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Posts
Communication, coordination and competition in causal problem solving
Published:
Once upon a time, I received an offer to pursue my PhD study at the school of Philosophy, Psychology & Language Science, University of Edinburgh. I wanted to study what are the differences/similarities between human and RL agents in cooperative problem-solving. But, situation did not go well, and I could not start the PhD program at Edinburgh.
TensorFlow Control Flow: tf.cond()
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I have published this post at Medium too. You can find Medium version here
Causal Inference and learning
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I have started to learn more about the topic of causal inference and causal learning. Therefore, I have decided to put together here every resource I am using during my journey towards understanding this topic. My interest in this topic originated from the philosophical debate about causality, and recently, I have become interested in the current trends and attempt in the machine learning community for reconciling causal graphs with deep learning.
Review: Deep Learning In Drug Discovery
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Deep learning algorithms have achieved a state of the art performance in a lot of different tasks. Convolutional Neural Network (CNN) can be used to achieve considerable performance in image classification, object detection, and semantic segmentation tasks. Recurrent Neural Networks (RNNs) and their descendants like LSTMs and GRUs are the first things that come to mind to tackle problems like neural language translation, speech recognition, and even they are used to generate new texts and music.
portfolio
Portfolio item number 1
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Portfolio item number 2
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publications
Optimum Design, Manufacturing and Experiment of a Passive Walking Biped: Effects of Structural Parameters on Efficiency, Stability and Robustness on Uneven Trains
Published in Applied Mechanics and Materials, 2013
In this research, a deployed model of biped that can be built has been considered, and then its walking performance sensitivity such as efficiency, stability and robustness on uneven trains due to variation of structural parameters and their optimum limits have been investigated.
Recommended citation: Hadi, Sadati et al. (2013). "Optimum Design, Manufacturing and Experiment of a Passive Walking Biped: Effects of Structural Parameters on Efficiency, Stability and Robustness on Uneven Trains." Applied Mechanics and Materials. https://www.scientific.net/AMM.307.107
Enhanced Waddington Landscape Model with Cell-Cell Communication Can Explain Molecular Mechanisms of Self-Organization
Published in Bioinformatics, 2019
Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation.
Recommended citation: Hosein, Fooladi. (2019). "Enhanced Waddington Landscape Model with Cell-Cell Communication Can Explain Molecular Mechanisms of Self-Organization." Bioinformatics 1. 1(3). https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz201/5418791?redirectedFrom=fulltext
Recognizing Arrow Of Time In The Short Stories
Published in ACL, 2019
Recognizing arrow of time in short stories is a challenging task. i.e., given only two paragraphs, determining which comes first and which comes next is a difficult task even for humans. In this paper, we have collected and curated a novel dataset for tackling this challenging task. We have shown that a pre-trained BERT architecture achieves reasonable accuracy on the task, and outperforms RNN-based architectures.
Recommended citation: Hosseini, Fahimeh, Hosein Fooladi, and Mohammad Reza Samsami. "Recognizing Arrow Of Time In The Short Stories." arXiv preprint arXiv:1903.10548 (2019). https://www.aclweb.org/anthology/W19-3606/
Bayesian optimization for ternary complex prediction (BOTCP)
Published in Artificial Intelligence in the Life Sciences, 2023
Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which labels the protein for degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase.
Recommended citation: Rao, Arjun, et al. "Bayesian optimization for ternary complex prediction (BOTCP)." Artificial Intelligence in the Life Sciences 3 (2023): 100072. https://www.sciencedirect.com/science/article/pii/S2667318523000168
Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network
Published in ChemRxiv, 2023
Machine learning models are employed to enhance the speed and provide novel insights in drug discovery due to their demonstrated effectiveness in predicting properties of small molecules like pKa, solubility, and binding affinity. These approaches accelerate drug discovery by helping researchers efficiently identify, prioritize, and optimize compounds.
Recommended citation: Mekni, Nedra, et al. "Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network" ChemRxiv (2023) https://chemrxiv.org/engage/chemrxiv/article-details/657c870b66c13817294342d9
Quantifying the hardness of bioactivity prediction tasks for transfer learning
Published in Journal of Chemical Information and Modeling, 2024
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks.
Recommended citation: Fooladi, Hosein, et al. "Quantifying the hardness of bioactivity prediction tasks for transfer learning" Journal of Chemical Information and Modeling (2024) https://chemrxiv.org/engage/chemrxiv/article-details/65b3cafd9138d23161cc5ea4
talks
System biology and waddington landscape
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In this talk, I presented our work on pattern formation during human embryonic stem cell development. We proposed a multicellular mathematical model for pattern formation during the in-vitro gastrulation of human ESCs. This model enhances the basic principles of the Waddington epigenetic landscape with cell-cell communication, which enables us to describe how the pattern and tissue formation occurs in the course of development.
Causal learning and inference
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Causal learning and discovery have received significant attention in the machine learning community. Causal learning allows us to answer the question about intervention (i.e., tinkering in the world) and imagination. This framework can mitigate the problem of robustness, adaptation, and explanation that is present in current machine learning practice.
teaching
Advanced bioinformatics
graduate course, Sharif University of Technology, Computer Engineering, 2017
I worked as the teaching assistant of “Advanced bioinformatics course”. This is a graduate level course which covers topics such as: Microaarray and RNA-Seq data analysis, sequence alignment and assembly from genomics data and so on.
Machine learning workshop
Workshop, Shenakht Pajouh, 2019
I am running a machine learning workshop, which I present diverse topics; from knowledge distillation and compression to causal inference and invariance.
Active learning in drug discovery
Workshop, Online, 2022
Active learning and Bayesian optimization have become important/prevalent tools in drug discovery and design. They provide a systematic way to select samples with a high amount of information. This property has made them an ideal choice in early-stage drug discovery, e.g., in hit identification through virtual screening and also lead optimization. Also, they can be used to search through usually very large drug combination space to find drug pairs with the highest synergy score. In this workshop, first, we read and review the theoretical aspects of active learning and bayesian optimization.