across rewarded and unrewarded trials. The availability of reward information in motor cortex is crucial for theories of reward-based learning and motivational influences on actions.
    Schlagwörter Medicine ; R ; Science ; Q
    Sprache Englisch
    Erscheinungsdatum 2016-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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