Evaluating the method reproducibility of deep learning models in the biodiversity domain

• URL: http://arxiv.org/abs/2407.07550v1
• Date: Wed, 10 Jul 2024 11:19:15 GMT
• Title: Evaluating the method reproducibility of deep learning models in the biodiversity domain
• Authors: Waqas Ahmed, Vamsi Krishna Kommineni, Birgitta König-Ries, Jitendra Gaikwad, Luiz Gadelha, Sheeba Samuel,
• Abstract summary: Ensuring in AI-driven biodiversity research is crucial for fostering transparency, verifying results, and promoting the credibility of ecological findings. We design a methodology for evaluating the biodiversity-related publications that employ deep learning (DL) techniques across three stages. Our study shows that the dataset is shared in 47% of the publications; however, a significant number of the publications lack comprehensive information on deep learning methods.
• Score: 0.5937476291232802
• License:
• Abstract: Artificial Intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conservation efforts. Ensuring reproducibility in AI-driven biodiversity research is crucial for fostering transparency, verifying results, and promoting the credibility of ecological findings.This study investigates the reproducibility of deep learning (DL) methods within the biodiversity domain. We design a methodology for evaluating the reproducibility of biodiversity-related publications that employ DL techniques across three stages. We define ten variables essential for method reproducibility, divided into four categories: resource requirements, methodological information, uncontrolled randomness, and statistical considerations. These categories subsequently serve as the basis for defining different levels of reproducibility. We manually extract the availability of these variables from a curated dataset comprising 61 publications identified using the keywords provided by biodiversity experts. Our study shows that the dataset is shared in 47% of the publications; however, a significant number of the publications lack comprehensive information on deep learning methods, including details regarding randomness.

Related papers

• Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications [0.0]
  Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains. critical information about how these models are designed, trained, and evaluated is challenging to access and comprehend. We use five different open-source Large Language Models (LLMs): Llama-3 70B, Llama-3.1 70B, Mixtral-8x22B-Instruct-v0.1, Mixtral 8x7B, and Gemma 2 9B in combination with Retrieval-Augmented Generation (RAG) approach.
  arXiv  Detail & Related papers  (2024-11-14T08:12:36Z)
• Causal Representation Learning from Multimodal Biological Observations [57.00712157758845]
  We aim to develop flexible identification conditions for multimodal data. We establish identifiability guarantees for each latent component, extending the subspace identification results from prior work. Our key theoretical ingredient is the structural sparsity of the causal connections among distinct modalities.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Domain adaptation in small-scale and heterogeneous biological datasets [0.0]
  We discuss the benefits and challenges of domain adaptation in biological research. We argue for the incorporation of domain adaptation techniques to the computational biologist's toolkit.
  arXiv  Detail & Related papers  (2024-05-29T16:01:15Z)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model. It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• Understanding the Impact of Training Set Size on Animal Re-identification [36.37275024049744]
  We show that species-specific characteristics, particularly intra-individual variance, have a notable effect on training data requirements. We demonstrate the benefits of both local feature and end-to-end learning-based approaches.
  arXiv  Detail & Related papers  (2024-05-24T23:15:52Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Towards Biologically Plausible and Private Gene Expression Data Generation [47.72947816788821]
  Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. We initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data.
  arXiv  Detail & Related papers  (2024-02-07T14:39:11Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset [18.211840156134784]
  This paper presents a curated million-image dataset, primarily to train computer-vision models capable of providing image-based taxonomic assessment. The dataset also presents compelling characteristics, the study of which would be of interest to the broader machine learning community.
  arXiv  Detail & Related papers  (2023-07-19T20:54:08Z)
• Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity [25.488181126364186]
  This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. We apply our method to grand biological challenges, such as data integration in single-cell genomics. Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest.
  arXiv  Detail & Related papers  (2023-07-02T12:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.