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protein-protein-interaction
gene-essentiality
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6d1bbc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | """MLPFeatures: Hand-crafted feature MLP for PPI binary prediction.
Uses interpretable features rather than raw sequences:
- AA composition (20-dim × 2 proteins)
- Sequence length × 2
- Network degree × 2
- Length ratio
- Subcellular location co-occurrence (one-hot)
Simple 3-layer MLP. Cheapest to train, most interpretable.
"""
from __future__ import annotations
import torch
import torch.nn as nn
# Standard 20 amino acids
_AA_LETTERS = "ACDEFGHIKLMNPQRSTVWY"
_AA_TO_IDX = {c: i for i, c in enumerate(_AA_LETTERS)}
# Known subcellular locations (top-10 + "other")
SUBCELLULAR_LOCATIONS = [
"Nucleus", "Cytoplasm", "Membrane", "Cell membrane",
"Mitochondrion", "Endoplasmic reticulum", "Golgi apparatus",
"Secreted", "Cell junction", "Cytoskeleton", "other",
]
_LOC_TO_IDX = {loc: i for i, loc in enumerate(SUBCELLULAR_LOCATIONS)}
N_LOCATIONS = len(SUBCELLULAR_LOCATIONS)
def compute_aa_composition(seq: str) -> list[float]:
"""Compute 20-dim amino acid frequency vector."""
if not seq:
return [0.0] * 20
counts = [0] * 20
total = 0
for c in seq:
idx = _AA_TO_IDX.get(c)
if idx is not None:
counts[idx] += 1
total += 1
if total == 0:
return [0.0] * 20
return [c / total for c in counts]
def encode_subcellular(loc: str | None) -> list[float]:
"""Encode subcellular location as one-hot vector."""
vec = [0.0] * N_LOCATIONS
if loc is None:
return vec
idx = _LOC_TO_IDX.get(loc, _LOC_TO_IDX["other"])
vec[idx] = 1.0
return vec
def extract_features(
seq1: str,
seq2: str,
degree1: float,
degree2: float,
loc1: str | None,
loc2: str | None,
) -> list[float]:
"""Extract feature vector for a protein pair.
Returns:
Feature vector of length 20+20+2+2+1+11+11 = 67.
"""
aa1 = compute_aa_composition(seq1) # 20
aa2 = compute_aa_composition(seq2) # 20
len1 = len(seq1) if seq1 else 0
len2 = len(seq2) if seq2 else 0
len_ratio = min(len1, len2) / max(len1, len2) if max(len1, len2) > 0 else 0.0
loc1_vec = encode_subcellular(loc1) # 11
loc2_vec = encode_subcellular(loc2) # 11
return (
aa1 + aa2 # 40
+ [len1 / 1000.0, len2 / 1000.0] # 2 (normalized)
+ [degree1 / 1000.0, degree2 / 1000.0] # 2 (normalized)
+ [len_ratio] # 1
+ loc1_vec + loc2_vec # 22
)
FEATURE_DIM = 67 # 20+20+2+2+1+11+11
class MLPFeatures(nn.Module):
"""MLP classifier using hand-crafted protein pair features.
Args:
input_dim: Feature vector dimensionality.
hidden_dims: Sizes of hidden layers.
dropout: Dropout rate.
"""
def __init__(
self,
input_dim: int = FEATURE_DIM,
hidden_dims: tuple[int, int, int] = (256, 128, 64),
dropout: float = 0.3,
) -> None:
super().__init__()
layers: list[nn.Module] = [nn.BatchNorm1d(input_dim)]
in_dim = input_dim
for out_dim in hidden_dims:
layers += [
nn.Linear(in_dim, out_dim),
nn.ReLU(),
nn.BatchNorm1d(out_dim),
nn.Dropout(dropout),
]
in_dim = out_dim
layers.append(nn.Linear(in_dim, 1))
self.net = nn.Sequential(*layers)
def forward(self, features: torch.Tensor) -> torch.Tensor:
"""
Args:
features: (B, FEATURE_DIM) float tensor.
Returns:
(B,) raw logits.
"""
return self.net(features).squeeze(-1)
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